AgriEngineering doi: 10.3390/agriengineering8090396

Authors: Qi Liu Haiyan Zhang Liang Yu Lei Liang Ding Ma Qiao Zhang Yubin Lan

To clarify the effects of rotor-induced downwash and crosswind on liquid sheet breakup and spray atomization characteristics of agricultural UAVs, an experimental platform integrating particle image velocimetry (PIV), a UAV spray system, and a wind tunnel was established. The droplet size and velocity characteristics of a flat-fan nozzle were investigated under different rotor speeds, crosswind conditions and spray pressures. The results showed that rotor-induced airflow significantly altered the post-breakup droplet characteristics. As the rotor speed increased from 0 to 2200 rpm, the volume median diameter (DV0.5) increased from 206.45 to 245.06 μm (18.7%), while the volume fraction of droplets smaller than 150 μm (V<150 (%vol)) decreased from 12.86% to 10.26%, indicating a shift toward coarser droplets under stronger downwash. Crosswind exhibited a limited influence on the primary breakup process but substantially modified droplet transport. Without rotor operation, increasing crosswind velocity from 0 to 6 m/s reduced the mean horizontal droplet velocity by 77.0%, promoting lateral droplet displacement. Under rotor operation at 2000 rpm, the downwash effectively enhanced spray plume stability and mitigated crosswind-induced distortion. Furthermore, increasing spray pressure from 0.10 to 0.50 MPa reduced DV0.5 from 274.29 to 222.24 μm and increased the proportion of fine droplets, demonstrating that spray pressure was the dominant factor controlling primary atomization. Overall, rotor-induced airflow primarily regulated droplet redistribution after atomization, whereas crosswind mainly affected droplet transport behavior. These findings provide theoretical guidance for optimizing UAV spray parameters and improving precision pesticide application efficiency.

AgriEngineering doi: 10.3390/agriengineering8090395

Authors: Ignacio Sánchez-Gendriz Victor N. Gomes Luiz Affonso Guedes

Acoustic sensing is a valuable approach for non-destructive fruit-quality evaluation. For coconut maturity determination, acoustic responses elicited by tapping may convey relevant information; however, recording-level analyses may not fully exploit individual tapping observations or the repeated measurements available for each fruit. This study developed a hierarchical framework for classification at the candidate tapping-event and fruit levels. A publicly available dataset comprising immature, mature, and overmature coconuts recorded at three ridge positions was analyzed. Candidate tapping-event segments were selected using a band-limited energy criterion and represented using conventional Mel-spectral representations or frozen embeddings extracted from a pretrained Contrastive Language–Audio Pretraining (CLAP) model. Within this representation framework, multiple conventional machine-learning and deep-learning configurations were evaluated. Fruit-disjoint partitioning prevented observations from the same coconut from occurring in different model-development and evaluation subsets. Event-level predictions were aggregated across tapping events and ridge positions by majority voting. The configuration combining CLAP embeddings with a regularized multilayer perceptron (MLP) achieved the highest balanced accuracies among the evaluated configurations, reaching 64.72% at the event level and 93.06% at the fruit level. With the downstream MLP held constant, CLAP exceeded the Mel-spectral controls by 8.89–10.18 percentage points at the event level and by 34.73–38.89 percentage points at the fruit level. For the CLAP–MLP configuration, fruit-level balanced accuracy was 28.34 percentage points higher than event-level balanced accuracy (93.06% versus 64.72%). These results suggest that general-purpose pretrained audio embeddings retain information relevant to coconut maturity discrimination and that combining repeated observations can substantially improve fruit-level decisions when event-level predictions are sufficiently informative.

AgriEngineering doi: 10.3390/agriengineering8090394

Authors: Túlio Souza Mariano Carlos Eduardo Alves Oliveira Ilda de Fátima Ferreira Tinôco João Vitor Ferreira da Silva Fernanda Campos de Sousa Luciano Barreto-Mendes Matteo Barbari

Heat stress poses a challenge to dairy production under increasing climate variability, highlighting the need for long-term assessments of thermal risk. This study assessed heat stress risk across major dairy-producing regions of Minas Gerais, Brazil, using ERA5-Land reanalysis data and the Temperature–Humidity Index (THI). Daily air temperature and dew point temperature data from 2004 to 2024 were used to calculate THI. ERA5-Land-derived THI was validated against observations from 63 INMET automatic weather stations using the modified Kling–Gupta Efficiency (KGE′). At the regional scale, heat stress risk was characterized based on the frequency, spatial distribution, seasonality, and temporal trends of THI-defined climatic heat-stress categories. Temporal trends were assessed using Pearson’s correlation between year and annual frequency for each THI category, with significance evaluated using a t-test at the 5% significance level. KGE′ values were predominantly between 0.80 and 0.90, indicating high agreement between observed and ERA5-Land-derived data. Heat stress conditions (THI ≥ 68) accounted for 69.72% of the observations, with the highest seasonal frequencies occurring in summer (24.23%) and spring (22.25%). Temporal analysis indicated an average increase of 2.23 days year−1 in heat-stress conditions, mainly due to moderate heat stress, while thermoneutral days showed predominantly decreasing trends. These findings demonstrate that integrating climate reanalysis data with bioclimatic indicators provides a robust framework for assessing heat stress risk and supporting regional planning of livestock facilities, cooling strategies, and climate change adaptation.

AgriEngineering doi: 10.3390/agriengineering8090393

Authors: Davlat Mukhammadiev Khamidulla Akhmedov Farkhod Ibragimov Lola Zhamolova Ortiq Abzoirov Baxrom Primov Ilhom Ergashev Orifjon Mallaev

This study develops an integrated electromechanical model of a seed-removing device used in a saw-type cotton gin. The modeled machine unit comprises a squirrel-cage induction motor, an elastic-dissipative belt transmission, a seed-removing tube rigidly connected to a ring gear, planet gears mounted on a fixed carrier, and an auger rigidly connected to the sun gear. The equations of motion were derived using Lagrange’s equations of the second kind. The induction motor was represented by the dynamic characteristic proposed by A.E. Levin, which was selected as a reduced-order model that captures the transient electromagnetic torque response during start-up without requiring the additional electrical parameters of a full direct–quadrature (d–q) axis model, while providing a more realistic transient representation than a static torque–speed characteristic. The moments of inertia of the rotating components were identified experimentally by the acceleration method, and the resulting nonlinear ordinary differential equations were solved by a fourth-order Runge-Kutta scheme. The model reproduces the start-up, transient, and steady-state stages and enables the evaluation of angular velocities, torques, angular accelerations, power demand, and rotational irregularity. Experimental validation was performed for the steady-state rotational speeds of the seed-removing tube and auger and for motor power, whereas the reported transient peak torque and angular acceleration were obtained from the numerical simulation. For the 3 kW, 735 rpm induction motor, the rated torque was 38.98 N·m, whereas the calculated peak starting torque reached 101.63 N·m, corresponding to a starting-torque ratio of 2.61. The transient process lasted approximately 3.5 s, and the maximum motor angular acceleration reached 2988.6 rad/s2 at t = 2.25 s. Within the investigated parameter ranges, the OFAT sensitivity analysis showed that the resistance moment of the seed-removing tube and the inertia of the auger exert the strongest influence on rotational irregularity, whereas the inertia and resistance of the planet gears have a comparatively weak effect. A reduction in the effective torsional stiffness of the belt drive from 17.2 to approximately 10.3 N·m/rad reduced the start-up rotational irregularity of the auger, evaluated over t = 2–4 s, from 0.435 to 0.420 and decreased motor power consumption from about 2.55 to 2.50 kW. The proposed model provides a system-level framework for selecting drive parameters and limiting torsional oscillations in planetary-driven cotton-processing machinery.

AgriEngineering doi: 10.3390/agriengineering8090392

Authors: Jian Tang Junyu Zhao Yun Deng Zubo Meng

Accurate estimation of winter wheat canopy nitrogen concentration (CNC) supports crop diagnosis and precision nitrogen management, yet the relative value and redundancy of multispectral, structural, and chlorophyll-related variables remain unclear in small dat asets. Using 155 multi-temporal observations retained from the 2016–2017 and 2017–2018 growing seasons, seven prespecified feature combinations were evaluated with four traditional regression models under five-fold repeated season-stratified cross-validation. A stricter season-balanced, unit-level grouped five-fold cross-validation was added to prevent observations from the same field from occurring in both training and test partitions. Two fully connected neural networks were additionally assessed for the selected compact module. Spectral-only combinations yielded negative mean R2 values, whereas LAI, vegetation cover, and chlorophyll content achieved a mean R2 of 0.684. Combining these variables with four raw multispectral bands produced M5, which achieved mean RMSE, R2, and RPD values of 0.328, 0.782, and 2.147, respectively, with 36.4% fewer variables than the full module. RF provided the best numerical performance under repeated sample-level cross-validation (R2 = 0.796 ± 0.007), whereas M5 retained R2 values of 0.736–0.778 under unit-grouped validation, with SVR performing best in that stricter setting. Parameter-removal analysis showed the largest incremental contribution for vegetation cover and limited additional value from LAI. Bidirectional cross-season validation remained direction- and model-dependent. Overall, controlled low-redundancy feature fusion was more beneficial than increased model complexity, while field-level and cross-season tests indicated that the strong within-dataset results should not be interpreted as broad generalization capability.

AgriEngineering doi: 10.3390/agriengineering8090391

Authors: Zikun Li Qun Chen Juan Lu Liwei Jin

Maintaining stable temperature and relative humidity (RH) is essential for edible fungi cultivation, yet indoor climates exhibit nonlinear, coupled thermal–moisture dynamics that can degrade disturbance rejection and increase energy use. This study proposes a reproducible lumped-parameter temperature–humidity model and evaluates a dual-loop (SISO × 2) fuzzy-PID regulation strategy in MATLAB/Simulink under standardized simulation scenarios. The model formulates energy and moisture conservation using physically interpretable parameters (air mass, ventilation exchange, heat transfer, and actuator limits), with RH obtained from a psychrometric transformation of humidity ratio. Three benchmark tests are designed for repeatable assessment: set-point tracking, step disturbances (±2 °C and ±5% RH), and periodic disturbances. A Simulated Growth Indicator (SGI) is introduced as a phenomenological model representing potential growth trends under controlled temperature and humidity, rather than actual measured crop yield. The fuzzy-PID strategy is compared with conventional PID and a no-control baseline using unified performance metrics (steady-state deviation, recovery/settling behavior, integral error) and actuator energy consumption computed from explicit power models. Results show that fuzzy-PID achieves faster recovery and lower error accumulation than PID under identical disturbances while reducing total energy consumption (e.g., 5.7 kWh vs. 6.7 kWh in a representative case). Although the plant is dynamically coupled, the controller implementation remains a practical dual-loop structure; the presented framework therefore serves as a reproducible simulation benchmark for controller comparison in high-humidity cultivation stages.

AgriEngineering doi: 10.3390/agriengineering8090390

Authors: Peng Zheng Xuan Li Zu-Hong Liu Ze-Zhang Liu Liu Yang Jie Cai Yan-Fang Liu Zhong-Bao Shao Zhe Yang Zhen-Yu Pu Bing Deng

AI-enabled smart pig farming has evolved from isolated sensing applications to integrated systems for health monitoring, precision feeding, welfare assessment, and environmental control. However, evidence remains fragmented across computer vision, sensor engineering, nutritional management, and thermal monitoring, obscuring mature and commercially viable technological pathways. This review evaluates deployment-relevant AI technologies for commercial pig production through structured evidence mapping and critical thematic synthesis. We analyzed 707 publications from the Web of Science Core Collection (WoSCC; 1991–2025) and evaluated candidate themes based on publication activity, citation patterns, temporal persistence, and thematic convergence. Three technical streams were examined in depth: vision-based pig detection, precision feeding, and AI-assisted infrared body-temperature monitoring. Across these areas, research has progressed from proof-of-concept algorithms to integrated sensing-to-decision systems. Vision-based detection is advancing toward robust, lightweight models; precision feeding toward individualized closed-loop control; and thermal monitoring toward automated region-of-interest (ROI) localization and AI-assisted temperature interpretation. Major gaps remain in dataset representativeness, cross-farm generalizability, methodological consistency, field-scale validation, system reliability, economic feasibility, thermal calibration, surface-to-core temperature inference, ROI localization, and false-alarm control. This review is limited by its reliance on a single bibliographic database, predefined search terms, and potential publication and citation biases. Future progress requires cross-site validation, multimodal sensing, interpretable decision models, cost-effective deployment, adaptive thermal calibration, and reliable alert strategies. Overall, AI-enabled smart pig farming is not only an algorithmic challenge but also a systems-integration task that must translate sensing and prediction into actionable farm management.

AgriEngineering doi: 10.3390/agriengineering8090389

Authors: Agustín Sancén-Plaza José Eleazar Peralta-López José Alfredo Padilla-Medina Gerardo Acosta-García Luz del Carmen García-Rodríguez Juan Prado-Olivarez Alejandro Espinosa-Calderón

Water stress is a critical limiting factor in horticultural crop productivity. This study presents a non-invasive approach for detecting water stress in jalapeño pepper (Capsicum annuum L. var. Huichol) through visible–near-infrared (VIS-NIR) imaging-based quantification of the foliar vascular network under controlled conditions. Nine plants were subjected to three irrigation regimes (100%, 50%, and 0% of daily water loss) and monitored over 13 days, acquiring VIS-NIR and LWIR images under light and darkness conditions. Using a digital image processing pipeline, foliar vascular networks were segmented from VIS-NIR images, and the vascular proportion index (Rv) was computed. A factorial ANOVA revealed significant main effects for irrigation treatment (F = 11.01, p < 0.001), illumination (F = 13.01, p < 0.001), and spectral modality (F = 422.77, p < 0.001), with the spectral band accounting for the highest variance (ηp2 = 0.079). The VIS-NIR + Light configuration produced the clearest treatment discrimination, with significant pairwise differences between 50% WL and both 100% WL (MD = 0.025, p < 0.0001) and 0% WL (MD = 0.020, p = 0.0001). This VIS-NIR-based technique offers a practical, scalable alternative for real-time monitoring of plant water status in horticultural systems.

AgriEngineering doi: 10.3390/agriengineering8090388

Authors: Theodore A. Tsiligiridis Sergio Godinho Katerina Ainali Rui Machado

This study utilizes Sentinel imaging to monitor small farming plots (<5 ha) and objectively estimate their significance in agricultural output. It develops, tests, and evaluates methodologies within and across ten southern European NUTS-3 regions—commonly referred to as prefectures, provinces, or departments—located in Greece, Italy, France, Portugal, and Spain. The study provides stakeholders with the necessary tools to estimate their spatial distribution, crop diversity, crop area extent, and agricultural yields. The approach is expanded as needed to similar landscapes, serving as a model for other European NUTS-3 regions and providing a comprehensive view of the remote sensing solution. It employs random forest crop identification and classification analysis to build crop-type maps and record the countless small agricultural plots. The Sentinel data utilized are from 20 NUTS-3 pilot regions selected across 11 EU countries, focusing on 29 estimated key crop areas. The research demonstrates remarkable classification accuracy for a previously identified wide range of key crop data across each of the selected NUTS-3 regions. Crop area estimates derived from field data were adjusted and stratified. The RS-based methodology reduces propagation errors in estimating the area and production of crop categories with low accuracy levels, enhancing the overall understanding of the crucial role of small farming plots. For highly accurate key crop products (per crop-type categorization, with FScore > 75%), production estimates are calculated by multiplying predicted self-reported crop yields by unbiased key crop small farming plot area estimates. However, for the referenced period of cultivation, only 16 out of the above 29 estimated key crop areas are recorded for SFs by the regional official statistics. In addition, of the 16 reported key crop areas listed above, eleven are from nine NUTS-3 regions spread across four southern EU countries, with the remaining five coming from four NUTS-3 regions of two non-southern EU countries. The analysis shows a strong correlation between estimated key crop areas from SFs reported in regional official statistics and Sentinel-based key crop area estimates obtained from small farming plots, with R2=0.96 for 16 estimated key crop area datasets across all NUTS-3 regions in the EU and R2=0.98 for 11 estimated key crop area datasets across all southern NUTS-3 regions. As a result, in NUTS-3 regions lacking regional official statistics data, RS can serve as a reliable alternative source for estimating the extent of key crop areas.

AgriEngineering doi: 10.3390/agriengineering8090387

Authors: Jéssica Caroline de Lima Eduardo Luiz Buligon Valkerson Zacarkim Juliane Almeida Battisti Monica Sarolli Silva de Mendonça Costa

The intensification of beef cattle feedlot systems generates large volumes of manure, whose inadequate management can lead to negative environmental impacts and nutrient losses. Anaerobic digestion is an effective strategy for mitigating greenhouse gas emissions while producing renewable energy and nutrient-rich by-products. However, the high solids content of beef cattle manure may limit process performance and operational stability. This study evaluated an integrated management approach combining solid–liquid separation and digestate recycling during semi-continuous anaerobic digestion of beef cattle manure, followed by the agronomic use of the resulting by-products in wheat cultivation. Four treatments were evaluated in 60 L horizontal tubular digesters operated under mesophilic conditions and a hydraulic retention time of 30 days: solid–liquid separation followed by water dilution; solid–liquid separation combined with 100% digestate recycling; raw manure diluted with water; and raw manure diluted with water and digestate, with 60% recycling. Biogas and methane production were monitored, and the digestate and separated solid fractions were chemically characterised. A greenhouse experiment was subsequently conducted to compare wheat growth and grain yield under mineral, digestate-based, and organomineral fertilisation strategies. Solid–liquid separation combined with full digestate recycling produced the highest specific methane yields, whereas treatments without separation showed higher volumetric methane production due to their higher organic loading rates. Digestate-based fertilisation resulted in wheat grain yields comparable to or higher than those obtained with mineral fertilisation. These findings suggest that integrating solid–liquid separation and digestate recycling has potential to reduce freshwater demand, promote nutrient recovery, and support the agronomic reuse of anaerobic digestion by-products.

AgriEngineering doi: 10.3390/agriengineering8090386

Authors: Alexander V. Klokov Natalia A. Semenova Anna S. Tatarinova Suprava Chakraborty Egor Yu. Loktionov

Agrivoltaics (AV) is a concept that involves dual land use for both growing crops (or grazing) and installing solar panels for electricity production. AV should be assessed not only as a dual land use strategy, but also as a climate adaptation and resilience solution for agriculture; solar panels can shield crops and livestock from extreme weather events (EWEs). This PRISMA review synthesises evidence from Scopus- and PubMed-indexed studies (up to 2025) on AV and EWEs: 1152 (Scopus) and 1208 (PubMed) records were identified, 93 (Scopus) and 160 (PubMed) full texts were assessed, and 27 studies were included in the final review. Published data indicate that AV can reduce frost damage risk by more than 40% (simulation-based evidence), prevent crop leaf sunburn (experiment-based evidence) and hail damage (experiment-based evidence), alleviate heat (experiment-based evidence) and water stress (experiment-based evidence), and act as windbreaks (experiment-based evidence). The majority of reviewed studies were conducted in temperate climates (54%), limiting the generalisability of findings to more extreme climate types where EWE impacts may be most pronounced. The EWE effect on agriculture is complex: crop losses, interruptions of energy, fuels, fertilisers, and other supplies, which could lead to the inability to complete the agricultural work on time and cause economic losses. AV, like other distributed PV systems, has the potential to enhance the energy resilience of farms, though direct evidence of this benefit from AV-specific studies is limited. However, resilience to EWEs is explicitly addressed in less than 3% of Scopus-indexed AV studies. This review presents current evidence, key design trade-offs, and research gaps in the field of AV aiming to reduce EWE consequences.

AgriEngineering doi: 10.3390/agriengineering8090385

Authors: Lu Zhu Lin He Kaihua Liu Xiaodong Guan Shi Xiong Yong Gao Wei Liu Minglin Chen

To address the inefficiencies of two-stage sweet potato harvesting in southern China, an integrated machine for synchronous haulm shredding and tuber excavation was developed. The equipment features a front-mounted, reverse-rotating crushing knife roller and a rear-mounted, adjustable grate-type digging shovel. The performance of the prototype was systematically evaluated through two-stage field trials in clay loam soil. First, an orthogonal test was employed to assess the haulm shredding quality. The results indicated that the knife roller speed significantly increased the qualified rate of crushed stems and leaves, whereas the forward speed exerted a negative effect. Additionally, the blade-to-ridge clearance primarily dictated the ridge-top stubble length. Second, a quadratic orthogonal rotational composite design was utilized to optimize the integrated harvesting parameters. The analysis demonstrated that shovel inclination significantly enhanced the tuber exposure rate, while both clearance and inclination exhibited quadratic nonlinear effects on the tuber damage rate. Multi-objective optimization established the optimal operational parameters as a blade-to-ridge clearance of 66.6 mm and a shovel inclination of 34.0°. Field validations under these settings achieved a tuber exposure rate of 83.7% and a damage rate of 4.3%, confirming the high reliability of the predictive models. The integrated equipment effectively shortens the harvesting cycle and demonstrates robust adaptability to clayey moist soils, thereby advancing the mechanization of sweet potato production.

AgriEngineering doi: 10.3390/agriengineering8090384

Authors: Chengpeng Li Yanru Bi Gang Wang Min Zhang

High-moisture rice straw processed by whole-feed combine harvesters often exhibits high cutting resistance and uneven particle size distribution after comminution. To address these issues, a straw comminution device integrated with the straw outlet of the threshing and cleaning system was developed, and the shear fracture mechanism and operating parameters were investigated. The geometric characteristics, density, contact properties, and bending properties of rice straw cultivars Yongyou 7301 and Kenuigeng 1 were measured. A hollow flexible straw discrete element model was established using the Hertz–Mindlin with Bonding contact model, and its parameters were calibrated and validated through quasi-static shear cutting tests. The effects of shear cutting angle on maximum cutting force, bond failure evolution, and load transfer behavior were analyzed at shear angles of 30°, 45°, and 60°. Device-scale DEM simulations combined with field experiments were further conducted to optimize the guide plate angle and rotor speed. The results showed that the maximum cutting force under quasi-static single-stalk cutting conditions initially decreased and then increased with increasing shear angle. At a shear angle of 45°, the maximum cutting force was 78 N, representing a 44.8% reduction compared with that at 30°. Meanwhile, the fracture zone expanded along the blade sliding direction and stress concentration was alleviated. The DEM model effectively characterized the fracture behavior of rice straw, with an average relative error of 11.07% between simulated and experimental cutting forces. The optimized operating parameters under the tested conditions were a shear angle of 45°, guide plate angle of 55°, and rotor speed of 2500 r/min, resulting in average chopped lengths of 17.3 mm in simulation and 20.5 mm in field experiments, with a comminution qualification rate of 95.26%. These findings provide theoretical support for improving the fracture characteristics and chopping performance of straw comminution systems.

AgriEngineering doi: 10.3390/agriengineering8090383

Authors: Guoqing Zhang Shuping Zhang Lili Tao Yunlong Zhang Jingbo Zhao Haimei Liu

Reliable prediction and interpretation of photosynthetic rate are important for precision environmental management in greenhouse tomato production, but model stability is often limited by variable redundancy, measurement-session effects, and environmental heterogeneity. This study developed an integrated gas-exchange-data-based framework for key-factor selection, photosynthetic-rate reconstruction, cross-session validation, environmental correction, and physiology-informed limitation screening. Core predictors were first identified from high-dimensional gas-exchange variables using K-means clustering and random-forest importance analysis. A CatBoost–ExtraTrees–RBF-SVR stacked ensemble was then constructed to reconstruct the leaf photosynthetic rate from selected gas-exchange variables, and its cross-session generalization was evaluated using nested cross-validation and leave-one-file-out (LOFO) extrapolation. Environmental correction was further applied to improve cross-session comparability, and rule-based limitation screening was used to classify potential environmental constraints associated with reduced photosynthesis. The stacked model achieved an RMSE of 0.806 and an R2 of 0.9918 under nested cross-validation, and an RMSE of 0.814 and an R2 of 0.9917 under LOFO extrapolation. After environmental correction, the cross-session variability of the environmentally corrected photosynthetic indicator was reduced by 96.0%, reflecting improved cross-session comparability. Under deployable sensor inputs, performance decreased markedly (A1: RMSE = 4.578, R2 = 0.735; A2: RMSE = 4.610, R2 = 0.731), compared with the gas-exchange baseline (RMSE = 0.833, R2 = 0.991). Thus, routine greenhouse sensor models should be regarded as approximate rather than high-accuracy substitutes for the gas-exchange-based model.

AgriEngineering doi: 10.3390/agriengineering8090382

Authors: Emerson Flávio dos Santos Brenno Santos Leite Lessando Moreira Gontijo

Brassica crops, including collard greens, are economically important vegetable crops cultivated worldwide. Among the major pests affecting these crops, the aphids Brevicoryne brassicae (Linnaeus) and Myzus persicae (Sulzer) (Hemiptera: Aphididae) are particularly relevant due to the direct and indirect damage they cause. Increasing consumer demand for sustainable agricultural practices and pesticide-free produce has intensified the search for environmentally friendly pest management alternatives. In this context, plant-derived insecticides have emerged as promising sources of bioactive compounds because of their biodegradability, selectivity, and generally low toxicity to non-target organisms and mammals. Likewise, agro-industrial residues such as avocado seeds have attracted attention as sustainable raw materials with potential bioactive and insecticidal properties. In the present study, biopolyols produced from avocado and neem seeds through a liquefaction process based on green chemistry and waste valorization principles were evaluated for their insecticidal activity against B. brassicae and M. persicae. A series of laboratory and field bioassays were conducted to assess toxicity and repellency effects on aphids. Among the tested formulations, the AAA avocado seed biopolyol (2:1/30 min) showed the highest insecticidal activity, possibly due to better preservation of bioactive compounds under the liquefaction conditions tested. Dose–response and lethal-dose (LD) analyses estimated an LD50 of 64 mg/mL and an LD90 of 429 mg/mL (p < 0.001) for the AAA biopolyol. In addition to its lethal effects, the LD90 concentration of the AAA biopolyol exhibited significant repellency against aphids and outperformed Azamax®applied at the recommended field rate. Field bioassays further demonstrated that both the AAA biopolyol at LD90 and Azamax® significantly reduced aphid abundance in collard greens of the ‘Manteiga’ cultivar under field conditions, compared to the control. Nonetheless, there was no difference in the efficacy of the AAA biopolyol and Azamax® in the field study. Altogether, these findings demonstrate the potential of seed-derived biopolyols as sustainable bioinsecticides for aphid management in Brassica crops, while also highlighting the value of agro-industrial waste as a source of novel bioactive compounds for integrated pest management programs.

AgriEngineering doi: 10.3390/agriengineering8090381

Authors: Hasna Hajjaj Kacem Makroum Assia Harkani Hanane Ouhemi Mounia Sibaoueih Khalid Ibno Namr Abdellah El Aissaoui

Cereal–legume forage mixtures provide sustainable agronomic and environmental benefits for arid Mediterranean agricultural systems, but their productivity is often limited by within-field soil spatial variability. Site specific management practices are required to address the yield gaps. This study aimed to identify management zones in a no-till Triticale–Avena–Pea mixture by integrating apparent electrical conductivity (ECa) and NDVI measurements. ECa was mapped using an EM38-MK2 sensor, while NDVI was derived from UAV and Sentinel-2 imagery at four key growth stages. Plant biomass data and soil samples were collected for evaluating and explaining the yield gaps. Using K-means clustering, three management zones were delineated using ECa and NDVI data. Results showed a yield gap of 46% in fresh biomass between high- and low-productivity zones, associated with strong gradients in soil organic matter and total nitrogen availability. The delineation of management zones enabled targeted agronomic interventions, such as adjusting seeding rates in the high-productivity area and prioritizing soil improvement strategies in the low-productivity zone, thereby enhancing input-use efficiency.

AgriEngineering doi: 10.3390/agriengineering8090380

Authors: Sazzad Mahmud Rifat Jianfeng Zhou Andrew L. Thomas

American elderberry (Sambucus nigra subsp. canadensis) is an emerging specialty crop with increasing demand for value-added products; however, postharvest cleaning, separation, and weighing remain highly labor-intensive. This study developed and preliminarily evaluated a prototype of a water-assisted cleaning and weighing system designed to assist post-destemming handling during preliminary prototype testing. The prototype consisted of a stainless-steel, water-based separation tank; a sieve-type funnel guide; a screw conveyor; a motor and coupling assembly; an electronic control unit; a touchscreen interface; and a digital weighing scale. Manually destemmed elderberries were introduced into the water-filled tank, where mature berries settled, and the floating debris was removed. The submerged berries were then transferred by screw conveyor to the weighing unit. Conveyor speed was evaluated from 300 to 650 revolutions per minute (rpm) with three replications per treatment. Machine throughput increased significantly with conveyor speed, from 40.50 ± 27.09 kg h−1 at 300 rpm to 368.18 ± 19.44 kg h−1 at 650 rpm. Visible external berry damage after processing decreased from 50.00 ± 2.50% at 300 rpm to 18.50 ± 2.20% at 650 rpm. However, because pre-processing damage, berry loss, quantitative cleaning efficiency, weighing accuracy, and labor savings were not measured, these results should be interpreted as a preliminary prototype evaluation rather than a complete commercial validation. Within the tested speed range, 650 rpm was the best tested endpoint, producing the highest measured throughput and the lowest visible post-process damage. Further work is needed to quantify cleaning efficiency, recovery yield, berry loss, sanitation performance, weighing-control accuracy, and comparison with conventional manual processing.

AgriEngineering doi: 10.3390/agriengineering8090378

Authors: Kushal P. Dhake Sanjay Kumar Jain Pankaj B. Pathare Sandip D. Patil

The present study investigates the effect of drying temperature and potassium metabisulfite (KMS) pretreatment on the drying kinetics, effective moisture diffusivity, activation energy, thermodynamic properties, and thin-layer drying-model performance of green banana slices. Banana slices (4 ± 0.5 mm thickness) were subjected to KMS pretreatments at concentrations of 0 (control), 0.5%, 1.0%, and 1.5%, followed by drying in a convective tray dryer at 40, 50, 60, and 70 °C. The drying process exhibited a predominantly falling-rate period. Effective moisture diffusivity ranged from 2.04 × 10−10 to 4.35 × 10−10 m2/s and increased with drying temperature. Apparent activation energy decreased from 16.19 kJ/mol for the untreated control to 13.46 kJ/mol at 1.5% KMS. Thermodynamic analysis gave enthalpy values of 10.61–13.59 kJ/mol, entropy values of −209.15 to −219.72 J/mol·K, and Gibbs free energy values of 76.35–87.73 kJ/mol. The positive ΔG values indicate that moisture removal was non-spontaneous under the investigated conditions and required external thermal-energy input. Ten thin-layer models were evaluated using R2, RMSE, and χ2; the Midilli–Kucuk model gave the best overall fit within the investigated temperature and KMS ranges. Because endpoint moisture contents differed among treatments, drying-time comparisons were additionally standardised to an interpolated moisture content of 10% d.b.

AgriEngineering doi: 10.3390/agriengineering8090379

Authors: Mariorosario Prist Paolo Cicconi Michele Trovato Andrea Monteriù Alessandro Freddi Andrea Bonci

Circular economy is today a key driver of every transformation process aimed at reducing and optimizing the use of energy and materials. The production of solid biofuel from waste is a typical route to lower the potential impact of greenhouse-gas emissions. In this context, olive pomace is a relevant feedstock, as 4 million tonnes are generated worldwide each year alongside olive oil production. However, only a small fraction of olive pomace is currently valorized. Fresh olive pomace must first be quickly dried to a low, controlled moisture. This step is performed poorly and at a high energy cost. This paper presents an automation-based approach to enhance biomass production from olive pomace, thereby advancing circular-economy practices in olive oil production. The work is focused on four aspects. In the first part, a review of the state of automation in agricultural engineering with a focus on biomass and olive pomace is proposed. Then, the design and construction of an innovative drying system that integrates an infrared solution directly into the transporting screw conveyor is described, integrating real-time online microwave moisture sensing and PLC control. After that, a cloud-based service is presented for remote monitoring, data analysis, and optimization. The innovative and automated drying system was validated during a preliminary field campaign at an olive mill. After about sixteen hours of continuous, cloud-monitored operation, the resulting olive pomace moisture fell below the 5% threshold across a wide range of inlet-moisture conditions, with a stable electrical power demand of approximately 1.85 kW. Finally, an environmental analysis is provided to evaluate the environmental aspects related to the proposed system. The preliminary analysis confirms a significant avoided-carbon potential if the resulting olive pomace is reused as biomass for energy production. The impact associated with 1 kWh-eq produced from olive pomace is in the range of 0.006–0.033 kg CO2-eq.

AgriEngineering doi: 10.3390/agriengineering8090377

Authors: Mohamed El Bakkari Nabila Rabbah Mourad Bouneffa Nicolas Waldhoff Abdelwahed Touati

Soil health is central to sustainable agriculture, but remains challenging to assess in diversified agroecosystems such as permaculture. Soil condition reflects the interaction of physical, chemical, and biological properties, but practical assessment commonly relies on a limited set of informative indicators. In this study, a PCA-weighted Soil Health Index (SHI) was constructed from five surface soil indicators: organic carbon, total nitrogen, microbial biomass (PLFA), bulk density, and gravimetric water content. The first principal component explained 75.30% of the total variance. The analysis used 84 observations collected between 2019 and 2021 from permaculture and conventional farming systems across nine locations in Germany and Luxembourg, encompassing different land use types and two soil depths. Permaculture plots showed higher SHI values overall than conventional plots, with the same trend observed across all nine locations, although land use imbalance limited fully matched comparisons. To avoid circular prediction of the PCA-derived target, the five surface variables used directly to construct the SHI were excluded from the predictive feature set. Machine learning models were evaluated using grouped validation in which entire locations were held out from model training. The best-performing full-profile Ridge model achieved an out-of-fold R2 of 0.710, an MAE of 0.159, and an RMSE of 0.214. Out-of-fold SHAP analysis indicated that magnesium, zinc, soil pH, subsoil bulk density, and copper made the largest model-specific contributions to SHI estimation. These findings demonstrate that PCA-based soil health assessment can distinguish systematic differences between studied farming systems and that a leakage-aware, interpretable modeling framework can provide moderate predictive performance across held-out locations. The results should be interpreted as internal evidence from a small multi-location dataset rather than as externally validated or causal estimates of management effects.

AgriEngineering doi: 10.3390/agriengineering8090376

Authors: Yurii Syromiatnykov Farmon Mamatov Sanjar Toshtemirov Sherzod Kurbanov Uchkun Kodirov Dilsabo Choriyeva Golib Shodmonov Moxichexra Begimkulova Shakhriyor Jalilov Azamat Safarov Muso Xidirov Gayrat Otamurodov Shahnoza Abduganiyeva

Intelligent seedbed preparation requires more than an optimized soil-engaging component: field condition must be diagnosed, a controllable setting must be adjusted, and the resulting soil zone must be verified. This critical review synthesizes 132 unique sources across soil mechanics, component design, numerical modeling, field validation, and sensing-control research for water-limited cropping systems. Evidence was compared as bounded within-study contrasts rather than pooled effects because outcome definitions, soils, operating regimes, and validation scales differ. Four recurring conflicts organize the synthesis: fracture versus draft, tilth versus evaporative exposure, residue retention versus blockage, and immediate loosening versus persistence. Reported examples include an increase in the <50 mm aggregate fraction from 76.1% to 88.0%, a study-specific fragmentation index increase from 83.0% to 94.54%, a 43–47% reduction in penetration resistance after geometry-optimized loosening, and rainfall-dependent water-use-efficiency gains of 6.0–11.7% after subsoiling. These values are retained as study-specific anchors, not universal settings. The proposed framework adds value by linking four decision stages—field diagnosis, component design, controlled operation, and post-pass verification—while explicitly separating conventional optimization, monitored operation, and closed-loop intelligent control.

AgriEngineering doi: 10.3390/agriengineering8090375

Authors: Christopher Tunji Oloyede Simeon Olatayo Jekayinfa Toyin Peter Abegunrin Opeyemi Olusegun Opadotun Yusuf Yinka Oyekunle Christopher Chintua Enweremadu

The efficient utilization of underexploited biomass resources for renewable energy production requires reliable engineering data on their mechanical behaviour during processing. However, information on the compressive mechanical properties of Canarium schweinfurthii seeds, a promising oil-bearing biomass resource, remains scarce. This study investigated the effects of moisture content (3.89–25.65%, db) and loading orientation (horizontal and vertical) on the compressive mechanical properties of the seeds using a universal testing machine. Measured parameters included rupture force, rupture energy, bio-yield force, bio-yield energy, deformation at rupture point, and hardness. Moisture content significantly influenced the measured mechanical properties under both horizontal and vertical loading orientations (p < 0.05). Rupture force decreased from 1947.67 to 604.47 N under horizontal loading and 1584.27 to 465.12 N under vertical loading. Rupture energy decreased from 0.801 to 0.193 Nm and 3.258 to 0.390 Nm, respectively. Bio-yield force decreased from 1945.54 to 703.26 N under horizontal loading but increased from 521.85 to 811.45 N under vertical loading. Bio-yield energy, deformation at rupture point, and hardness decreased from 0.805 to 0.234 Nm, 0.804 to 0.461 mm, and 2422.94 to 1301.45 N/mm under horizontal loading, and from 0.506 to 0.121 Nm, 3.639 to 1.372 mm, and 430.51 to 337.05 N/mm under vertical loading, respectively. Regression models showed strong predictive performance (R2 = 0.890–0.967), demonstrating reliable prediction of the seed behaviour. These findings provide engineering data for designing and optimizing shellers, crushers, grinders, pelletizers, and other biomass processing equipment for the sustainable utilization of Canarium schweinfurthii as a renewable bioenergy feedstock.

AgriEngineering doi: 10.3390/agriengineering8090374

Authors: Ponthep Vengsungnle Jarinee Jongpluempiti Adun Janyalertadun Paisarn Naphon

Forest microclimate strongly influences medicinal plant growth and habitat suitability; however, year-round characterization of forest microclimate for medicinal plant cultivation remains limited. This study presents a year-long, multi-station microclimate dataset and evaluates its potential for preliminary suitability assessment. Three monitoring stations (Pt1–Pt3) were deployed within the Plant Genetic Conservation Project under the Royal Initiative (RSPG), Thailand. Air temperature and relative humidity were continuously monitored at 30-min intervals from January to December 2025 using an Internet of Things (IoT)-based system. We used descriptive statistics, coefficients of variation (CV), repeated-measures analyses, and adjusted pairwise comparisons to evaluate year-round, seasonal, and site-specific microclimatic variabilities. Significant spatial differences were observed among the monitoring stations (p < 0.05). Pt3 exhibited the lowest annual mean temperature (26.05 °C), the highest relative humidity (80.60%), and the lowest environmental variability (CV = 16.41%), whereas Pt2 showed the highest temperature (30.72 °C), the lowest humidity (60.83%), and greater variability. A relative microclimatic comparison showed that Pt3 was cooler, more humid, and more stable; Pt1 exhibited intermediate conditions; and Pt2 was warmer, drier, and more variable. These findings characterize relative temperature–humidity conditions among the monitored sites and provide baseline information relevant to future species-specific assessment of medicinal plant cultivation.

AgriEngineering doi: 10.3390/agriengineering8090373

Authors: Mahrokh Shafiei István Waltner Zoltán Vekerdy Gábor Ernő Halupka

Accurate mapping of Soil moisture (SM) is essential for effectively monitoring agricultural drought. However, the coarse spatial resolution of passive microwave products, including the 9 km Soil Moisture Active Passive (SMAP) retrievals, limits their effectiveness at regional and local scales. To address this limitation, three machine learning-based downscaling frameworks were compared to improve SMAP SM resolution from 9 km to 1 km over Békés County, Hungary. The study period covered the growing seasons (April to October) from 2020 to 2023. A set of multi-temporal MODIS-derived variables, including vegetation indices (NDVI, EVI), daytime and night-time land surface temperature, and evapotranspiration, along with land cover classification and topographic elevation, were combined as auxiliary predictor variables. Three machine learning algorithms, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Machine (GBM), were trained and evaluated. The results showed that (1) the RF model had the highest accuracy during the testing (R2 = 0.71, RMSE = 0.0295 m3/m3) phase and validation against four in situ monitoring stations with confirmed reliable SM estimation at the local scale; (2) daytime LST was the most important predictor in all models, underscoring the strong thermal–moisture coupling that governs surface SM dynamics; and (3) the validated RF model produced 1 km Standardized Soil Moisture Index (SSI) maps that effectively captured inter-annual drought variability, identifying the severe drought of July 2022. Overall, this study presents a downscaling approach for generating high-resolution SM data suitable for Central European agricultural environments. The resulting 1 km SM and SSI products provide valuable tools for decision-makers to enhance planning during drought periods and reduce agricultural losses through improved irrigation scheduling.

AgriEngineering doi: 10.3390/agriengineering8090372

Authors: Allan Lincoln Rodrigues Siriani Danilo Florentino Pereira Juliana de Souza Granja Barros Daniella Jorge de Moura

Automated monitoring of welfare-relevant behavior in laying hens (Gallus gallus domesticus) has advanced with developments in computer vision, deep learning, and precision livestock farming (PLF). This narrative review integrates the ethological basis of welfare indicators with the development and readiness of monitoring technologies. It distinguishes routinely expressed diagnostic behaviors, including preening, locomotion, dustbathing, feeding, drinking, and nesting, from high-priority welfare risks such as aggression, piling, feather pecking, and inactivity or prostration. The review traces the progression from manual ethograms to semi-automated tools and recent artificial intelligence (AI) applications. These include You Only Look Once (YOLO)-based detection, multi-object tracking with BoT-SORT (a robust association-based tracking algorithm), pose estimation, and multimodal sensor fusion. Application-specific Technology Readiness Levels (TRL 1–9) indicate that most behavior-analysis systems remain at TRL 4–6. Several technologies extend into TRL 6–8, whereas few established systems reach TRL 9. Persistent barriers include domain shift under production conditions, annotation costs, inconsistent validation protocols, and limited economic accessibility. By linking welfare relevance, evaluation level, and deployment evidence, this review identifies priorities for scalable and actionable monitoring in commercial laying-hen production.

AgriEngineering doi: 10.3390/agriengineering8090371

Authors: Jingkun Zhang Yongheng Wang Taiyi Yuan Yibo Sun Qingtao Zhang

Capillary wick irrigation offers a passive means of gradual water delivery, but field evidence comparing wick configurations with conventional drip irrigation remains limited. This study compared annular capillary wick irrigation (AI), straight capillary wick irrigation (SI), drip irrigation (DI), and a non-irrigated rainfall-only control (NI) in a Red Globe grapevine field experiment in Ningxia, northwest China. Each irrigated experimental unit consisted of one reservoir supplying three grapevines and received four 90 L irrigation events, corresponding to a seasonal applied irrigation amount of 360 L per experimental unit. The local soil moisture at a monitored 10 cm depth, vegetative growth, berry development, yield, fruit quality traits, physiological indicators, and irrigation water productivity were evaluated. The capillary wick treatments generally showed a more persistent local soil moisture response than the more rapid post-irrigation response under DI. AI had the highest-recorded treatment mean yield (11.97 kg experimental unit−1), irrigation water productivity among the irrigated treatments (0.033 kg L−1), final 100-berry weight (864.49 g), and soluble solids content (18.77%). A 13-indicator AHP–Entropy Weight evaluation ranked AI first (0.8765), followed by SI (0.5129), DI (0.4860), and NI (0.3502); the same treatment order was retained in a sensitivity analysis using mean SPAD readings. Because the archived dataset did not permit consistent reconstruction of experimental unit identity for all response variables, the treatment differences are interpreted descriptively rather than as inferential statistical effects. These results provide preliminary field evidence that annular capillary wick irrigation warrants further engineering evaluation as a delivery configuration, while direct measurements of hydraulic discharge, spatial wetting patterns, and long-term operational reliability are required before its broader application.

AgriEngineering doi: 10.3390/agriengineering8090370

Authors: Houkui Zhou Shutong Guo Chengxuan Li Haoji Hu Lujun Lin

Tomato is a major global crop, yet foliar diseases seriously affect yield and quality. Accurate and efficient disease identification techniques are of great significance for ensuring agricultural production safety. To address the limitations of existing methods in feature extraction capability, model complexity, and dependence on large-scale labeled data, as well as the difficulty of recognizing early-stage diseases whose visual symptoms are not yet fully developed, this paper proposes SAEFormer, a lightweight and robust disease recognition model. The model integrates a Multi-scale Selective Fusion Attention Block to enhance the ability to model multi-scale semantic information in lesion areas. In addition, by leveraging a self-supervised loss derived from dense relative localization as an auxiliary regularization term, the model’s generalization ability under limited training data is notably enhanced. To optimize normalization and improve inference efficiency, the RepBN normalization strategy is further adopted, significantly reducing computational cost while maintaining model performance. Experimental results on Dataset A show that SAEFormer achieves a Top-1 accuracy of 87.86% with 24.14 M parameters and 5.35 GFLOPs, demonstrating a favorable balance between recognition accuracy and model complexity. The training curves further indicate stable convergence during model optimization. Ablation experiments validate the complementary contributions of the proposed modules. Moreover, SAEFormer achieves competitive performance in cross-dataset evaluation on Dataset B, indicating its potential adaptability to different data distributions. Overall, SAEFormer provides an efficient approach to tomato leaf disease recognition and shows potential for deployment in precision agriculture applications.

AgriEngineering doi: 10.3390/agriengineering8090369

Authors: Xiongchu Zhang Zhongle Zhou Zhengtong Liu

Autonomous driving relying on visual navigation plays a vital role in promoting automation within the jujube industry. Conventional visual navigation strategies fail to satisfy the demands of straddle-type jujube harvesters owing to their unique row-straddling configuration and complex orchard environments. Accordingly, this paper proposes a novel binocular vision-based path detection algorithm for autonomous jujube harvesters. In the proposed method, target trunks detected from binocular images are used to generate a navigation path that better aligns with the operating trajectory of harvester. A Single Shot MultiBox Detector (SSD) deep learning model is employed to detect trunk bounding boxes. To mitigate interference induced by false detections from the deep learning model, a curve-fitting-based path calibration strategy is implemented. Experimental results demonstrate that the proposed algorithm achieves a detection speed of 14.38 fps with a false detection rate of 3.64%, satisfying the operational demands for autonomous driving of jujube harvesters. Furthermore, this algorithm can be extended to other orchard mobile robots that execute row-straddling operations similar to jujube harvesters.

AgriEngineering doi: 10.3390/agriengineering8090368

Authors: Paola Andrea Mateus Abaunza Sandra Milena García Ávila Luisa Paola Zúñiga Castro Leyiber Orlando Villa Pinto Daniel Felipe Silva Gaona Ricardo Alirio González Bustamante

Brown Rot (Monilinia spp.) and Leaf Curl (Taphrina deformans) are principal fungal diseases affecting peach (Prunus persica L. Batsch) production, lacking validated AI-based diagnostic tools in tropical highland orchards. This study presents a compact Convolutional Neural Network (3658 trainable parameters), applied identically to fruit and leaf classification, integrated with a background-removal preprocessing pipeline and evaluated through stratified 5-fold cross-validation on 800 in-situ images from four orchards in Cómbita and Choachí, Colombia. The proposed architecture achieved mean accuracies of 82.8% (fruit) and 95.3% (leaf), with AUC values of 0.87 and 0.98, and a trained model footprint of approximately 100 KB, supporting storage- and bandwidth-efficient deployment. Benchmarked against ImageNet-pretrained MobileNetV3-Small and MobileNetV2 under an identical protocol, the proposed architecture matched or exceeded MobileNetV3-Small on leaf classification despite a 257-fold smaller parameter count, and achieved comparable or lower inference latency than both larger backbones. To our knowledge, this is the first validated system for simultaneous detection of both pathogens in Prunus persica under real field conditions, combining a compact, deployment-ready architecture with an ablation-verified preprocessing pipeline. The proposed model was deployed in the DurAPP web platform, giving peach growers in tropical highland regions a practical, low-footprint diagnostic tool suited to smallholder farming conditions.

AgriEngineering doi: 10.3390/agriengineering8090367

Authors: Ionuț-Alexandru Dumbravă Petru-Marian Cârlescu Radu Roșca Vlad Nicolae Arsenoaia Alexandru-Ioan Tanasă Ioan Ţenu

The cleaning system of combine harvesters is an important component whose efficiency directly depends on the uniform distribution of the air velocity profile on the sieve surface. However, the experimental evaluation of the air flow rate is often limited by the high cost and the impossibility of simultaneous multi-point acquisition using commercial anemometers. This paper presents the design, development, and calibration of a low-cost anemometric sensor, based on an NTC thermistor, intended for aerodynamic optimization in the design and laboratory testing phase of cleaning systems. The proposed system replaces the need to use Wheatstone bridges by combining a constant current source, a 16-bit ADC converter, and an RC filter, allowing it to resolve fine voltage variations at low velocity. The algorithm integrates temperature compensation in the 3rd degree polynomial equation by simultaneously reading the environmental temperature using a digital sensor with an accuracy of ±0.1 °C. Experimental validation on the bench against the reference anemometer testo 405i showed excellent agreement (R2 = 0.998, mean bias = 0.017 m/s, and a maximum error of ±0.08 m/s), falling within the tolerance of the reference anemometer. By the ability to use multiple sensors and parallel acquisition in real time, the proposed solution offers an alternative for 2D/3D aerodynamic mapping of sieves under controlled laboratory conditions for the design of combine harvester cleaning systems.

AgriEngineering doi: 10.3390/agriengineering8090366

Authors: Alejo Osuna Davi Souza Eduardo Fernandes Nunes Leandro Tiago Manera Luis Felipe Villani Purquerio Thais Queiroz Zorzeto Cesar

Plant factories with artificial light (PFAL) enable precise environmental control for vertical indoor agricultural production systems. However, their multi-layer configuration often creates stagnant air zones with significant temperature and humidity gradients. While ventilation systems are essential for mitigating these issues, their effective design requires accurate and distributed climate monitoring. The deployment of distributed microclimate sensors in PFAL environments remains challenging when multiple identical I2C sensors are required, particularly in low-cost monitoring architectures. Because of their fixed I2C addresses, these devices cannot be connected directly to the same bus without conflicts, prompting the need for multiple controllers and thereby increasing system costs. This study evaluates the implementation of a low-cost, multiplexed IoT sensor network for PFAL microclimate monitoring based on an ESP32 microcontroller and an I2C multiplexer (TCA9548A). This network enables simultaneous operation of five SHT20 temperature and humidity sensors at different levels within a PFAL structure. The multiplexing architecture generated coherent multipoint measurements, demonstrating its practical suitability for microclimate monitoring in PFAL environments. However, the system’s performance was compromised by its reliance on the local Wi-Fi network, resulting in intermittent connectivity failures and significant data loss during internet outages. The presented findings and observed limitations underscore the need for complementary strategies, such as local data buffering or a dedicated private network, to ensure reliable long-term monitoring in smart agricultural environments.

AgriEngineering doi: 10.3390/agriengineering8090365

Authors: Bharati Ainapure Imaad Imran Hajwane Maitreyee Rajesh Ekbote Mohee Prashant Bansal Gauri Mehul Patel Bhargav Appasani Nicu Bizon Alin Gheorghita Mazare

Plant diseases pose a significant challenge to global agriculture, with early diagnosis particularly problematic for resource-limited farmers. This study introduces an AI-powered web application for classifying leaf health in two key medicinal plants, Ricinus communis (Eranda) and Vitex negundo (Nirgundi). The tool categorizes leaf images into three health states—Healthy, Medium Healthy, and Unhealthy to facilitate timely and informed decision making in crop management. A dataset comprising 2834 images of Ricinus communis and 3796 images of Vitex negundo under various conditions was created and used to train the model. Advanced transfer learning architectures, including VGG16, ResNet50, MobileNetV2, Xception, EfficientNetB0, and VGG19, were employed to enhance the classification accuracy of the system. Notably, VGG16, EfficientNetB0, and VGG19 achieved highest level of accuracy, whereas the other models showed comparatively lower level of accuracies. This work performs real-time assessment of leaf health for sustainable Ayurvedic agriculture.

AgriEngineering doi: 10.3390/agriengineering8090364

Authors: Sai Ma Zhenhua Wang Qiang Wang Gaixia Zhai Dong Li Li Yang

Smart ranching stands at the forefront of digital transformation in modern animal husbandry, with sensing and intelligent perception technologies serving as the critical infrastructure of the perception layer. In contrast to existing reviews that have broadly surveyed generic technologies or global applications, this review systematically examines advanced sensing and intelligent perception technologies through three integrated perspectives—technical principles, system architecture, and application performance—with particular emphasis placed on forage-harvesting mechanization and intelligent livestock farming in the grassland pastoral areas of China. Although the underlying sensing principles are universally applicable, the adaptation challenges and bottleneck analyses presented herein are specifically contextualized to the environmental and operational conditions of northern Chinese grassland pastoral areas.

AgriEngineering doi: 10.3390/agriengineering8090363

Authors: Shuwei Huang Jingguo Lv Boyu Wang Beibei Shen

Automatic detection of grassland livestock from unmanned aerial vehicle (UAV) imagery can support livestock resource surveys and grazing management. However, compared with larger livestock such as cattle and horses, sheep in wide-field images are typically small, densely distributed, closely spaced, and easily confused with similar background textures, resulting in missed detections, background false positives, and duplicate detections. To address these challenges, this study presents DAFS-YOLO for UAV-based livestock detection in dense sheep scenes. A Dense-Aware Shallow Convolution module (DASConv) enhances shallow-feature discriminability for small-scale sheep in dense scenes, reducing missed detections and background false positives. A Local Spatial–Semantic Complementary Mapping module (LSCM) preserves richer shallow spatial information during feature propagation, improving small-object localization. Gaussian Soft-NMS optimizes the selection of highly overlapping candidate boxes in dense sheep regions and reduces duplicate detections. Experiments on a self-constructed UAV livestock dataset show that DAFS-YOLO achieves mAP50 and mAP50:95 values of 0.930 and 0.634, outperforming YOLOv11n by 4.4 and 4.8 percentage points, respectively. The corresponding sheep-class values are 0.922 and 0.564. With only 2.65 M parameters, the model also demonstrates good cross-dataset generalization on SheepCounter, providing an effective solution for intelligent UAV-based livestock monitoring.

AgriEngineering doi: 10.3390/agriengineering8090362

Authors: Chaoyi Zhang Fei Pan Ying Ruan Yangtan Xiao Chengkai Yu Pengjun Xiang Xuliang Duan

Reliable maturity detection of Sichuan pepper is critical for intelligent harvesting, precision agriculture, and automated quality assessment. However, existing vision-based methods often suffer from performance degradation in natural orchard conditions due to illumination variation, fruit occlusion, background interference, and substantial appearance diversity. In addition, the lack of dedicated Sichuan pepper maturity datasets limits the development and reliable evaluation of deep learning-based detection models. To address these challenges, this study proposes C-MorphYOLO (Circular-Morphological YO-LO), a morphology-aware detection framework for Sichuan pepper maturity recognition under field conditions. By incorporating circular morphological information into feature representation learning, the proposed framework enhances the perception of fruit structural characteristics and improves fine-grained maturity discrimination. Specifically, a Circular Depthwise Convolution Module (CDCM) is developed to strengthen boundary and shape-aware feature extraction, while a Feature Enhancement-based Upsampling Module (FEUM) and a Multi-Scale Adaptive Spatial Attention Gate (MASAG) are introduced to improve small-object representation and detection robustness. Furthermore, a natural-scene Sichuan pepper maturity dataset containing 2763 images and 2962 annotated instances was established for model training and comprehensive evaluation. Experimental results on the held-out validation set demonstrate that C-MorphYOLO achieves a Precision of 96.1% and an mAP50:95 of 89.1%, representing absolute improvements of 2.2% and 2.7%, respectively, compared with the YOLOv12s baseline. These results demonstrate that the proposed framework effectively improves maturity recognition and localization performance, providing a practical vision-based solution for intelligent Sichuan pepper harvesting and automated agricultural management.

AgriEngineering doi: 10.3390/agriengineering8090361

Authors: Rafael Reveles-Martínez Humberto Morales-Magallanes Edgar S. Bañuelos-Treto Claudia Acra-Despradel Sandra E. Flores Huizilopoztli Luna-García Klinge Orlando Villalba-Condori

Visible foliar damage in maize caused by fall armyworm(Spodoptera frugiperda)is commonly assessed through manual field scouting, a labor-intensive process that is difficult to scale across large planting areas. Multispectral and hyperspectral sensing can support more objective assessment but remain costly and impractical for routine field use. This exploratory case study evaluated whether low-cost red–green–blue (RGB) imagery can provide preliminary indicators of visible foliar damage associated with natural S. frugiperda infestation. RGB video was recorded in maize fields in Jerez, Zacatecas, Mexico, yielding seven field-acquired sequences and 302 extracted frames. The pipeline combined hue–saturation–value (HSV)-based foliar segmentation with four visible-spectrum indices—Excess Green (ExG), Excess Red (ExR), the Visible Atmospherically Resistant Index (VARI), and the Green Leaf Index (GLI)—an ExG-ratio damage threshold, and a 17-feature descriptor per frame used to train a Random Forest (RF) severity classifier. The study is positioned relative to RGB, Unmanned Aerial Vehicle (UAV)-based, deep learning, and multimodal approaches through its emphasis on traceability, low acquisition cost, and sequence-aware validation. Using the recovered canonical HSV/ExG-ratio pipeline, sequence-level mean damage ranged from 0.1086% to 0.4511%, with maximum frame-level damage up to 7.7401%. Severity labels were percentile-derived from the canonical damage index, yielding 100 Low, 99 Medium, and 103 High samples. Under a stratified frame-level split, the RF baseline reached 76.9% accuracy and a macro F1-score of 0.759. Under leave-one-sequence-out validation, performance decreased to 57.3% overall accuracy and 0.577 macro F1-score, indicating sequence-level dependence and supporting a conservative interpretation of classifier generalization. A zero-shot comparison using the Segment Anything Model (SAM) on a curated ten-frame-per-sequence subset produced higher damage estimates (SAM 1.30–11.67% versus HSV 0.82–2.61% on the same frames), suggesting HSV segmentation may under-detect pale or bleached tissue. These results provide preliminary, exploratory evidence that low-cost RGB indices can capture information associated with visible foliar damage in the studied recordings, without establishing agronomic validation, generalization beyond this dataset, or readiness for field deployment.

AgriEngineering doi: 10.3390/agriengineering8090360

Authors: Tianyu Shi Tianrong Li Yiping Tang Shuaiying Zhan Shiming Feng Pinglan Lu Xuezhen Hong

Chinese yam planting requires horizontal placement, consistent apical–basal orientation, controlled spacing, and low-damage handling of slender seed segments. This study developed and field-evaluated an integrated vision-guided Chinese yam planter combining furrow opening, feeding, visual inspection and rejection, orientation adjustment, buffered discharge, spacing regulation, furrow tracking, seed placement, and soil covering. Conventional image processing extracted seed-segment length and end orientation, an EfficientNet-B0–kNN one-class method supported auxiliary visual-anomaly screening, and a YOLO11s-seg model with PID control-enabled furrow tracking. Field tests and a two-factor experiment were conducted under sandy-soil conditions. The prototype achieved 22 seedlings min−1, a spacing coefficient of variation of 8.4%, a miss-seeding rate of 4.5 ± 1.3%, an orientation accuracy of 99%, and a path-tracking RMSE of 2.30 ± 0.18 cm; forward velocity was the dominant factor affecting spacing uniformity. The screening module achieved a 98.6% interception rate on marker-simulated anomalies under controlled imaging conditions, while recognition of natural defects remains unvalidated. The system can reduce reliance on skilled labor, and its modular structure and quantified performance provide practical references for users and equipment manufacturers.

AgriEngineering doi: 10.3390/agriengineering8090358

Authors: Murad Irshied Al-Maaitah Rasha A. Tarawneh Mervat Sameer Al-Ajlouni Raed Lutfi Ahmad Ahmad Elmanaseer

Virgin olive oil (VOO) is highly valued for its nutritional, sensory, and health-promoting properties, which are strongly influenced by olive fruit maturity and extraction technology. This study evaluated the combined effects of fruit ripening stage and extraction system on the yield, physicochemical characteristics, bioactive compounds, fatty acid composition, and sensory quality of virgin olive oil obtained from the Jordanian Nabali Baladi cultivar. Olive fruits were harvested at four maturity indices (MI-3, MI-4, MI-5, and MI-6) during the 2024/2025 harvest season and processed using industrial two-phase and three-phase centrifugation systems under identical malaxation conditions (28–30 °C for 45 min). Oil yield, physicochemical quality parameters, total phenolic content, pigment concentrations, fatty acid composition, and sensory attributes were determined using standard analytical methods. Fruit ripening significantly increased oil yield (p < 0.05), reaching 27.80% and 30.00% at MI-6 in the two-phase and three-phase systems, respectively. However, increasing maturity was associated with higher free acidity and peroxide values and with significant reductions in phenolic compounds, chlorophyll, carotenoids, and sensory quality. Oils extracted using the two-phase system consistently exhibited lower acidity, lower peroxide values, and significantly higher concentrations of phenolic compounds than those obtained using the three-phase system. Sensory analysis showed progressive declines in fruitiness, bitterness, and pungency with advancing maturity, particularly in oils produced by the three-phase extraction system. Significant interactions between ripening stage and extraction technology confirmed that oil quality depends on both biological and processing factors. Although delayed harvesting improved oil recovery, it adversely affected the nutritional and sensory quality of virgin olive oil. The two-phase extraction system better preserved bioactive compounds and overall oil quality than the three-phase system. Harvesting olives at intermediate maturity stages (MI-4) combined with two-phase extraction provides the most suitable strategy for achieving an optimal balance between oil yield and quality under Jordanian growing conditions. Overall, this study provides a scientific basis for integrating harvesting and processing decisions within a unified quality-management framework for virgin olive oil production.

AgriEngineering doi: 10.3390/agriengineering8090359

Authors: Keila Garcia Franco Elcio Ferreira Santos Caroline Figueiredo Oliveira Selleri Mateus Roberto Gualdi Wagner Henrique Moreira Aurélio Rubio Neto José Milton Alves

The formulation of organomineral composts can be used to modulate nutrient dynamics and improve the quality of fertilizers produced from agro-industrial residues. This study evaluated the effects of compost formulation, phosphorus source, and inoculation with phosphate-solubilizing bacteria on nutrient dynamics during the composting of sugarcane filter cake. Six formulations combining filter cake, poultry litter, and agricultural gypsum were evaluated: organic compost (T1); compost enriched with reactive phosphate rock (T2); reactive phosphate rock + phosphate-solubilizing bacteria (T3); triple superphosphate (T4); triple superphosphate with a modified filter cake-to-poultry litter ratio (T5); and compost without gypsum (T6). The experiment was conducted in a completely randomized design with five replicates, and composts were evaluated at 45, 65, and 115 days. Data were subjected to analysis of variance considering formulation, composting time, and their interaction. A significant interaction between compost formulation and composting time was observed for organic matter, organic carbon, P, K, Ca, Mg, and pH, whereas N and S were independently affected by these factors. Organic matter and organic carbon contents decreased by 18% and 19%, respectively, at 115 days compared with the earlier composting periods. P-enriched formulations had 52% higher P content than non-enriched composts, while T2 and T5 showed comparable P content. Inoculation with phosphate-solubilizing bacteria temporarily increased P content during the composting phase, whereas gypsum did not improve N conservation. These findings demonstrate that formulating composts with reactive phosphate rock is a technically feasible strategy for producing P-enriched organomineral fertilizers while promoting nutrient recycling and the valorization of agro-industrial residues.

AgriEngineering doi: 10.3390/agriengineering8090357

Authors: Xiaoxing Dong Wenyi Zhao Fengzhi Piao Han Dong Zhixin Guo Yong Wang Yaling Li Tao Zhang

Solar radiation is the foundation of efficient greenhouse production. Studying how structural parameters affect light transmission and solar energy utilization efficiency is crucial for optimizing greenhouse design and increasing crop yields. This study constructed a full-process solar radiation model using ray tracing technology. It examined the solar radiation energy intercepted, captured, and distributed by the greenhouse. The simulation yielded high prediction accuracy and rapid subsequent computation after pre-calculation of the shape factor matrix. The coefficient of determination R2 was at least 0.95 for four observation points, with computations for 51 time points completed within 125 s. Subsequently, a comparative analysis of large-span insulated plastic greenhouses with different parameters was conducted during the cold and warm seasons based on eight representative solar terms. The results indicated that for a 20-m-span greenhouse at 35° N, an east-west orientation with a 15 m south roof and a 6 m ridge was better for the cold season. For comprehensive seasonal adaptability, a north-south orientation with a 5 m ridge was recommended. This study provides seasonal-oriented decision support for the structural parameter design of large-span insulated plastic greenhouses.

AgriEngineering doi: 10.3390/agriengineering8090356

Authors: Erika do Carmo Ota Ana Carolina Amorim Orrico Luís Antonio Kioshi Aoki Inoue Isabella da Silva Menezes Brenda Kelly Viana Leite Laurindo André Rodrigues Alfredo Leonel de Encarnação Marco Antonio Previdelli Orrico Junior Tarcila Souza de Castro Silva

Aquaculture intensification increases waste generation, whose composition may influence methane emissions. This study evaluated the effects of dietary protein and energy sources on nutrient digestibility and the methane production potential of Nile tilapia feces. Nine experimental diets were tested in digestibility assays: one reference diet (RD) and eight test diets containing 80% RD and 20% of an animal or plant ingredient. Feces were collected and subjected to batch anaerobic digestion. Soybean meal and meat and bone meal diets showed the highest apparent digestibility coefficients (ADC) for dry matter (84.1 and 82.6%) and energy (87.3 and 87.9%), whereas poultry by-product meal presented the lowest values. Crude protein digestibility ranged from 78.74% for blood meal diet to 94.3% for soybean meal diet. Feces from poultry by-product meal generated the highest methane potential (260.9 L kg−1 VS), whereas blood meal exhibited the highest methane production per unit of COD removed (310.5 mL g−1 reduced COD). After 10 days, poultry by-product meal accumulated the highest biogas volume, while soybean meal and blood meal showed the lowest. Principal component analysis suggested inverse relationships between nutrient digestibility and fecal biodegradability. Overall, dietary composition influenced nutrient digestibility, physicochemical characteristics of fish feces, and their methane generation potential. This integrated assessment suggests that feed formulation could contribute to strategies aimed at reducing the methane generation potential of aquaculture wastes.

AgriEngineering doi: 10.3390/agriengineering8090355

Authors: Andong Liu Chenglin Zhang Chongwu Guan Yulei Zhang

To address the challenges of low efficiency, high labour intensity, severe fish damage, and insufficient intelligence in adult fish harvesting within industrialised aquaculture systems, this study developed an integrated harvesting and sorting system incorporating bottom-drain fish collection, automatic herding, a modified screw-lifting mechanism, and digital grading. The system employs a bottom-discharge port on the culture tank—controlled by an electric ball valve—to replace manual net-herding, enabling simultaneous fish-and-water drainage. An automatic herding cart with a retractable net (equipped with surface floats and bottom weights) achieves bottom-hugging herding to prevent fish escape. A multi-column convolutional neural network (MCNN) fish-density recognition model was deployed above the collection tank to adaptively adjust the push-board position, thereby avoiding compression injury. The screw blade head was modified by removing the inlet-edge portions to eliminate the fish-cutting zone, reducing the mean damage rate from 1.8% to 1.32% (N = 12; 66.7% of trials recorded zero damage). Twelve repeated full-scale harvesting trials were conducted using largemouth bass (Micropterus salmoides; body length 20–30 cm, body mass 300–500 g) in a 2.5 m diameter PP circular tank. Results showed a mean collection efficiency of 95.94 ± 3.33% (p < 0.001), a mean damage rate of 1.32 ± 1.96%, and a mean sorting accuracy of 96.83 ± 2.50% (p = 0.014). The MCNN model attained a recognition accuracy of 78–85%. The proposed system realises automated, intelligent, and low-damage fish harvesting, providing technical support for fully automated production-line aquaculture.

AgriEngineering doi: 10.3390/agriengineering8090354

Authors: Nuri Nurlaila Setiawan Balázs Labus Ferenc Tóth Anna Divéky-Ertsey Dániel Bori Dóra Drexler

Late blight in tomato caused by Phytophthora infestans can lead to severe economic losses. Early detection is essential for effective disease management. This study investigates the spectral characteristics of healthy and late blight-infected tomato leaves and plants using non-destructive hyperspectral imaging and proposes a cost-effective machine vision system for early disease detection. Hyperspectral images from seven batches of leaf sets and six batches of whole plant sets were taken hourly over a 96 h period under both controlled and artificially infected conditions. The hyperspectral data cubes were processed with an image analysis model that identified healthy vs. infected regions. Key wavelengths (77 from leaf datasets and 24 from plant datasets) were selected using recursive feature elimination and analysed with four machine learning classifiers: k-nearest neighbour, support vector machine, random forest, and artificial neural network. The models differentiated healthy and infected tissue with high accuracy (98–99%). The hyperspectral data were simplified into a multichannel image with most informative wavelengths, using a custom spectral index and binary decision rule. Experimental limitations were addressed, and a conceptual design of practical hardware was proposed: a monochrome camera combined with a multichannel light source and polariser mounted on mobile equipment. Although further trials will be needed, this proof-of-concept study and conceptual hardware design can be adapted in other crops facing similar disease challenges.

AgriEngineering doi: 10.3390/agriengineering8090353

Authors: Jianhua Zheng Huanghui Zhao Xiaoshan Ma Guiming Huang Yongshen Liang Jinfang Liu Zhaoxi Luo Yuanlan Ye Jianru Chen

In natural scenes, the high resemblance between unripe tomatoes and foliage, combined with severe occlusion, challenges current semantic segmentation models, causing poor accuracy and indistinct boundaries. To overcome this, we propose CMAE-UNet, a camouflage suppression and edge enhancement model based on the UNet architecture. Specifically, a Global-Local Integrated Spatial Attention (GLISA) encoder merges dual-branch dilated convolutions, residual structures, and an Efficient Multi-scale Attention mechanism to expand receptive fields and highlight targets in complex backgrounds. Furthermore, a Frequency-Domain Feature Enhancement (FFE) module leverages the Fast Fourier Transform to separate and adaptively enhance distinct frequency components, effectively mitigating camouflage interference. Additionally, a Directional Edge Enhancement (DEE) module uses three-directional learnable convolutions and spatial attention to sharpen indistinct target contours. Evaluated on a custom Tomato dataset encompassing five complex scenarios, CMAE-UNet outperforms 12 prominent methods in mIoU, Dice, and Sen metrics, yielding smoother and more precise segmentation boundaries. The model robustly withstands field interference, providing strong technological support for automated tomato detection, intelligent harvesting, and growth monitoring.

AgriEngineering doi: 10.3390/agriengineering8090352

Authors: Yaoqian Liu Wenrui Zhang Yongmei Wang Tong Liu

The scientific problem addressed in this study is how a continuous mechanical architecture can maintain stable lettuce handling while improving treatment-medium access to irregular, overlapping leaf surfaces. We formulate this problem as a morphology-informed design and evaluation task. The proposed machine integrates soil removal, a reserved vision-based yellow-leaf detection and root-trimming station, multi-angle disinfection, water–air washing, combined airflow drying, film wrapping, weighing, and boxing modules on a chain-conveyor platform with bowl-shaped fixtures. The evaluation follows a design-to-evidence workflow: lettuce morphology and process requirements are mapped to module geometry; chain, lead-screw, gear, and motor parameters are checked analytically; an application-oriented geometric spray-coverage model tests fixed versus swinging bilateral nozzles; static finite element analysis screens the frame under defined design loads; and prototype assembly verifies spatial compatibility. The covered-surface proxy increased from 7.24% for fixed bilateral spraying to 13.58% for a ±35° swinging case under explicit screening assumptions, while the frame analysis gave 0.0224 mm maximum deformation and 7.30 MPa maximum von Mises stress. These outputs support a preliminary, mechanically feasible platform and a testable explanation for why adjustable spray orientation may improve access to complex lettuce surfaces. They do not constitute measured cleaning, microbial, trimming, drying, packaging, throughput, or reliability performance.

AgriEngineering doi: 10.3390/agriengineering8090351

Authors: Dan Zhou Aolong Liu Yanli Lv Pei Yuan

This paper presents a study on the prediction of greenhouse temperature and humidity using an improved particle swarm optimization (IPSO) algorithm combined with a long short-term memory (LSTM) neural network, namely IPSO-LSTM. Accurate environmental prediction is crucial for modern protected agriculture, but traditional LSTM models often suffer from suboptimal hyperparameter tuning. To address this, we propose an IPSO-LSTM model where an improved PSO with a linearly decreasing inertia weight is employed to automatically search for the optimal hyperparameters of the LSTM. Experimental results based on hourly data collected from a Venlo-type glass greenhouse over a 90-day period demonstrate the superiority of the proposed model. Specifically, in the tomato scenario, the IPSO-LSTM achieved an R2 of 0.9969 for temperature and 0.9967 for humidity, with MAPE values as low as 0.0118 and 0.0127, respectively. These results indicate that the IPSO-LSTM model significantly outperforms conventional LSTM and standard PSO-LSTM models, providing a reliable tool for intelligent greenhouse climate control.

AgriEngineering doi: 10.3390/agriengineering8090350

Authors: Jutarut Chaoraingern Akarat Pattaraanuvong Kantapon Paraksa Kantiporn Khunthong Tirawat Nontiwantok Arjin Numsomran

Small-scale and urban indoor farms typically rely on manual observation, which delays stress detection and yields inconsistent crop quality. While large language models (LLMs) and retrieval-augmented generation (RAG) have been explored for agricultural advisory systems, their integration into a single cloud-free indoor-farming platform that couples multimodal symptom interpretation with autonomous environmental control remains largely unexamined. This study presents an integrated platform built around an agentic AI advisory pipeline that runs entirely on-device on commodity hardware. The pipeline couples a RAG-grounded Mistral 7B language model with a LLaVA 7B vision-language model through condition-based routing, intent classification, multi-step reasoning, and an LLM validation gate, delivering context-aware text and image-based symptom-level guidance from a conversational interface. The advisory layer operates alongside vision-based plant monitoring and a deliberately isolated threshold-based control layer, in which an ESP32 microcontroller autonomously actuates irrigation and lighting against predefined thresholds while a Raspberry Pi 5 performs continuous plant detection and browning monitoring. On Cos lettuce, the advisory pipeline achieved 82.00% weighted accuracy across 50 queries spanning health, symptom, watering, pest, root-health, and growth-stage categories, scored against established plant pathology and postharvest references, with no incorrect responses recorded. The study contributes the design of an agentic advisory pipeline and its integration into a working, cloud-free indoor-farming platform, providing an on-device foundation for intelligent small-scale farming.

AgriEngineering doi: 10.3390/agriengineering8080349

Authors: Linmin Yu Rongfang Qu Qifeng Wu Xiaofei An Ruxiao Bai Lingxian Zhang Chunmei Zhu

In protected agriculture such as greenhouses, pest and disease monitoring via UAVs and fixed cameras suffers from extremely small object proportions owing to shooting altitude constraints, posing considerable detection challenges. Moreover, fine-grained annotation of numerous small-scale images incurs prohibitive costs. Targeting this bottleneck, this paper proposes a cross-scale unified semantic space learning framework and introduces an end-to-end DS-DETR detector based on DETR. Unlike existing methods relying on domain adaptation, multi-scale fusion, or super-resolution reconstruction, this work explicitly models instance-level cross-scale semantic correlation, transferring fine-grained semantics from large-scale close-up images to small-scale scene feature space. A Single-Point Dual-Shooting (SPDS) strategy is adopted to collect high-fidelity paired images via ordinary smartphones at low cost. A dual-stream encoder with cross-view attention and an instance-level contrastive loss align features of identical instances in a unified semantic space. A self-built CropScale-Det dataset covering three crop diseases is constructed in greenhouse scenarios. Experimental results show that DS-DETR achieves 42.5 ± 1.2% mAP@50 under limited annotations, outperforming YOLOv8-n by 11.2%, with small-target average precision reaching 26.8 ± 1.1%. Ablation experiments and feature visualization validate the effectiveness of the designed mechanism. This approach considerably reduces reliance on large-scale densely annotated data, establishing a data-efficient proof-of-concept for small-scale pest detection in protected agriculture.

AgriEngineering doi: 10.3390/agriengineering8080348

Authors: Khunnithi Doungpueng Jirasin Prueksawan Lalita Panduangnat Prasit Somjinda Jetsada Posom

Certified seed purity is a prerequisite for sustaining the agronomic performance and commercial value of Khao Dawk Mali 105 (KDML-105), Thailand’s premium aromatic rice; however, conventional inspection methods are destructive, labour-intensive, and poorly suited to high-throughput operations. This study developed a non-destructive purity inspection system integrating five-band MSI acquired using a MicaSense RedEdge-MX sensor with three machine learning classifiers: Extra Trees (ET), Random Forest (RF), and Support Vector Machine (SVM). The system was evaluated systematically across four illumination levels (360.90–13,188.46 lx) to discriminate KDML-105 from three morphologically similar contaminating varieties: Chainat-1, RD-6, and RD-15. Two-way ANOVA confirmed that classifier type was the dominant performance determinant (η2 = 0.847). Under optimal illumination (L4, 13,188.46 lx), ET achieved the highest accuracy (88.8%), recall (86.6%), and F1-score (88.4%) with a training time of 0.098 s. External validation confirmed model generalisability: KDML-105 seeds were identified with 90.6% accuracy and 95.6% recall, while Chainat-1 and RD-6 yielded accuracies of 85.1% and 90.6%, respectively; RD-15 remained challenging (62.9%; 67.4%) owing to spectral proximity to KDML-105. These findings establish MSI combined with ET classification as a viable, cost-effective approach for automated seed purity screening in certified rice production.

AgriEngineering doi: 10.3390/agriengineering8080347

Authors: John D. Wanjura Matt Bohn Gregory A. Holt Mathew G. Pelletier

Radio frequency identification (RFID) tags are now included in the plastic wrap used to protect seed cotton formed into cylindrical or “round” modules on modern cotton harvesters. In this paper, the development of a new work tool system for handling round modules with articulated wheel loaders or telehandlers is described. The work tool system reads the module-specific identification number from the RFID tags in the wrap and associates the module’s weight, seed cotton moisture content, GPS location, cotton ownership, and load information with the module serial number. Finite element analysis of critical components indicated that the system was capable of processing modules weighing 3178 kg (7000 lb.) Module weight was determined on the loader using measurements of the hydraulic pressure in the lift arm circuit. Seed cotton moisture content was measured using a custom-designed resistance-based probe. To help reduce the potential for lint bale contamination from module wrap plastic, the work tool system was designed to rotate modules so that the wrap can be cut within the manufacturer-recommended cut zone before the wrap is removed at the gin. The total cost for the system configured for fully automated data collection and module rotation control was $28,909.

AgriEngineering doi: 10.3390/agriengineering8080346

Authors: Genta Rexha Erion Papalilo Arbri Jesku Aleksandër Biberaj Elson Agastra

Olive cultivation is an important agricultural sector in Albania, where disease monitoring remains challenging due to fragmented orchards, heterogeneous management practices, and limited adoption of advanced sensing technologies. Recent studies have shown the potential of unmanned aerial vehicles (UAVs), sensor systems, and artificial intelligence (AI) for monitoring specific olive diseases in different olive-growing regions. However, the suitability of olive diseases reported in Albania for monitoring with UAVs and AI has not yet been systematically assessed. This paper examines the main olive diseases relevant to Albania using a semi-quantitative, literature-based multicriteria framework in which five monitoring criteria are scored from 1 to 3 and combined using equal weights. It also formalizes a UAV-first screening workflow that links image acquisition, AI-based canopy segmentation, feature extraction, anomaly scoring, decision thresholds, and targeted field or laboratory confirmation. Based on this assessment, the study identifies the most promising disease targets for future research and outlines key considerations for sensor selection and validation. The paper provides a context-specific foundation for future UAV- and AI-supported disease monitoring in Albanian olive groves. The revised analysis also distinguishes indicative acquisition targets from experimentally validated detection limits and specifies practical requirements for ground truth, radiometric calibration, dataset design, and geospatial validation.

AgriEngineering doi: 10.3390/agriengineering8080345

Authors: Hieu V. Nguyen Duc A. Le Nghi T. Nguyen

Fresh paddy transported by barge in the Mekong Delta can accumulate respiration heat during journeys longer than 24 h, accelerating quality deterioration. This study evaluated forced aeration using a 1 m2 × 2.5 m laboratory model (approximately 1.4 t) and a field trial on a 60 t barge with aerated and non-aerated compartments. Fresh paddy (24.1 ± 1.4% wet basis) was aerated at an average superficial air velocity of 0.053 m s−1, equivalent to 129 m3 h−1 t−1. Grain temperature, moisture content, airflow, static pressure, air enthalpy, and milling quality were measured. In field trials, aeration reduced grain temperature to approximately 29.0 °C after 6 h, close to ambient temperature (29.3 °C), whereas non-aerated paddy reached 37.8 °C. The temperature difference of approximately 10–11 °C was maintained during transportation. Mean specific heat removal was 616 kJ h−1 t−1, and cumulative thermal exposure decreased from 243.8 to 13.1 °C·h, corresponding to 94.6% suppression. Moisture content and all measured quality indicators did not differ significantly; chalkiness showed a numerical decrease from 7% to 4% (p = 0.101). Forced aeration can therefore stabilize high-moisture paddy during barge transportation and reduce heat-related quality loss.

AgriEngineering doi: 10.3390/agriengineering8080344

Authors: Érika Cecília Pereira da Costa Ana Carolina Amorim Orrico Brenda Kelly Viana Leite Isabella da Silva Menezes Marco Antonio Previdelli Orrico Junior Rusbel Raul Aspilcueta Borquis Juliana Dias de Oliveira Luana Galdino Lopes Fernando Miranda de Vargas Junior

Monensin supplementation in ruminant diets to improve energy efficiency also results in the excretion of this compound through manure. However, in sheep, monensin excretion rates remain unknown and are of concern due to the potential risks of environmental contamination, as well as possible impacts on waste treatment and recycling processes. Therefore, the objective of this study was to evaluate monensin excretion in sheep manure and its influence on the anaerobic digestion of these residues when increasing dietary doses of monensin were administered. Sheep were fed 0, 8, 14, and 20 mg of monensin kg−1 of dry matter intake, and the collected manure was used to prepare substrates for feeding biodigesters. The substrates were digested either as whole material or after sieving for separation and removal of the solid fraction and were maintained under anaerobic digestion for hydraulic retention times (HRT) of 20 and 30 days. Increasing monensin doses resulted in higher excretion rates, reaching 13.32%, 32.01%, and 34.57% for the 8, 14, and 20 mg doses, respectively. When fraction separation was performed prior to digestion, higher monensin concentrations were detected in the sieved substrates; however, the loading rates applied to the biodigesters were similar. During anaerobic digestion, fraction separation promoted greater reductions in volatile solids, with mean reductions of 47.88% and 53.30% for HRTs of 20 and 30 days, respectively. Consequently, biogas production potential increased, reaching 220.6 and 306.4 L kg−1 VS, with methane concentrations of 72% and 69.87% for HRTs of 20 and 30 days, respectively. Both HRTs were effective in reducing monensin levels, achieving a minimum removal efficiency of 80.14% by the end of the digestion process. Under the conditions of this study, a 30-day HRT combined with prior solid–liquid separation improved biogas and methane production. However, further studies are needed to evaluate economic feasibility, scalability, operational costs, and management of the separated solid fraction.

AgriEngineering doi: 10.3390/agriengineering8080343

Authors: Jose M. Diaz-Larios Percy A. Luna-Flores David E. Bances-Saavedra Juan Arcila-Diaz

The objective of this research was to develop a system capable of detecting diseases in maize leaves through the analysis of foliar images, using machine learning techniques to support early diagnosis in the agricultural sector. A custom dataset of field-captured images was built, and a preprocessing workflow was applied that included data augmentation, segmentation, feature extraction, and normalization. Subsequently, three models: EfficientNetB0, DenseNet121, and EKNN were trained and evaluated to determine the architecture with the best classification performance. The results showed that model performance varied according to how each approach processed visual features, with the EKNN model achieving the highest overall accuracy of 94.33%, outperforming EfficientNetB0 (89.70%) and DenseNet121 (88.04%). The CNN-based architectures achieved adequate classification in diseases with well-defined patterns but presented limitations when dealing with visually similar lesions. In contrast, the EKNN model, which relies on segmentation and enhanced feature extraction, achieved the best overall performance, demonstrating the importance of preprocessing in diagnostic accuracy. Finally, the selected model was integrated into a functional web application, validating its practical utility as a tool for the early detection of diseases in maize leaves. This research demonstrates that machine learning can effectively assist farmers and agricultural technicians in the efficient identification of plant diseases, contributing to improved productivity and better decision-making in the field.

AgriEngineering doi: 10.3390/agriengineering8080342

Authors: Agadir Jhonatan Mossmann Roberto Carlos Orlando Cristiano Márcio Alves de Souza Leonardo França da Silva Victor Crespo de Oliveira José Rafael Franco Dhiones Kenedys Ulisses Dias Filipe Bittencourt Machado de Souza

The pursuit of greater efficiency in agricultural operations, particularly in input application, has led producers to adopt strategies aimed at minimizing production costs. Aerial fertilizer application has emerged as a viable alternative due to its high operational efficiency and its ability to operate in conditions where ground-based application is not feasible. However, few studies have evaluated its efficiency, resulting in limited technical guidelines for calibration and adjustment. In this context, the present study aimed to evaluate the quality of broadcast application of solid potassium fertilizer via aircraft. The experiment was conducted using a split-plot design in a factorial arrangement with three flight altitudes (10, 15 and 20 m) and four application rates (50, 75, 100 and 125 kg ha−1), each with three replications. Longitudinal and transverse distributions were evaluated, as well as the correlation between wind speed and applied doses. The longitudinal analysis showed that flight altitude influenced both the uniformity of distribution and the effective dose applied, with a significant interaction between factors. In the transverse analysis, the overall transverse deposition pattern was predominantly governed by the 4.0–2.0 mm particle-size fraction, which represented approximately 90% of the recovered fertilizer mass across all flight heights. Although the granulometric composition remained consistent, the spatial distribution of individual particle-size classes varied with flight height, particularly for the finer fractions. Overall performance was achieved and the best results were observed at a 25 m swath width with flight heights between altitudes of 10 and 15 m. While the 10 m flight height resulted in lower eccentricity and greater fertilizer deposition, the 15 m flight height provided lower coefficients of variation after overlap simulation across most application rates, indicating more uniform transverse distribution.

AgriEngineering doi: 10.3390/agriengineering8080341

Authors: Leandro Fagundes Mançano Ana Carolina Moura de Sena Aquino Osvaldo Resende Eliane Mauricio Furtado Martins Breno Pereira de Paula Gabriel Henrique Horta de Oliveira

Acerola is a tropical fruit rich in vitamin C and other bioactive compounds, but its high perishability limits storage and distribution. This study evaluated the hygroscopic behavior and storage stability of acerola pulp powder produced by foam-mat drying. The pulp was foamed with Emustab and dried at 60 °C. Fresh pulp and powder were analyzed for water activity, total titratable acidity, reducing sugars, vitamin C, carotenoids, total phenolic compounds, and antioxidant activity. Powder adsorption isotherms, hygroscopicity, caking, and solubility were evaluated at 10 and 25 °C under relative humidities of 20, 50, 70, and 90%. Drying increased the reducing sugar concentration from 5.24% to 45.49%. The Oswin model best described the Type II sigmoidal isotherms, with R2 values of 0.9921 and 0.9974 and mean relative errors of 4.61% and 3.27% at 10 and 25 °C, respectively. Equilibrium moisture content was higher at 25 °C, an atypical behavior associated with the high concentration of low-molecular-weight sugars. Hygroscopicity ranged from 8.77 ± 0.72% to 53.39 ± 0.51%. Although the powder has potential as a functional ingredient, its high hygroscopicity and limited physical stability require moisture-barrier packaging and formulation strategies to improve storage and industrial applicability.

AgriEngineering doi: 10.3390/agriengineering8080340

Authors: Marly Guelac-Santillan Julio Puscan-Rojas José Anderson Sánchez-Vega Angel Fernando Huaman-Pilco Angel J. Medina-Medina Katerin M. Tuesta-Trauco Jorge Marino Canta-Ventura Elgar Barboza Jhon A. Zabaleta-Santisteban

Unmanned aerial vehicle (UAV)-based multispectral remote sensing has emerged as a promising tool for monitoring crop physiological status and supporting precision disease management. However, the capacity of multispectral vegetation indices to detect foliar diseases under commercial field conditions remains insufficiently understood, particularly in perennial crops grown in humid tropical environments. This study evaluated the potential of UAV-derived multispectral vegetation indices to assess the physiological response of avocado (Persea americana Mill.) canopies affected by anthracnose caused by Colletotrichum fructicola in Amazonas, Peru. Disease incidence and severity were assessed through field evaluations, while multispectral imagery was acquired using a UAV equipped with a MicaSense RedEdge-MX Dual sensor. Vegetation indices including the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Red Edge Index (NDRE), Red Edge Chlorophyll Index (CIred-edge), Plant Senescence Reflectance Index (PSRI), and Visible Atmospherically Resistant Index (VARI) were calculated, and their relationships with anthracnose incidence were analyzed using Spearman’s rank correlation. Field observations confirmed a high incidence of foliar anthracnose with a heterogeneous spatial distribution across the orchard. Multispectral imagery successfully characterized spatial variability in the canopy physiological condition, revealing differences in vegetation vigor, chlorophyll-related reflectance, and senescence among experimental blocks. Nevertheless, none of the evaluated vegetation indices showed statistically significant correlations with anthracnose incidence (p > 0.05), indicating that spectral variability primarily reflected the general canopy physiological status rather than a disease-specific spectral response. These findings demonstrate that UAV-derived multispectral vegetation indices are valuable for monitoring spatial variability in the avocado canopy condition but have limited capability for independently detecting anthracnose under humid tropical field conditions. Future studies integrating multi-temporal UAV acquisitions, hyperspectral and thermal imagery, LiDAR, environmental variables, and machine-learning approaches are expected to improve the early detection and spatial prediction of anthracnose in avocado production systems.

AgriEngineering doi: 10.3390/agriengineering8080339

Authors: Jiunyuan Chen Chiachung Chen

Greenhouses in subtropical and tropical regions must be designed as agricultural engineering systems adapted to local climates, rather than simply replicating the “insulation” models of temperate areas. Under extreme climatic conditions such as persistent high temperatures, intense solar radiation, high humidity, heavy rainfall, and frequent extreme winds, greenhouses transform from enclosed insulation layers into selective climate filters, mitigating crop stress while maintaining close contact with the outdoor environment. This paper summarizes how these climate drivers are reshaping the use, structure, and control concepts of greenhouses, emphasizing that the performance of warm-zone greenhouses depends primarily on heat dissipation, humidity management, and biohazard control, rather than heating and insulation. In this review, we analyze the climatic boundary conditions that define warm-climate conservation cultivation, including long-term overheating risk, high UV radiation, vapor pressure deficit, and suppressed condensation tendency, as well as storm-induced uplift and dynamic loads. These constraints necessitate unique structural forms: tall, lightweight, well-ventilated building types with large roof and side openings, roof geometries that facilitate rainwater runoff, sophisticated drainage systems, and corrosion-resistant materials suitable for humid and coastal environments. Because insect netting significantly reduces ventilation, pest control and temperature regulation become co-design issues, requiring oversized vents, optimized airflow paths, and hybrid roof–mesh structures. Ventilation is considered the primary climate-control mechanism, supplemented by passive cooling measures such as shading and radiation/optical management (e.g., diffuse films and near-infrared-selective films). Active evaporative cooling is considered a conditional measure due to humidity limitations and disease risks. This paper also integrates the impacts on specific crops (fruits and vegetables, leafy greens, and orchids). It highlights emerging trends: typhoon-resistant and adaptive geometries, computational fluid dynamics (CFD)-based design, and sensor-rich IoT/digital twin control frameworks. These principles collectively establish a coherent design framework for achieving resilient, resource-efficient greenhouse production in warm climates.

AgriEngineering doi: 10.3390/agriengineering8080338

Authors: Bernardo José Marques Ferreira Caroline Lima De Andrade Tiago Bresolin Janiele Santos De Araújo Eduardo Michelon Do Nascimento Salete Alves De Moraes Marcelo Caique Félix Rodrigues Sánara Adrielle França Melo Daniel Ribeiro Menezes Mário Adriano Ávila Queiroz

Accurate body weight monitoring is essential for efficient sheep production, yet conventional weighing methods are labor-intensive and require frequent animal handling. Computer vision provides a promising non-invasive alternative; however, many published studies rely on validation strategies that may overestimate predictive performance because repeated observations from the same animals are simultaneously included in training and testing datasets. This study developed and evaluated an integrated analytical framework for image-based body weight estimation in ewes that combines standardized image processing, robust frame-level quality control, longitudinal statistical modeling, and strict leakage-controlled validation. A longitudinal dataset comprising 20 Dorper ewes monitored over six sampling periods was acquired using top-view depth imaging synchronized with body weight measurements under semi-arid conditions. The analytical framework integrated three complementary modules: longitudinal mixed-effects modeling, early prediction of final body weight, and contemporaneous body weight estimation. Predictive analyses were evaluated using nested leave-one-animal-out cross-validation, in which all preprocessing, correlation filtering, feature selection, model optimization, and model selection were performed exclusively within the training animals of each outer fold. The longitudinal mixed-effects model accurately characterized individual growth trajectories (conditional R2 = 0.98). Initial body weight remained the strongest predictor of final body weight (R2 = 0.770), whereas image-derived morphometric descriptors alone showed limited predictive performance under strict animal-level validation (R2 = −0.899 to −0.031). Combining baseline body weight with selected morphometric descriptors produced modest but biologically informative improvements, achieving a maximum R2 of 0.839. Contemporaneous body weight estimation achieved moderate predictive performance (maximum R2 = 0.332) and revealed temporal changes in the importance of morphometric descriptors throughout growth. Overall, the proposed framework provides a reproducible methodology for evaluating image-derived phenotypes under rigorous animal-level validation, contributing to the development of more robust, interpretable, and biologically grounded computer vision systems for Precision Livestock Farming.

AgriEngineering doi: 10.3390/agriengineering8080337

Authors: Najia Ait Hammou Abdellah El Aissaoui Yassine Abouch Hajar Mousannif

Weeds create a significant challenge in agricultural production by competing with crops for essential resources such as nutrients, sunlight, and water. This competition leads to reduced crop yields and quality, resulting in substantial economic losses. Consequently, there is a critical need for effective weed control strategies to mitigate the impact of unwanted plant growth and ensure sustainable agricultural practices. In precision agriculture, enabling autonomous navigation between crop rows during tasks such as weeding and harvesting presents a significant research challenge, particularly when leveraging cost-effective technological solutions. Effective robot navigation requires adaptive traffic management strategies and robust object recognition capabilities to distinguish between cultivated and uncultivated areas. In fact, the integration of computer vision techniques into these systems is essential for optimizing trafficability in cropping fields and enhancing robots’ dynamics for better working efficiency in agricultural environments. This review addresses the challenge of enhancing inter-row navigation in field crops and delivering reliable guidance for autonomous agricultural robots. A PRISMA-based systematic review methodology was adopted to identify, screen, and analyze 38 relevant studies selected from the Scopus and Web of Science databases. The selected studies are classified according to their target platform (Unmanned Ground Vehicles and Unmanned Aerial Vehicles) and grouped into three methodological categories: conventional computer vision, deep learning architectures, and hybrid approaches. The findings provide practical guidance for selecting appropriate vision-based crop row detection technologies according to the application requirements and highlight key research directions toward more robust, cost-effective, and adaptable autonomous navigation systems. This article presents an outline of artificial-intelligence-based row detection methods used in agricultural fields and a classification of related semantic segmentation approaches. Unlike previous surveys, it provides an overview of the technological progress in agricultural robots and navigation based on systems vision for crop row detection, with a focus on comparisons balancing technical performance with economic and practical constraints.

AgriEngineering doi: 10.3390/agriengineering8080336

Authors: Jared Nishimoto Jason Dzurisin Roberto Rodriguez Melissa A. Johnson

Achieving uniform agrochemical deposition in coffee is challenging because canopy structure, terrain, and wind conditions influence spray movement and retention. This study compared an unmanned aerial spray system (UASS), backpack sprayer, and tractor-mounted sprayers across three commercial coffee farms on Hawai‘i Island. Spray coverage, droplet density, droplet size metrics, and operational efficiency were evaluated using water-sensitive cards positioned throughout the canopy and analyzed using mixed-effects models. UASS produced significantly lower spray coverage and droplet density than the ground-based application systems, whereas backpack and tractor sprayers did not differ. Deposition patterns varied with canopy position, with application method effects depending on canopy height, depth, and aspect. Volume median diameter decreased in the upper canopy and with increasing wind speed, while relative span varied modestly among methods and was greater within the canopy interior. Canopy position and wind strongly shaped agrochemical deposition across spray platforms. Although UASS required less field labor and improved accessibility in terrain-limited systems, these operational advantages were accompanied by reduced deposition relative to ground-based sprayers. These findings demonstrate that canopy position and environmental conditions strongly influence agrochemical deposition and support UASS as a complementary application platform rather than a direct replacement for conventional sprayers under the conditions evaluated.

AgriEngineering doi: 10.3390/agriengineering8080335

Authors: Kristóf Utassy Tamás Kocsis Andrea Pomázi

Modern viticulture is undergoing a paradigm shift, transitioning from a plant-centric view to the holobiont concept, which considers the grapevine (Vitis vinifera L.) and its associated microbiota as a single co-evolved functional unit. This review synthesizes current knowledge on the grapevine holobiont, identifying the drivers of microbial assembly (genotype, environment, management) and their functional implications for plant health and wine quality. We discuss the pillars of plant health, defining disease as a state of dysbiosis rather than merely the presence of a pathogen. Special attention is given to the spatial compartmentalization of the microbiome, from the gating mechanisms of the rhizosphere to the transient diversity of the anthosphere. Furthermore, we highlight the methodological evolution from culture-dependent techniques to Next-Generation Sequencing (NGS) and the emerging role of MALDI-TOF MS as a rapid, cost-effective tool for real-time monitoring. Finally, we propose a roadmap for microbiome-assisted viticulture that utilizes synthetic microbial communities (SynComs) and hologenomic breeding to enhance resilience against climate change.

AgriEngineering doi: 10.3390/agriengineering8080334

Authors: Isabel Correia Silva Almeida Danilo José Barros Michelle Justino Gomes Alves Belchior Oliveira Trigueiro Silva Breno Leonan Carvalho Lima Felipe José Cury Fracetto Giselle Gomes Monteiro Fracetto Ademir Oliveira Ferreira Erika Valente Medeiros Mario Andrade Lira

Water scarcity has a significant impact on global agriculture, particularly in semi-arid regions, hindering economic development. The use of recycled urban wastewater in agriculture is a sustainable practice; however, it is essential to assess its impact on soil carbon stocks and microbial activity. This study hypothesized that the use of wastewater in soil cultivated with forage cactus and amended with 8 or 12 tons of sorghum straw as soil cover could increase carbon stocks, microbial biomass, and microbial activity compared to bare soil, even after only 8 months. The experiment was conducted in a tropical semi-arid region of Brazil, based on a factorial design with different cactus intercropping systems and soil cover treatments under wastewater irrigation. Overall, soil carbon stocks did not increase significantly compared to the control, although they increased by approximately 21% over the study period. However, soil cover increased C-CO2 emissions by 70% after 4 and 8 months. Microbial biomass carbon increased by 65% compared to the baseline (time 0), particularly in treatments with soil cover. Soil cover and consortium under wastewater irrigation improved microbial activity and biomass, even over a short experimental period, indicating a sustainable soil management strategy to enhance soil organic matter quality and microbial properties.

AgriEngineering doi: 10.3390/agriengineering8080333

Authors: Lilian Yang Bing Lu Margaret Schmidt Shujian Jin Ali Jamali David McCaffrey

Accurate mapping of crop residue cover (CRC) is important for sustainable agricultural management because residue supports soil health, erosion control, and carbon sequestration. Traditional ground-based CRC methods are labor-intensive and spatially limited, increasing interest in UAV-based remote sensing. UAVs can carry RGB, multispectral, and hyperspectral sensors, which capture different spectral information for distinguishing crop residue from soil. This study compared four high-spatial-resolution (2.5 cm) UAV imagery types—RGB, multispectral, visible–near-infrared (VNIR) hyperspectral, and shortwave infrared (SWIR) hyperspectral—for fine-scale CRC classification. A fully connected neural network (FCNN) was developed to classify residue and soil pixels. Performance was evaluated using two complementary approaches: pixel-level accuracy assessment based on manually delineated image samples and plot-level validation against residue percentages derived from ground photos. Results showed that high pixel-level classification accuracy values were achieved across all imagery types, with overall accuracies above 94%. However, plot-level validation revealed that sensor performance depended on the evaluation metric considered. Multispectral imagery produced the highest R2 with ground photo-derived reference CRC values (R2 = 0.672). These results indicate that greater spectral dimensionality did not necessarily improve plot-level CRC estimation under the tested field conditions. More importantly, the findings show that high pixel-level classification accuracy does not necessarily translate into stronger plot-level CRC estimation, highlighting the importance of using complementary validation approaches when evaluating UAV-based CRC estimation.

AgriEngineering doi: 10.3390/agriengineering8080332

Authors: Paulo Carteri Coradi Adílio Flauzino de Lacerda Filho José Benício Paes Chaves Luiz Fernando Albino Teixeira

The study evaluated the effects of corn grain deterioration under different storage conditions in processed feeds and broiler digestibility. The corn grains were stored in chambers (quality corn and contaminated corn) at different storage temperatures and relative humidity (10 °C and 90%, 30 °C and 40%), and the evaluations were carried out at five storage times (zero, three, six, nine, and twelve months). After feed formulation (zero, six, and twelve months of corn stored), the broilers (504 animals) were fed for up to 14 days. The excreta collection was done twice per day for metabolism analysis. The 10 °C and 90% RH conditions influenced the loss of dry matter; the physical quality of corn grain was lower for the 30 °C and 90% RH conditions. When we obtained lower dry matter losses, the microbiological and mycotoxin levels were directly influenced by the loss of dry matter in corn. The storage time increased the risk of fungi (8.5 × 107 CFU g−1), bacteria (7.5 × 106 CFU g−1), and aflatoxins (64.96 µg kg−1), reaching levels above acceptable limits for the corn quality and interfering with digestibility and animal metabolism. In addition, the storage time was the determining factor in the nutritional loss of corn quality, feed, and animal digestibility. Furthermore, the storage time reduced the levels of crude protein (5%), gross energy in the feed (3200 kcal kg−1), metabolizable energy (3000 kcal kg−1), and animal digestibility (3100 kcal kg−1) after six months of storage. Based on the results of this study, the feed processing industry should pay close attention to the origin of stored corn and the associated environmental conditions, as these factors led to significant changes in grain quality, directly impacting animal nutrition and production. Therefore, it is recommended that the production chain invest more in the sector—specifically in adequate infrastructure and grain storage monitoring and control systems—to provide higher-quality raw materials to the processing industry.

AgriEngineering doi: 10.3390/agriengineering8080331

Authors: Yuelin Deng Hinayah Rojas de Oliveira Richard M. Voyles Upinder Kaur

Enteric methane measurement from ruminant livestock faces fundamental trade-offs between accuracy and operational feasibility. Existing methods quantify methane after eructation and atmospheric dilution, limiting temporal resolution and confounding biological signals with environmental variables. We present the Smart Cannula-mounted Optical Unit for Trace methane (SCOUT), an autonomous system for continuous in vivo monitoring of ruminal headspace methane concentrations. SCOUT uses a closed-loop gas recirculation circuit that samples the headspace continuously without venting gas to the atmosphere and mounts onto a standard cannula plug without degrading its seal integrity. SCOUT was deployed on cannulated Simmental heifers under contrasting dietary treatments. Headspace concentrations were two to three orders of magnitude above concurrent ambient sniffer readings, providing substantially greater signal resolution for characterizing methane dynamics. High-frequency monitoring revealed concentration changes associated with postural transitions and feeding on timescales inaccessible to ambient methods. Cross-platform comparison with ambient sniffers showed that eructation events produced the expected inverse concentration signature, supporting the validity of the in vivo concentration signal. These results demonstrate that the rumen headspace contains continuous, biologically interpretable methane signals that SCOUT can reliably access, establishing the measurement infrastructure necessary for developing concentration-to-flux models that would support precision phenotyping, emission proxy calibration, and mitigation strategy evaluation.

AgriEngineering doi: 10.3390/agriengineering8080330

Authors: Sebastian Tonu Ioana-Alexandra Tonu Otilia Zvorișteanu Ștefan Daniel Achirei

Accurate estimation of grassland biomass is fundamental for designing sustainable grazing strategies and optimizing pasture management, yet conventional field methods remain labor-intensive, destructive, and difficult to scale. In this study, we exploit recent advances in computer vision to estimate multiple components of grassland biomass from overhead RGB imagery. The analysis is based on the Image2Biomass dataset, comprising 1162 annotated images of grasslands across Australia, each paired with laboratory-validated biomass measurements. A structured preprocessing pipeline was implemented, including exploratory data analysis, outlier mitigation, and logarithmic transformation of target variables, in accordance with the dataset evaluation protocol. Building on this foundation, we propose an encoder–decoder regression framework that integrates self-supervised visual representation learning with ensemble-based prediction. The encoder employs a DINOv2 Giant model as a feature extractor to capture detailed spatial and structural characteristics of the sward, while the decoder uses a stacking ensemble combining LightGBM, XGBoost, and Ridge Regression. Across 15 repetitions of shuffled four-fold cross-validation, the cross-fitted stacking ensemble achieved a weighted coefficient of determination of Rw2=0.7757±0.0171, a weighted mean absolute error of 8.3867±0.2736 g, and a weighted root mean squared error of 13.3994±0.4995 g. The ensemble significantly outperformed LightGBM, XGBoost, and Ridge Regression on the primary weighted R2 metric in paired comparisons (Holm-adjusted p<0.001 for all three comparisons). These results highlight the potential of computer vision methods as scalable, non-destructive tools for operational monitoring of grassland biomass, supporting more informed agronomic decision-making in pasture-based systems.

AgriEngineering doi: 10.3390/agriengineering8080329

Authors: Kenny Paul Vera Pils Pablo Rischbeck Hans-Peter Kaul

Cereal crops, including wheat and barley, are essential for global food security, but their productivity is strongly affected by nitrogen availability and water limitation. This study investigated the phenotypic responses of two commercially significant spring wheat cultivars, Videodur (DU) and Sensas (SW), and two spring barley cultivars, Tiroler Imperial (SG1) and Amidala (SG2), exposed to two nitrogen regimes, low nitrogen at 25 kg N/ha (N25) and high nitrogen at 130 kg N/ha (N130), under drought and well-watered conditions. Plants were monitored from the late vegetative stage through maturity under controlled multivariable climatic conditions similar to field settings. A high-throughput phenotyping workflow was applied, combining precision watering, RGB imaging, infrared thermography, and VNIR–SWIR hyperspectral imaging to quantify plant growth, projected digital biomass, plant temperature, water use efficiency, and spectral vegetation indices associated with pigment dynamics, water status, maturation, and senescence. The results revealed cultivar-specific responses to combined nitrogen and drought stress. Under drought conditions, the high nitrogen treatment (N130) increased plant temperature (Tplant) for barley (cv. SG1) and wheat (cv. SW) compared to N25, thereby accelerating early maturation. However, the decline in chlorophyll was not uniformly faster across all cultivars tested. The DU cultivar exhibited superior chlorophyll absorption and reflectance, indicating better drought adaptation compared to other tested species. The high nitrogen treatment (N130) reduced water use efficiency (WUE) in the SW and SG2 cultivars compared to N25, implying that these cultivars used more water. Enhanced nitrogen did not consistently improve water use efficiency but did accelerate the growth cycle. SG2 was particularly sensitive to drought, showing declines in vegetation indices, except for the Water Content Index, highlighting the need for precise water and nitrogen management. Overall, the integration of hyperspectral, thermal, RGB, and water use measurements enabled the identification of trait signatures linked to drought adaptation, nitrogen response, maturation, and senescence. These findings provide practical insights for optimizing nitrogen and irrigation management and for supporting breeding strategies aimed at improving cereal crop resilience under climate-change-associated stress conditions.

AgriEngineering doi: 10.3390/agriengineering8080328

Authors: Wensheng Yuan Shuangcheng Xie Yugang Feng Chengqian Jin Zhenjie Qian Fuqiang Gou

In this study, a miss-seeding detection and self-compensation system based on suction-hole seed-carrying state recognition was developed to address miss-seeding and reduced seed-spacing uniformity caused by missed pickup at suction holes in air-suction soybean seed-metering devices. A through-beam photoelectric sensor was installed between the seed-cleaning and seed-discharge zones to detect the seed-carrying state of each suction hole in real time. With a programmable logic controller (PLC) as the control unit and an encoder for seed-metering disc speed acquisition, the system performed missed-pickup identification, compensation pulse output, and operating-state monitoring. When a suction hole without a seed was detected, the control system triggered a short-term acceleration of the seed-metering disc, enabling the subsequent normally loaded suction hole to enter the seed-discharge zone in advance. In this manner, miss-seeding compensation was achieved without adding an independent supplementary seeding channel. Bench tests were conducted at different operating speeds, and the integrated system performance was evaluated under the optimized parameter combination of the auxiliary seed-delivery mechanism. The results show that the missed-pickup detection accuracy and compensation success rate ranged from 96.49% to 100.00% and from 94.55% to 100.00% over the operating speed range of 6~12 km/h, respectively. With the proposed system, the qualified seed-spacing rate increased from 92.4% to 96.0%, the miss-seeding rate decreased from 6.4% to 3.0%, and the multiple-seeding rate decreased from 1.2% to 1.0%. These results indicate that the proposed system can identify missed seed pickup before seed discharge and achieve self-compensation through short-term acceleration of the seed-metering disc, thereby improving seeding continuity and seed-spacing uniformity.

AgriEngineering doi: 10.3390/agriengineering8080327

Authors: Panagiotis Dalias Anastasis Christou Christina Constantinou Kalia Kaikiti Damianos Neocleous

The sustainable management of biogas production digestate and lignocellulosic green waste remains a major challenge in circular bioeconomy systems. This study evaluated the feasibility and effectiveness of co-composting liquid digestate with woody pruning residues as an integrated strategy for nitrogen conservation and enhanced composting performance. Experiments were conducted at both laboratory and intermediate scales using shredded residues from several tree species soaked in digestate or nitrogen solutions prior to aerobic incubation or open-pile composting. Nitrogen mass balance analysis based on laboratory incubations demonstrated that plant residues, which retained substantial amounts of liquid (173% of dry weight on average), enabled negligible nitrogen losses, even under high ammonium loading conditions. Temporal monitoring of inorganic nitrogen forms indicated an initial phase of microbial immobilization followed by gradual remineralization, but also strong nitrogen retention within the lignocellulosic matrix. Intermediate-scale composting trials confirmed the operational feasibility of the approach. Digestate-amended piles rapidly entered the thermophilic phase, reaching 58.8 °C within five days, whereas control piles treated only with water remained slightly above ambient levels. The results suggested that co-composting of digestate with green waste improves moisture conditions, enhances decomposition of recalcitrant biomass, and mitigates ammonia-related nitrogen losses. The proposed soaking-based co-composting strategy represents a promising and scalable solution for sustainable management of both digestate and urban green waste streams.

AgriEngineering doi: 10.3390/agriengineering8080325

Authors: Hasan Mirzakhaninafchi Manjeet Singh Apoorv Prakash Santosh Kumar Aseem Verma Shikha Sharda Jugminder Kaur Parmar Raghuvirsinh Ali Pirhadi Tavandashti Ali Mirzakhani Nafchi Glen Rains Wesley Porter

Low-cost variable-rate applicators are needed to translate crop-sensing nitrogen (N) recommendations into field-scale fertilizer delivery. This study presents a field demonstration of a normalized difference vegetation index (NDVI)-guided stepwise variable-rate granular urea topdressing applicator for rice in a single-site, single-season comparison with a uniform split-management treatment. The tractor-mounted system used an active optical canopy sensor, a deterministic NDVI-to-rate rule, pulse-width-modulation actuator control, and fluted-roller metering. Fertilizer rates are reported as urea product, with elemental N equivalents calculated from 46% N content. The uniform split-management treatment prescribed three 75.00 kg urea ha−1 splits, totaling 225.00 kg urea ha−1 seasonally. The NDVI-guided split-management treatment prescribed two fixed manual splits of 56.25 kg urea ha−1 each and used a sensor-guided third split at 45 days after transplanting. Because the first two splits differed between treatments, the comparison represents two complete split-management strategies rather than the isolated effect of the sensor-guided third application. The aggregate field-collection estimate for the sensor-guided third split was 100.92 kg urea ha−1, which was 25.92 kg urea ha−1 greater than the uniform third split; the estimated seasonal total was 213.42 kg urea ha−1, or 5.1% less than the prescribed uniform seasonal total because of the two lower early manual splits. Harvest measurements adjusted to 14% grain moisture showed mean grain yields of 7.35 and 8.02 t ha−1 for the uniform and NDVI-guided split-management treatments, respectively. Treatment-level partial factor productivity, calculated from treatment-mean yield and estimated seasonal N input, was 71.0 and 81.7 kg grain kg−1 N, respectively. These results show the effectiveness of the applicator within the tested split-management comparison; however, the retained records do not verify rate-class frequency, spatial as-applied delivery, or complete strip coverage, and the yield and partial-factor-productivity differences should not be attributed solely to NDVI sensing or variable-rate actuation.

AgriEngineering doi: 10.3390/agriengineering8080326

Authors: Kornél Szalay Gábor Bércesi Szilvia Erdei-Gally

Kinetic shock triggers thigmomorphogenetic responses in plants, posing both an unintended handling risk and a deliberate technique to enhance ornamental value. Detecting its immediate, non-visual impacts remains a challenge. This study used non-destructive contact spectroscopy to detect short-term kinetic stress in three species with distinct leaf anatomies (Alocasia sp., Monstera deliciosa, Ficus elastica) under 20 s and 40 s stimuli. While a pooled global classification model failed due to anatomical variations masking the universal stress signal (70% accuracy), optimized species-specific models revealed distinct dynamics. At 0 min post-stress, high variance and low separability occurred across all species. However, a diagnostic change emerged within 30 min for Alocasia sp. (79.37%) and Ficus elastica (77.50%); the same tendency could not be confirmed for Monstera deliciosa, and further investigation is needed to support this pattern. Ficus elastica’s distinct architecture also proved consistently the least sensitive of three species in the pooled control-versus-treated comparison. A significant dosage effect was captured only in Alocasia sp. (87.50% accuracy). Spectral index analysis showed the dominance of the Normalized Difference Water Index (NDWI), suggesting the change reflects micro-structural leaf turgor modifications rather than biochemical changes. Results indicate that leaf spectroscopy has the potential to diagnose transport injury and monitor conditioning.

AgriEngineering doi: 10.3390/agriengineering8080324

Authors: Suphachai Phawiakkharakun Sunee Pongpinigpinyo

Non-destructive fruit quality assessment is essential for improving consistency, scalability, and objectivity of postharvest decision-making. This study presents a deep learning-assisted acoustic tapping system for classifying pineapple juiciness using recorded tapping signals. A total of 300 Pattavia pineapples were evaluated, producing 3600 audio recordings and 6840 curated tapping-sound samples evenly distributed across three juiciness classes. The proposed workflow integrates mobile-phone-based sound acquisition, tap-event segmentation, acoustic feature extraction, and supervised classification. Three audio representations Mel-Frequency Cepstral Coefficients (MFCCs), YAMNet embeddings, and VGGish embeddings were evaluated with deep learning, conventional machine learning, and ensemble models, including CNN, LSTM with attention, GRU with attention, hybrid CNN–LSTM–attention, random forest, logistic regression, gradient boosting, multilayer perceptron, voting, and stacking classifiers. The results showed that MFCC-based deep learning models provided the most reliable classification performance. The best-performing CNN achieved an accuracy of 0.9415, F1-score of 0.9411, Cohen’s kappa of 0.9123, and AUC of 0.9923. Additional analyses using confusion matrices, ROC curves, McNemar’s tests, bootstrap confidence intervals, and t-SNE visualization confirmed the robustness and discriminative capability of the proposed approach. These findings demonstrate that acoustic tapping combined with deep learning offers a practical, low-cost, and non-destructive method for pineapple juiciness classification, with potential application in postharvest sorting, quality control, and precision agriculture.

AgriEngineering doi: 10.3390/agriengineering8080323

Authors: Cen Liu Shuyao Shao Zongjie Cai Yue Cao Chengming Sun

The number of grains per spike is a critical determinant of wheat yield and plays an important role in phenotyping, breeding evaluation, and yield-related trait analysis. However, conventional grain counting methods rely on manual operation, which is time-consuming, labor-intensive, and prone to subjective bias, making them unsuitable for high-throughput applications. To address these limitations, this study proposes an improved wheat grain detection and counting method based on YOLOv8 integrated with a P2 detection layer and an Efficient Multi-Scale Attention (EMA) mechanism. The P2 detection layer enhances the resolution of shallow feature maps, improving the model’s ability to detect small and densely distributed grains. Meanwhile, the EMA module strengthens multi-scale feature representation and improves training stability and generalization performance, particularly in complex canopy and overlapping grain scenarios. Experimental results demonstrate that the proposed YOLOv8-EMA-P2 model achieves a precision of 96.10%, recall of 95.60%, mAP@0.5 of 96.80%, and mAP@0.5:0.95 of 68.40% on the test set, indicating strong detection performance. For counting performance, the model achieves a coefficient of determination (R2) of 0.8384, with a root mean square error (RMSE) of 1.8517, mean absolute error (MAE) of 1.2628, and mean relative error (MRE) of 5.33%. In addition, the average inference time per image is 6.5 ms, demonstrating its potential for real-time applications. Overall, the proposed method improves the accuracy, robustness, and efficiency of wheat grain detection and counting, providing an effective solution for automated wheat phenotyping and yield estimation.

AgriEngineering doi: 10.3390/agriengineering8080322

Authors: Denis V. Yanykin Mark O. Paskhin Sergey A. Shumeyko Dmitry A. Zakharov Grigorii A. Oloviannikov Nikita S. Parashchuk Denis N. Chausov Yurii Trutnev Sergey V. Gudkov Valeriy A. Kozlov

The effect of light spectrum conversion using photoconversion covers (PCCs) containing CdSe/CdS-ZnS quantum dots (PCC-QDs) on the growth and photosynthesis of tomato plants was studied. Luminophore-free covers and covers containing rhodamine B as a luminophore (PCC-RB) were used as reference covers. The obtained covers had a luminescence maximum in the red region (618 nm for PCC-QDs and 615 nm for PCC-RBs). It was shown that both types of PCC promoted plant growth, with PCC-QDs increasing leaf number by 28% and chlorophyll content by 6%, while PCC-RB enhanced stem length (17%) and leaf number (23%), with no effect on chlorophyll content after a reduction in the thermal dissipation of absorbed light energy and intensifying electron transfer in the photosynthetic electron transport chain, which was accompanied by a 40–50% increase in the hydrogen peroxide content in plant tissues and a 2–3-fold increase in ascorbate peroxidase activity. Light-induced activation of transpiration in PCC plants was found to occur within 1–2 min, while control plants exhibited greater stomatal inertia with a 4–5 min delay in response to light. It is assumed that changes in the light spectrum induced by PCCs led to the optimization of electron transport in chloroplasts, triggering H2O2-mediated activation of gas exchange in leaves.

AgriEngineering doi: 10.3390/agriengineering8080321

Authors: Mirna Castro-Bello Mario Alberto Duque-Peralta Cornelio Morales-Morales Lizbeth Gómez Muñoz Vitervo López Caballero Daniel Angeles-Herrera Sergio Ricardo Zagal-Barrera Diego Esteban Gutiérrez-Valencia

The effects of climate change have altered precipitation patterns, causing extreme weather events that impact crop yields and increase the demand for agricultural production, which drives the need to implement emerging technologies. This research presents the design, construction, and validation of a real-time monitoring system for environmental parameters in open-field avocado orchards. A four-phase methodology was employed: (1) Establishment of required parameters and construction of the IoT architecture; (2) Design of the geometry and final elaboration of the system: modeling in SolidWorks, circuit diagrams in Fritzing, and system assembly; (3) Development of a mobile application: Android Studio and development of the autoencoder model with machine learning; and (4) Validation: Evaluation of the wireless link; system implementation and deployment of the mobile application. The results obtained include a system with a transmitter node equipped with sensors and a receiver, both incorporating ESP32 and nRF24L01+PA+LNA modules, with a wireless transmission range of 2300 m. A total of 2016 data records were stored on microSD and in the cloud, which can be queried, visualized, and analyzed through the mobile application via Bluetooth or Wi-Fi; the system also detects outlier data that can be used for decision-making in the agricultural sector.

AgriEngineering doi: 10.3390/agriengineering8080320

Authors: Talha Batuhan Korkut Ahmed Rachid

Aquaponic systems couple fish and plant production in recirculating loops, yet quantitatively assessed dynamic models for engineering analysis, scale-up, and operation under realistic conditions remain limited. Here, a modular process-based MATLAB R2026a framework is developed for the recirculating aquaponic system operated at the ASTREDHOR facility (France). The model links hydraulic transport with fish metabolism, nitrification, solids removal, and plant nitrate uptake, using monitoring-derived boundary conditions for temperature, dissolved oxygen, pH, and electrical conductivity. The fish-growth component was calibrated and evaluated against archived, temporally reconstructed biomass trajectories derived from campaign-based biometrics in three production cycles with different fish compositions and environmental regimes. Tank-wise R2 values were 0.865–0.952 in the calibration windows and 0.700–0.921 in the fixed-parameter prediction windows, with prediction-period NRMSE values of 0.64–3.91%. These descriptive metrics quantify agreement on the reconstructed evaluation grid rather than performance over independently retained biometric sampling occasions. Complete corresponding time series were unavailable for TAN, NO2−, NO3−, total suspended solids, and plant uptake; these simulated outputs were therefore used only for mechanistic consistency assessment and exploratory scenario analysis, rather than independent validation. Local sensitivity analysis showed limited effects of temperature sensitivity (αT), optimal temperature (Topt), and minimum dissolved oxygen (DOmin) under observed conditions, whereas the feeding ratio (TR) and metabolic scaling exponent (n) strongly influenced simulated fish growth and nitrogen loading. Parametric sweeps provided preliminary, model-derived indications of feeding and biofilter-sizing limits under intensified loading; these thresholds require confirmation against independent water-quality measurements. The resulting framework is positioned as an off-line digital shadow with a fish-growth component assessed against reconstructed biomass trajectories and exploratory water-quality simulations.

AgriEngineering doi: 10.3390/agriengineering8080319

Authors: Xiao Han Huarui Wu Wang Guo

The mechanical characteristics of duckbill seeders, combined with complex environmental disturbances in the field, often lead to quality problems such as exposed seedlings and stem-buried seedlings when mechanically transplanted. These issues directly affect the subsequent yield and efficiency of automated harvesting. This paper is based on the YOLOv11 architecture and studies an automatic recognition model (Yolov11-ARM) for the transplanting quality of cabbage seedlings. The model applies a custom CPNMViTBv3 backbone that integrates Transformer-based global context modeling with efficient CNN feature reuse, replacing the standard C3f module to more effectively capture structural details in seedlings. The detection head is augmented with multi-scale feature aggregation and a lightweight SENetV2 attention mechanism, enhancing discriminative capability for small seedling instances. Additionally, a dynamically adaptive loss function for keypoint estimation is introduced, which adjusts according to object scale and error distribution, thereby ensuring training stability and robust localization performance. Experimental results show a high mean Average Precision (mAP@0.5) of 98.7% for seedling status (object) detection and 99.5% for keypoint localization with the studied model. This research provides an effective technical solution for the real-time, multi-parameter quality assessment of automated transplanting.

AgriEngineering doi: 10.3390/agriengineering8080318

Authors: Leiting Yi Lei Wang Zhi Pu Lei Luo Siyu Zhou Shipeng Wang

Accurate vineyard mapping is important for agricultural resource monitoring, land-use management, and inventory updating in arid regions. However, vineyard identification in Xinjiang, China, is challenged by fragmented parcels, exposed soil backgrounds, irrigation-driven heterogeneity, and strong accumulated-temperature gradients. This study developed a multi-source remote-sensing framework for vineyard mapping and inventory updating by integrating Sentinel-1/2 data, terrain variables, growing-degree-day-derived agrothermal zones (ATZs), and seasonal-difference features. RF, LightGBM, XGBoost, 1D-CNN, and a Stacking ensemble were evaluated using polygon-level in-distribution testing and Leave-One-ATZ-Out cross-zone validation. The in-distribution test was used to assess vineyard separability under similar sample distributions, whereas cross-ATZ validation was used to evaluate model transferability across heterogeneous thermal domains. Tree-based models and Stacking achieved near-ceiling performance under the in-distribution setting, but cross-ATZ validation revealed substantial performance degradation, indicating that conventional local validation can overestimate operational transferability. RF achieved the highest mean cross-zone F1-score, while Stacking achieved the highest cross-zone AP and Recall and provided a flexible probability surface for thresholding, mosaicking, vector post-processing, and patch-level mapping. In Gaochang District, the Stacking-derived result identified 22,998.84 ha of potential vineyard area, achieved an Area Recall of 0.7908 against the historical inventory, and covered 88.03% of existing parcels at ≥30% spatial overlap. The workflow also identified 1356 candidate vineyard patches for inventory updating covering 2502.32 ha for subsequent inventory verification. These results demonstrate that multi-source feature integration and probability-based ensemble mapping can support vineyard mapping and inventory updating in arid regions, while cross-zone validation is essential for assessing operational generalization.

AgriEngineering doi: 10.3390/agriengineering8080317

Authors: Jason Barnetson Hemant Raj Pandeya Grant Fraser

The operational satellite monitoring of pasture biomass requires models that transfer beyond the properties on which they were calibrated. We present a hierarchical, open-source cascade that scales in situ clip-and-weigh biomass (n = 1120 samples across eleven sites on five Queensland properties) through UAV digital-surface-model imagery to Sentinel-2 predictions, using TabPFN—a pre-trained transformer foundation model for small tabular data—as the regressor at all three nested spatial scales. Under a leave-one-site-out (LOSO) protocol on twenty site–date aggregates across nine sites, spectral-only Sentinel-2 models failed to transfer (best R2=−0.15, RMSE 4.62 t ha−1). Appending open climate (Open-Meteo ERA5) and topsoil (SoilGrids 2.0) covariates and evaluating five learners (GBM, RF, XGBoost, TabPFN, and GBM + TabPFN stack) on log-transformed biomass increased LOSO R2 to −0.05 and reduced RMSE to 4.43 t ha−1; a leaf-nitrogen growth trajectory predicted by the TabPFN nitrogen regressor from our earlier pasture-chemistry work reduced pixel-level LOSO RMSE by a further 4%. Three alternative covariate classes—BARRA-R2 reanalysis climate, three independent fractional-cover products, and Sentinel-1 C-band SAR backscatter—were tested and rejected, all hitting the same RMSE floor. The symmetric negative results indicate that the residual LOSO ceiling on the current nine-property footprint is a sample-size and optical-saturation limit rather than a feature-engineering one; the most tractable operational path forward is to stratify the production model by climatic zone and Queensland Land Type rather than pursuing further covariates within a single global learner.

AgriEngineering doi: 10.3390/agriengineering8080316

Authors: Javier Quille-Mamani José Huanuqueño-Murillo Lia Ramos-Fernández Luis Ángel Ruiz

Field-scale rice yield prediction in irrigated coastal systems is constrained by small plot size, persistent cloud cover, and strong interannual variability, which limit the effectiveness of single-sensor remote sensing approaches. This study proposes an integrated framework that combines multispectral time series from Sentinel-2, PlanetScope, and unmanned aerial vehicle (UAV) platforms with phenological metrics derived from the Normalized Difference Vegetation Index (NDVI), climate variables aggregated within phenology-defined windows, and machine learning algorithms to predict rice grain yield at the plot level. The framework was structured in five phases: (i) cross-sensor consistency assessment of red, near-infrared, and NDVI values across platform pairs; (ii) linear harmonization and multisource temporal fusion of NDVI time series at 5-day intervals; (iii) extraction of phenological metrics from smoothed NDVI trajectories using a relative-threshold approach; (iv) aggregation of meteorological variables within crop-stage-specific windows; and (v) yield prediction using partial least squares regression (PLSR), Random Forest, and XGBoost under nested leave-one-out cross-validation. The framework was evaluated on 72 irrigated rice plots (37 in 2022, 35 in 2023) in Lambayeque, northern Peru. Cross-sensor analysis revealed that the PlanetScope–UAV pair achieved the strongest NDVI agreement (R2=0.87, RMSE =0.07), while Sentinel-2–PlanetScope showed higher correlation (R2=0.91) but with systematic bias requiring calibration. Multi-source fusion raised temporal coverage from 53–62% (individual sensors) to 82% in 2022 and 66% in 2023. The best single-season prediction was obtained in 2022 with PlanetScope-derived phenological metrics and XGBoost (Rcv2=0.72, RMSEcv=1.23 t ha−1), while the best cross-season performance was achieved with combined phenological and climate features using the PlanetScope+UAV configuration and XGBoost (Rcv2=0.64, RMSEcv=1.35 t ha−1). SHAP-based interpretability analysis identified post-peak phenological descriptors and climatic conditions during the grain-filling window as the most informative predictors. These findings demonstrate that PlanetScope-centered multi-source fusion, combined with phenology-informed feature engineering, provides a robust basis for rice yield prediction in cloud-prone irrigated environments.

AgriEngineering doi: 10.3390/agriengineering8080315

Authors: Željko Barač Ivan Plaščak Tomislav Jurić Monika Marković

This manuscript investigates the influence of microclimatic factors on favourable working conditions for agricultural workers, with an emphasis on air temperature, humidity, and air velocity inside greenhouses at their workplaces. Analysis of the results of air temperature, humidity, and air velocity revealed that the internal conditions inside the greenhouse change throughout the day, which may have an impact on the thermal comfort of workers and their work efficiency. Also, analysis of air velocity indicates the need to optimise ventilation systems to improve working conditions. The data obtained through statistical processing can serve as a basis for further research and development of systems that would ensure more stable and favourable microclimatic conditions in protected areas of agricultural production.

AgriEngineering doi: 10.3390/agriengineering8080314

Authors: Murilo Battistuzzi Martins Daniela de Lourdes Sabino Aldir Carpes Marques Filho Arthur Gabriel Caldas Lopes Lucas Santos Santana

The rubber tree is the world’s main source of natural latex. However, the latex extraction and harvesting system requires a high level of human labor with agricultural tractors, which can expose workers to adverse conditions, such as noise. This study focused on evaluating the noise level emitted by an agricultural tractor during rubber harvesting at different operating points and working speeds. The experimental design adopted was completely randomized in a two-factor factorial arrangement with six replications. The treatments consisted of two tractor engine speeds (900 and 1500 rpm) and two workstation positions (worker inside the tractor and inside the transport trailer) during the latex coagulate collection. For the harvesting operations, a Massey Ferguson model 265 tractor coupled with a transport trailer noise exposure were used during the following operations: 1—distribution of boxes between the rows of rubber trees; 2—loading of the boxes with the collected coagulate; 3—transport of the boxes with latex coagulate to the storage location for commercialization; 4—unloading of the boxes at the storage location for commercialization. Noise assessments were performed at the workers’ ear level using a decibel meter according to technical standards. The workers’ positions and the tractor’s engine speed influenced the noise level during latex coagulate harvesting. The highest noise levels were observed when the agricultural tractor operated at a higher engine speed (1500 rpm), compared to 900 rpm engine speed. Regarding the workstation position, it was found that the noise level at the tractor operator’s station was higher than the noise level at the trailer workers’ station in all harvesting operations.

AgriEngineering doi: 10.3390/agriengineering8080313

Authors: Yinzhang Fang Yuanyuan Liu Jie Gu Weiye Feng Tian Zhang

Low vaccination efficiency and high labor demand constrain large-scale sheep farming. This study developed an automated vaccination robot integrating posture constraint, vision-based injection-site localization, and automated needle handling in a unified “constrain-localize-inject” workflow. The system comprises a hindquarter posture-correction frame, a bilateral neck-restraint mechanism with pressure feedback, and a vision-guided injection unit with automatic needle disposal. A Kendryte K210 edge-computing module enabled real-time localization of the cervical muscle region. Finite element analysis of the restraint column under a 100 N lateral load showed a maximum von Mises stress of 1.832 MPa, well below the 220.6 MPa yield strength of plain carbon steel, and a maximum displacement of 0.003 mm. Functional trials on 20 life-size artificial sheep models were completed without failure. A 500-cycle bench endurance test achieved a 95.8% overall success rate, although performance declined from 100% to 88% across successive test sets. The vision model achieved 95.86% precision, 92.67% recall, and 94.15% mAP@0.5, with a localization error of 5.6 ± 2.1 mm and an inference latency of 38.6 ms per frame. Laying the foundation for subsequent live animal experiments.

AgriEngineering doi: 10.3390/agriengineering8080312

Authors: Jobelle J. Capilitan Abigael L. Balbin Junrie B. Matias

Advances in artificial intelligence (AI) are revolutionizing agriculture through applications in crop monitoring, precision agriculture, automation, and environmental management. With the rapid development of AI in agriculture, there is an increasing need for extensive evaluation to identify emerging trends and potential future directions. This paper provides a descriptive bibliometric and thematic synthesis of 31,452 publications on the application of artificial intelligence in agriculture in Scopus from 2020 to 2025. The trends and structure of topics in the area are analyzed using BERTopic topic modeling alongside thematic synthesis, temporal trend analysis, centrality-density mapping, and evidence synthesis. Six higher-order themes were identified in the analysis, with crop and production intelligence being the most common. Despite the current progress, the topic of crop-related applications of computer vision remains dominant in the research landscape. However, applications in livestock, socio-technical systems, sustainability, and advanced distributed artificial intelligence fall at the periphery of the landscape. Temporal and structural analyses reveal a concentrated research landscape dominated by production-oriented applications, while interdisciplinary and emerging AI domains remain relatively fragmented. The paper also emphasizes the importance of combining transformer-based topic modeling with thematic and structural analysis in multidisciplinary fields. The results have revealed important insights into the direction AI technology is taking in the agricultural sector, underscoring the need for interoperable, explainable, sustainable, and farmer-centered systems for agricultural development.

AgriEngineering doi: 10.3390/agriengineering8080311

Authors: Chenyu Yu Lu Li Bolin Huang

Accurate early-stage monitoring of greenhouse tomatoes is essential for reducing post-harvest losses, with reliable detection of immature green tomatoes being the core challenge. However, these fruits are small, heavily occluded, and chromatically highly similar to foliage, making detection from low-altitude UAV imagery extremely difficult, while onboard edge processors impose stringent power and weight constraints. To address this, we propose YieldNet, a lightweight framework that introduces targeted enhancements to vanilla YOLOv8n: the backbone is replaced with ShuffleNetV2 to reduce computation; Efficient Channel Attention (ECA) modules are embedded after the P3–P5 layers in the neck for channel recalibration; and PIoU v2 loss is adopted for bounding-box regression. This study focuses on immature green tomatoes in low-altitude UAV imagery and evaluates the detector on an RK3588 edge device. The model is evaluated on both a self-collected real-world UAV dataset comprising 600 low-altitude green-tomato images and a public multi-ripeness benchmark. Compared with the YOLOv8n baseline, YieldNet achieves relative improvements in mAP@50-95, Recall, and F1-score by 18.9%, 6.1%, and 5.8%, respectively, on the Tomato-Recog public validation set, and enhances Recall, F1-score, and Precision by relative gains of 4.3%, 4.0%, and 3.8%, respectively, on the GreenTomato-UAV validation set, while increasing parameters only from 3.0 M to 3.3 M and reducing FLOPs from 8.1 G to 8.0 G. A representative live camera-to-display reading on the Orange Pi 5 Max was 37.6 FPS with 86 ms end-to-end latency. YieldNet supplies countable detections for future pre-harvest yield-estimation studies; UAV flight deployment, fruit-size estimation, and harvest-weight validation were not evaluated.

AgriEngineering doi: 10.3390/agriengineering8080309

Authors: Evangelos Alexandropoulos Vasileios Anestis Federico Dragoni Alexandros Mavrommatis Eleni Tsiplakou Nicholas John Hutchings Barbara Amon Thomas Bartzanas

To meet national and global air quality and climate ceilings, it is essential to provide farm-level decision support tools for mitigating gaseous emissions from agriculture. A major challenge is to develop reliable tools that can be adapted to country-specific conditions, particularly in countries where such tools are currently lacking, and support farmers in assessing emission mitigation measures. To address this challenge, a Prototype Decision Support Tool (PDST) for estimating and mitigating gaseous emissions at the livestock farm scale was developed based on the FarmAC whole-farm model. The PDST supports livestock farm-level assessment of carbon emissions, including CH4 and CO2, and nitrogen-related emissions, including N2O and NH3. Emissions were estimated using the IPCC 2006 Guidelines, their 2019 Refinement, and the EMEP/EEA 2023 methodology. The PDST was applied to two intensive pig farms in Greece, both with fully slatted housing and outdoor slurry tank storage, and two intensive dairy cattle farms, one in Greece and one in Poland, both using deep-litter housing with solid manure storage. For these farms, the PDST estimated total annual emissions of 1.58 and 1.54 kg CO2eq per kg of pig live weight and 0.80 and 0.66 kg CO2-eq per kg of raw milk, respectively. These estimates were consistent with values reported in the literature for comparable production systems and emission sources, supporting the preliminary consistency of the PDST outputs. The PDST can form the software basis to support stakeholders in choosing farm-level practices that specifically reduce emissions.

AgriEngineering doi: 10.3390/agriengineering8080310

Authors: Rodrigo S. Pessoa

Conventional nitrogen fertilizers are essential to food production but impose substantial energy, greenhouse-gas, and reactive-nitrogen losses. This review compares Haber–Bosch-derived urea, ammonium nitrate, calcium nitrate, green ammonia, and fertigation with plasma-activated water (PAW), in which non-thermal plasma fixes atmospheric nitrogen directly into water as NO3−/NO2− and, in some systems, NH4+. A PRISMA-adapted Scopus screening retrieved 765 records. Automated screening excluded 312 records; all 453 provisionally retained records were then manually audited, removing 88 additional false positives and yielding 365 plasma nitrogen-fixation studies, including 157 PAW/plasma-in-liquid records. The comparison uses explicit system boundaries for energy, carbon intensity, nitrogen-use efficiency, and technology readiness. The lowest verified directly measured in-water system reports 1.14 MJ mol−1 N for total soluble nitrogen, whereas lower values near 0.4–0.5 MJ mol−1 N refer mainly to gas-phase or modeled plasma fixation and are not directly interchangeable with PAW. Controlled-environment studies report improved germination or vegetative growth in several crops and, in one full-cycle controlled horticultural study with a nitrate-equivalent control, fruit performance comparable with conventional nitrate fertilization. Nevertheless, PAW is not a general replacement for synthetic fertilizer. Its most credible near-term role is as a decentralized complement in fertigation, protected cultivation, hydroponics, and remote or supply-constrained systems supplied by low-carbon electricity. Major barriers are dilute and variable nitrogen concentration, reactor durability, storage stability, incomplete techno-economic accounting, and the absence of replicated multi-season field validation. Minimum reporting requirements and research priorities are proposed.

AgriEngineering doi: 10.3390/agriengineering8080308

Authors: Rodrigues Pascoal Castro Luís Carlos Carvalho Pires Pedro Dinho da Silva

In Mediterranean countries such as Portugal, summer heatwaves increasingly threaten agricultural productivity by disrupting crop physiological processes. Greenhouse cultivation often exacerbates heat stress, while conventional cooling systems such as air conditioning and evaporative cooling impose unsustainable energy demands. This study investigates an Earth-to-Air Heat Exchanger (EAHE) system consisting of a five-tier helical PVC pipe configuration (29 m, buried at a depth of 3 m), installed in a prototype polycarbonate greenhouse in Covilhã, Portugal, and monitored under real summer conditions. Four ventilation scenarios were simulated in EnergyPlus 25.1, and a segmented NTU thermal model, implemented as a Python plugin via the pyenergyplus API, predicted the EAHE outlet temperature with CV(RMSE) values of 1.47% at 30 m3/h and 3.0% at 50 m3/h. The IPMA meteorological dataset provided the best simulation accuracy (RMSE = 2.31 °C, R2 = 0.978). In simulations based on the experimentally calibrated models, EAHE preconditioning reduced accumulated heat stress degree-hours above 28 °C by 9.1 to 9.5% and lowered peak indoor temperature by up to 2.60 °C, at system COPs of 6.9 to 10.6, which are 2.3 to 3.5 times higher than conventional vapour-compression cooling; propagated measurement uncertainties confirm the robustness of this COP advantage. A model-based parametric scale analysis indicated that geometrically scaled circuits (DN200, DN400) achieve degree-hour reductions of 67 and 91%, supporting EAHE scalability through geometric proportioning, pending experimental validation at larger scales.

AgriEngineering doi: 10.3390/agriengineering8080307

Authors: Oluwatobi Solomon Olaleye Oluwaseun Temitope Faloye Oluwafemi E. Adeyeri Olayiwola Akin Akintola Akinwale Temitope Ogunrinde Bolaji Adelanke Adabembe Toju Esther Babalola John Omodara Akinremi

Drought persistence in the Sudan–Sahel transition zone of Northern Nigeria poses a substantial risk to agricultural productivity. This study develops a Hybrid Prophet–Long Short-Term Memory (LSTM) architecture to address the existing research gap in near-term predictive capacity for non-stationary hydroclimatic time series. Utilizing Kano State as a case study, the Prophet algorithm was employed to extract deterministic trends from the Standardized Precipitation Evapotranspiration Index (SPEI-12) derived from CRU TS v4.09 data (1980–2024), while an integrated LSTM network modeled the stochastic residuals. Diagnostic results indicate a statistically significant trend toward moisture recovery (p < 0.0001). Comparative analysis demonstrated that the hybrid model significantly outperformed standalone baselines, achieving a Nash–Sutcliffe Efficiency (NSE) exceeding 0.87 and a 67.2% reduction in Root Mean Square Error (RMSE). Furthermore, the framework accurately simulated hydroclimatic transitions with a directional accuracy exceeding 87%, confirming high predictive reliability. Projections for the 2025–2030 period indicate a continued positive moisture shift of approximately 0.9 SPEI units. These findings underscore the technical necessity of decoupling non-linear noise from deterministic signals to resolve complex drought dynamics. Consequently, the proposed framework serves as a robust tool for near-term climate prediction. Scaling this methodology across diverse agroecological zones is recommended to enhance national drought early warning systems and regional climate resilience strategies.

AgriEngineering doi: 10.3390/agriengineering8080306

Authors: Israel Viveros Torres Erica María Lara Muñoz Rogelio Reyna Vargas

The sorting of Persian lime Citrus × latifolia (Yu.Tanaka) Tanaka intended for export is still performed manually in many production units, introducing variability and low repeatability. Deep learning-based vision systems offer high accuracy but at a high cost, and with decisions that are difficult to trace against a quality standard. A system was developed that integrates classical computer vision (grayscale conversion, Gaussian filtering, thresholding and edge detection) with three supervised symbolic classifiers (PRISM, ID3 and Naive Bayes) under a hierarchical decision scheme, validated against the criteria of the Mexican Standard NMX-FF-077-1996-SCFI. The models were evaluated using a set of 7017 images, with a 265-image test subset, and the physical prototype was validated with 200 fruits. The system reached an accuracy of 95.1% on the test set and 95.5% during physical operation, with an F1 score of 0.97 for the export-grade class; only 2 of 265 and 1 of 200 non-conforming fruits were wrongly admitted. The cost of the deployed prototype remained at 407 USD. Integrating classical vision with interpretable symbolic rules constitutes an accessible and auditable solution for Persian lime quality control in accordance with the standard, with reproducible performance between algorithmic evaluation and physical operation.

AgriEngineering doi: 10.3390/agriengineering8080305

Authors: Matheus Luís Caron Carlos Augusto Alves Cardoso Silva Rodnei Rizzo Matheus Sterzo Nilsson Ana Karla da Silva Oliveira Marta Laura de Souza Alexandre Peterson Ricardo Fiorio

Pastures are the main feed source for beef cattle production, a sector in which Brazil plays a prominent global role. This study aimed to develop predictive models for dry matter yield (DM yield, kg ha−1), crude protein (CP), and neutral detergent fiber (NDF) in Urochloa brizantha tropical pastures using laboratory VIS-NIR-SWIR spectroscopy, and to identify spectral patterns associated with these variables. Samples were collected from a commercial pasture area of approximately 200 ha, subdivided into 19 paddocks cultivated with Urochloa brizantha cv. Marandu and managed under rotational grazing during 2023. Forage samples were oven-dried, ground, and spectrally measured using a FieldSpec spectroradiometer (350–2500 nm). Partial least squares regression (PLSR) models were calibrated and evaluated using cross-validation, and informative wavelengths were identified using Variable Importance in Projection (VIP) scores. DM variability was mainly associated with near-infrared regions, CP with visible and near-infrared regions, and NDF with the visible region. Models calibrated with VIP-selected wavelengths achieved acceptable performance for CP (R2CV = 0.74) and NDF (R2CV = 0.72), whereas the general full-spectrum models showed moderate performance for CP (R2CV = 0.57) and acceptable performance for NDF (R2CV = 0.75). Temporal transferability varied among sampling periods, with greater robustness for CP and NDF than for DM. Overall, DM prediction remained limited and showed poor temporal transferability.

AgriEngineering doi: 10.3390/agriengineering8080304

Authors: Naresh Sihag Ganesh Upadhyay Bharat Patel Swapnil Choudhary Vijaya Rani Arun Kumar Attkan

This study addresses the inefficiencies and environmental concerns associated with conventional broadcast spraying in agriculture, where uniform chemical application leads to significant off-target losses and excessive agrochemical usage. A plant detection-based nozzle actuation system was developed to enable real-time, selective spraying based on canopy presence. The system integrates a LiDAR sensor for precise canopy detection, an ESP32 microcontroller for signal processing, and a solenoid valve-controlled nozzle for automated on/off spray regulation. Laboratory experiments were conducted using a controlled conveyor-based setup to simulate field conditions and evaluate the effects of forward speed, sensor–nozzle distance, and sensor–canopy distance on spray deposition. Spray performance was assessed using water-sensitive papers and image analysis techniques, while statistical analysis (ANOVA) and optimization using Response Surface Methodology (RSM) were performed. The results indicated that forward speed and sensor–nozzle distance significantly influenced spray coverage, whereas sensor–canopy distance had no significant effect. The optimized parameters (3.0 km h−1 speed, 35 cm sensor–nozzle distance, and 70 cm sensor–canopy distance) achieved effective canopy coverage (~52–55%) while substantially reducing off-target deposition. Compared to continuous spraying, the developed system maintained comparable target coverage while significantly minimizing chemical losses. The findings demonstrate the potential of sensor-based precision spraying for improving input efficiency and environmental sustainability.

AgriEngineering doi: 10.3390/agriengineering8080303

Authors: Ruth M. Córdoba-Ortega Lidia M. Ortega-Alvarado Juan José Cubillas-Mercado M. Isabel Ramos-Galán

Rural digitization in the management of woody crops like olive orchards lags significantly behind the technological benchmarks established in other highly digitized sectors. We propose a modular Digital Twin (DT) framework that implements a multi-scale data fusion strategy integrating satellite, UAV-based hyperspectral/LiDAR sensing, and IoT devices. This study addresses a primary technological bottleneck: the high computational cost and disk I/O latency inherent in the real-time fusion and analysis of high-dimensional spectral data and dense 3D geometries. Conventional geospatial software often fails to provide real-time interactivity for these massive datasets due to frequent memory swapping. We designed the system’s core as a high-performance C++17engine (GEU), which leverages innovative data structures (Meanlets and Meshlets) to enable real-time 3D interaction and spectral “picking” directly in main memory. Experimental validation demonstrates that this approach eliminates I/O bottlenecks, providing instantaneous feedback on datasets exceeding typical memory limits. Additional modules round out the DT’s functionality. Furthermore, the integrated AI module achieves a strategic 8-month lead time for early crop yield estimation with absolute errors below 20%. The model also demonstrates high precision, reaching overall accuracies of 90.19% for Arbequina and 88.04% for Picual cultivars using 1D CNNs. Finally, ubiquitous mobile applications empower farmers as “human actuators” in the cyber–physical cycle. This framework establishes a replicable methodology for the digital transformation of the Mediterranean agricultural sector.

AgriEngineering doi: 10.3390/agriengineering8080302

Authors: Saba Rabab Luke Barrett Chathurika Amarathunga Melanie Bullock Rebecca Maher Deven Bhasin Susan Sprague

Accurate assessment of the presence and severity of plant diseases is essential for effective crop monitoring and management. This study presents a deep learning-based pipeline for quantifying blackleg crown canker disease severity in canola stems by combining image segmentation and severity prediction tasks. Three architectures (ResUNet, UNet and SegFormer) were compared for the first step of stem segmentation to isolate relevant regions. The disease severity scores of four experts, and their aggregated median, were used to train models which were evaluated for label consistency, ambiguity, and model robustness. Among the three segmentation architectures, SegFormer achieved the best performance (mean IoU = 0.939, F1 score = 0.962), outperforming ResUNet and UNet. There was a high correlation in disease severity scores across expert labels, with the median-trained model achieving correlation coefficients of 0.924–0.963 against individual expert assessors on the evaluation dataset. Confusion matrix analysis further demonstrated reliable classification across severity levels. This work highlights the influence of segmentation quality, label aggregation strategies and data imbalances on downstream prediction tasks. This study uses controlled imaging conditions, but the proposed framework provides a strong foundation for future application in field environments. The framework enhances model interpretability by generating severity scores that align closely with expert assessments to support users such as agronomists and plant breeders for better decision-making and help track disease resistance by providing consistent, objective disease measurements over time. Future work will focus on exploring multi-task learning for greater efficiency, alongside validating the approach in field conditions to enable broader adoption.

AgriEngineering doi: 10.3390/agriengineering8080301

Authors: Jesus A. Baro Jose A. Bodero Victor Romero

Precision livestock farming (PLF) is hindered by high costs, infrastructure demands, and complex deployment. To address these barriers, we developed CABRA, an open-hardware wearable system for real-time behaviour monitoring in pasture-based livestock. The collar-mounted device integrates a 6-axis IMU, a GPS, and a low-power ESP32 microcontroller within a modular architecture, using the routerless ESP-NOW protocol to transmit data directly to a base station—eliminating reliance on network infrastructure or cloud connectivity. The system supports both synchronised data logging for video annotation and real-time embedded behaviour classification via an optimized decision-tree pipeline deployed directly on the microcontroller. Field trials with dairy goats confirmed robust hardware performance, minimal animal disturbance, and reliable communication over 100 m. A two-stage evaluation revealed that while the extracted IMU features are highly discriminative (achieving F1 > 0.99 under window-level validation), cross-animal generalization remains challenging (macro F1 = 0.31 under rigorous animal-level partitioning), primarily due to the “sensor placement effect” and domain shift between individuals. These results honestly quantify the current limitations of uncalibrated wearable livestock sensing while validating the functional feasibility of edge-based inference. All design assets—CAD files, schematics, firmware, and data pipelines—are openly released to ensure full reproducibility and community-driven adaptation for diverse PLF applications.

AgriEngineering doi: 10.3390/agriengineering8080300

Authors: Dmitry Khudyakov Andrey Sherstyukov Ivan Shorstkii

This study investigated the influence of filamentary cold plasma (FCP) pretreatment on the isolation kinetics, yield and quality of lavender essential oil obtained by hydrodistillation (HD) and steam distillation (SD) at pilot scale. Lavender flowers were processed with or without FCP treatment under identical HD and SD conditions. FCP promoted tissue electroporation and the formation of additional mass transfer pathways in the calyx, shortening isolation time and increasing essential-oil yield from 2.46% to 2.65% for HD and from 2.33% to 2.65% for SD. In the SD process, FCP reduced process time from 240 ± 5.8 min to 95.3 ± 6.1 min to obtain an equal oil yield volume and decreased the specific energy consumption from 0.35 to 0.14 kWh mL−1 of essential oil. GC–MS analysis confirmed that FCP did not deteriorate the volatile profile; linalool and linalyl acetate were the dominant constituents, with the relative contribution of other compounds depending on the distillation method. The combined linalool and linalyl acetate fraction increased from 65.9% to 68.2% after HD and from 63.49% to 68.11% after SD, indicating partial conversion of bound essential oil into a more extractable form. These results demonstrate that conveyor-based FCP pretreatment can intensify lavender oil production while preserving oil quality and provide practical parameters for assessing industrial-scale implementation.

AgriEngineering doi: 10.3390/agriengineering8070299

Authors: Alexey Derepaskin Yurij Polichshuk Yurij Binyukov Artem Komarov Anton Kuvaev Nikolay Laptev

The article presents the results of research on the justification of the working unit parameters and the structural scheme of the chisel for loosening stubble fields. It was established that, according to the criterion of minimum draft resistance, the optimal installation angles are 20–24 degrees to the bottom of the furrow at a working share width of 40–50 mm. The calculated values of the share installation angles were verified on a laboratory setup, and the results confirmed the theoretical calculations. Based on the quality of loosening of the worked layer and the preservation of stubble, the shank width should be 20–30 mm, the share width 40–50 mm, and the share installation angle 20–24 degrees. The shank should be equipped with a safety mechanism to prevent breaking or bending. The results of research on different leveling device options are presented, and the structural–technological scheme, main parameters, and operating modes of the tool for chiseling compacted stubble fields are substantiated. Dependencies of the treatment quality on the parameters of the loosening working units, the leveling device, and the operating speed of the implement were obtained. Based on the justified parameters, an experimental prototype of the soil chiseling machine was manufactured and field tests were conducted under production conditions on stubble fields.

AgriEngineering doi: 10.3390/agriengineering8070298

Authors: Shuyuan Chen Jiajun Liu Shuai Cui Wangwang Shi Zedong Wu

Remote sensing has become an important tool for crop monitoring and precision agriculture, yet its applications in sugar beet production remain fragmented across sensing platforms, target traits and modelling strategies. This review synthesises the development, current applications and future directions of remote sensing in sugar beet production, with particular attention to the transition from crop monitoring to precision management. A structured search was conducted in Scopus and the Web of Science Core Collection for publications from 2003 to 2025, and 181 relevant peer-reviewed articles were retained for thematic analysis. The literature shows a clear increase in sugar beet remote sensing studies, particularly after 2015, coinciding with the availability of Sentinel-2 imagery and, from 2016 onwards, the growing use of unmanned aerial vehicle-based sensing. It also indicates a gradual shift from crop mapping and canopy monitoring towards disease detection, weed mapping, yield prediction and management-oriented applications. Current studies demonstrate the value of satellite, unmanned aerial vehicle and proximal sensing for retrieving canopy traits, assessing biotic stresses, estimating root yield and supporting field-scale management. However, sugar beet presents specific challenges because its economic value depends not only on canopy development or root biomass, but also on sucrose concentration, recoverable sugar yield, and processing quality. These quality-related traits remain less studied and are difficult to infer directly from canopy observations. Modelling approaches have evolved from vegetation-index-based empirical models towards machine learning, deep learning, multi-temporal analysis, data fusion and crop model assimilation, but issues of model transferability, ground-truth availability and operational decision support remain unresolved. Future research should strengthen multi-source observations, external validation, quality-oriented prediction and decision-support workflows to promote robust, scalable and economically meaningful remote sensing applications in sugar beet production.

AgriEngineering doi: 10.3390/agriengineering8070297

Authors: Bo Sun Xinwu Du Hua Yu Hua Zhan Bin Shi

Wear prediction for agricultural soil-engaging components is computationally demanding when discrete element method (DEM) simulations are repeatedly used for design evaluation and operating-parameter screening. In this study, an Archard-inspired Gaussian process regression (GPR) residual-learning surrogate was developed for rapid prediction of the total wear volume calculated by EDEM for a ploughshare. The physical prior was a monotonic operational-parameter proxy motivated by the load and sliding trends in Archard theory, which did not directly use DEM-derived normal force, sliding distance, or frictional work. A soil–ploughshare interaction model was used to generate 100 full-factorial samples with tillage depth, tillage speed, and penetration angle as inputs. The Archard-inspired prior, cubic polynomial Ridge regression, standard GPR, and prior-guided residual GPR were evaluated by cross-validation, repeated random splits, and boundary-level extrapolation tests. Across 30 repeated 90%/10% splits, standard and Archard-inspired GPR achieved mean R2 values of 0.9927 ± 0.0014 and 0.9908 ± 0.0021, respectively. In the 200 mm tillage-depth extrapolation test, the latter performed best, with R2 = 0.9752, RMSE = 0.000252 mm3, and MAPE = 2.68%; however, the former was more accurate in the tillage-speed and penetration-angle extrapolation tests, and the 48% interval coverage of the prior-guided model in the penetration-angle test indicated overconfidence when the prior was biassed. These results show a conditional, rather than universal, benefit of the Archard-inspired prior: it improved extrapolation plausibility for the load-dominated tillage-depth case but did not improve all boundary predictions. The surrogate predicts EDEM-simulated wear, and its engineering validity depends on DEM calibration, the selected wear coefficient, and future soil-bin or field validation.