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.

AgriEngineering doi: 10.3390/agriengineering8070296

Authors: Hadee Madadum Fazal E. Nasir Kanjana Haruehansapong

The precision of oil palm fresh fruit bunch (FFB) ripeness classification directly determines the extraction efficiency and chemical quality of the resulting crude palm oil (CPO). Traditional manual inspection at reception ramps remains labour-intensive, time-consuming, and subjective. To address these issues, this study presents a lightweight deep learning framework for automated oil palm FFB ripeness classification trained on a field-collected dataset of 857 images covering four ripeness classes (Over-ripe, Ripe, Under-ripe, and Unripe). Six lightweight classification backbones are evaluated, including MobileNetV2, EfficientNetV2B0/B1, and YOLO variants. An intra-class half-mixing augmentation with geometric transformation is proposed to address minority-class imbalance. Overall, YOLOv8n-cls achieved the highest accuracy (95.6%), followed by EfficientNetV2B0/B1, YOLO11n-cls, YOLO26n-cls, and MobileNetV2, respectively. In addition, all YOLO-family models achieved a recall score of 1.00 for the minority class while obtaining the highest F1 scores for the other classes. The experimental results suggest that the proposed augmentation method enables lightweight deep learning models to achieve promising classification performance on an imbalanced field-collected FFB dataset while improving minority-class detection.

AgriEngineering doi: 10.3390/agriengineering8070295

Authors: Seonghun Lee Wonjin Jung Subin Cho Myeong-Kwan Kim Donghee Park Wongwang Choi Ho-Hyun Park Jaehwa Park

Modern dairy farms continuously record individual cow milk yields, yet turning these records into on-demand forecasts requires a multi-step data-to-model pipeline that natural language alone cannot drive. We study large language model (LLM) orchestration over a verified forecasting pipeline, asking not whether it predicts yield but how faithfully it converts requests into correct tool-execution conditions. Against a deterministic Oracle pipeline, we compare a single-LLM function-calling framework and a multi-agent framework that distributes the same workflow across specialized agents. Using Gemini 2.5 Flash, both answer 1085 queries (4340 forecast points) varying in phrasing, horizon, and missingness, drawn from 13 months of commercial-farm records. On execution-matched points (those reproducing the Oracle’s cow, dates, input window, and horizon), the LLM output is statistically equivalent to the Oracle (paired TOST within ±0.5 kg, pTOST<0.001), so agreement is largely fixed by the deterministic tools. This equivalence is therefore confined to execution-matched points and does not describe overall system reliability. The frameworks differ instead in fidelity: the single-LLM framework reaches only 94.31% completion (zero-filled RMSE 9.70 kg), whereas the multi-agent framework reaches 99.88% at 35% added runtime; the single-LLM losses reflect systematic date-anchoring errors and silent false-accepts, not random noise. In this case study, LLM frameworks can orchestrate verified forecasting tools, and separating responsibilities improves workflow fidelity more than accuracy.

AgriEngineering doi: 10.3390/agriengineering8070294

Authors: Jian Zheng Yong Chen Junfan Jin Shengxiong Huang Xiangxing Zhou Wentao Huang

Accurate, high-throughput quantification of rice panicles is important for yield estimation and breeding-oriented rice phenotyping. However, unmanned aerial vehicle (UAV)-based panicle detection remains challenging because flight-altitude variation produces large target-scale changes. Flooded paddy backgrounds, leaf occlusion, and illumination fluctuations further obscure small panicle targets. To address these challenges, we constructed a composite multi-altitude dataset covering UAV imagery acquired from 3 to 20 m under varying field conditions. We then propose Panicle-DETR, a lightweight detection and counting framework based on a frequency-aware Cross Stage Partial (CSP) backbone. Rather than treating the Fast Fourier Transform (FFT) as a filter by itself, the proposed FasterFD module uses frequency-domain representations with learnable frequency-response reweighting to enhance panicle-related texture cues and reduce redundant background responses. A Lossless Feature Encoder is designed to preserve fine spatial information for small targets across altitude-induced scale changes, while a composite metric loss based on Normalized Gaussian Wasserstein Distance (NWD) and Inner-IoU improves localization for adherent and overlapping panicle clusters. On the composite dataset, Panicle-DETR achieved a Precision of 90.97%, a Mean Absolute Error of 4.28, and an R2 of 0.957 for single-frame panicle counting. With 13.78 M parameters and 53.0 GFLOPs, the framework achieved 16.9 FPS with 1.96 GB peak GPU memory in a battery-powered notebook benchmark, supporting its potential for resource-constrained field-side UAV image analysis.

AgriEngineering doi: 10.3390/agriengineering8070293

Authors: Emilio Suarez Hayley Milner Juan Carlos Diaz Perez Kate Cassity-Duffey Henry Y. Sintim Theodore McAvoy

Biochar and poultry litter have been proposed as soil amendments to improve soil health in coarse-textured agricultural soils, yet their field performance under southeastern U.S. conditions remains inconclusive. This two-year field study evaluated five biochar application rates (0–44.8 Mg ha−1) combined with inorganic fertilizer or poultry litter on selected soil health indicators in a sandy loam Ultisol under sweet corn production in the Georgia Coastal Plain. Treatments were arranged in a randomized complete block design with four replications and analyzed using linear mixed-effects models. Biochar application did not significantly affect aggregate stability, pH, cation exchange capacity, soluble salts, organic matter, active carbon, or estimated nitrogen mineralization, with only a marginal three-way interaction observed for microbial respiration. Poultry litter significantly increased microbial respiration relative to inorganic fertilizer, whereas responses for the remaining soil health indicators were broadly similar between fertilizer sources. Year was the dominant source of variation, with extreme rainfall in 2024 reducing aggregate stability, soluble salts, microbial respiration, and nitrogen mineralization while increasing organic matter and active carbon. These findings indicate that short-term soil health responses were driven primarily by environmental conditions rather than management practices. Under the conditions of this study, either fertilizer source can be used successfully, whereas longer-term studies are needed to determine whether biochar aging enhances soil function in sandy loam Ultisols.

AgriEngineering doi: 10.3390/agriengineering8070292

Authors: Jakhfer Alikhanov Aidar Moldazhanov Akmaral Kulmakhambetova Dmitriy Zinchenko Alisher Nurtuleuov Dauren Sembay Tsvetelina Georgieva Eleonora Nedelcheva Plamen Daskalov

Sorting apples by product category is a key stage of harvest preparation, especially for small and medium-sized farms, where traditional manual sorting leads to high labor intensity. This article presents the development and experimental study of a compact apple-sorting machine based on computer vision, color assessment, and indirect fruit-weight estimation. The designed machine was adapted for Kazakhstani apple varieties and integrates low-cost parts: a fruit feeding and positioning module, a computer vision system with an image processing unit, PLC-based control, and a sorting actuator. The decision-rule procedure uses color and visual geometric parameters of images, and classification by regression analysis. Statistical analysis identified projected fruit area as the primary predictor for indirect weight estimation using the proposed regression model. The model robustness and classification accuracy were assessed by confusion matrices and main validation metrics. The influence of conveyor speed on the stability of sorting was observed. The experimental results indicated that the optimal operating mode for the machine is an apple transport speed of 0.16 m/s, where sorting throughput is approximately 400 kg/hour, with an average accuracy of 92%. The proposed machine provides sufficient real-time sorting accuracy with a simple, cost-effective machine design and can be used as a useful sorting solution in small and medium-sized farms.

AgriEngineering doi: 10.3390/agriengineering8070291

Authors: Aldir Carpes Marques Filho Weverton Caetano Nunes Carlos Eduardo Silva Volpato Murilo Battistuzzi Martins Jordan Alexis Castillo Coronado Lucas Santos Santana Josiane Maria da Silva Vanessa Ribeiro Joaquim Tenório Neto

The common bean (Phaseolus vulgaris L.) is an important food crop in tropical agriculture. However, fertilization and soil management methods for common beans require further investigation to reduce production costs and increase sustainability. Furthermore, cultivation methods can directly affect GHG emissions. Thus, this study evaluates the CO2 emissions from fuel consumption as a function of soil management and fertilization methods on the common bean crop. The randomized block design was used in a 2 × 3 factorial scheme with six repetitions composed of two fertilization systems (spread and furrow) and three soil management systems: convention-al tillage—CT, minimum tillage—MT, and no-tillage—NT. The productive performance of common beans varies according to fertilization methods and soil management. Field capacity in the (CT) was impaired due to the various mechanized soil preparation operations with 0.30 and 0.32 ha h−1, without a significant effect from the fertilization method. CT system resulted in higher CO2 emissions of 175.74 kg ha−1 and 165.50 kg ha−1; thus, in soil conservation management, these same values were up to 58% lower, with the lowest rates for NT. Crop yield in the MT system presented the best result compared to the CT and NT, with an appropriate cost–benefit ratio for bean production in tropical crops.

AgriEngineering doi: 10.3390/agriengineering8070289

Authors: María I. Arrieta-Escobar Carlos D. Paternina-Arboleda Jorge I. Vélez Guisselle A. García-Llinás

Accurate short-term forecasts of banana productivity can improve harvest scheduling, packing-capacity allocation, and export logistics, yet commercial forecasts often deviate substantially from realized yields. Objective: We evaluated whether tuned ensemble machine learning could reduce that error for seven-week-ahead forecasting of weekly productivity (boxes/Ha). Methods: Using weekly records from nine commercial farms in northern Colombia (2007–2018), we benchmarked six tuned ensembles—Random Forest, Extra Trees, Gradient Boosting, LightGBM, XGBoost, and CatBoost—against a company forecast and three statistical baselines (Seasonal Naive, Historical Mean, and a per-farm ARIMA). Hyperparameters were tuned by time-series cross-validation on 2007–2017, and models assessed on a 2018 holdout (405 observations). Results: CatBoost obtained the lowest errors (MAE = 3.95, R2 = 0.61), a 57.0% MAE and 81.2% MSE reduction versus the company baseline (MAE = 9.18, R2 = −1.06). Bootstrap 95% CIs and the Diebold–Mariano test showed CatBoost, XGBoost, LightGBM, Gradient Boosting, Random Forest, and the Historical Mean to be statistically equivalent (CatBoost’s lead was not significant), all outperforming the ARIMA (MAE = 5.64), Seasonal Naive (MAE = 7.31), and company baselines; an ablation confirmed no data leakage. Conclusions: Tuned ensembles can substantially improve short-term harvest planning in commercial banana production, with model choice guided by operational and computational constraints.

AgriEngineering doi: 10.3390/agriengineering8070290

Authors: Luiz Carlos Brandão Carla Simone Araújo Gomes Sarmento Odair Lacerda Lemos Ednilton Tavares de Andrade Ricardo Rodrigues Magalhães

The prediction of specialty coffee quality remains a central challenge for value addition in the agricultural sector. This study presents a computational approach to model the complex sensory quality of Arabica coffee (Coffea arabica L.) using synthetic data grounded in real-world statistics. A synthetic dataset (identical to the real dataset with a number of samples = 207) was generated using Cholesky decomposition based on the descriptive statistics and Pearson correlation matrix extracted from the Coffee Quality Institute (CQI) Arabica 2023 database, comprising 17 numerical variables including sensory attributes, defect counts, and Total Cup Points. The synthetic dataset achieved a Kolmogorov–Smirnov similarity of 89.55% with the real data, with the target variable Total Cup Points preserved with high fidelity (real mean: 83.71; synthetic mean: 83.65). An ordinal classification model (Random Forest) trained exclusively on the synthetic data and validated against real-world samples achieved an overall accuracy of 87.92% and a Quadratic Weighted Kappa (QWK) of 0.9502, indicating excellent agreement and confirming the model’s ability to capture the ordinal hierarchy of coffee quality. SHAP (SHapley Additive exPlanations) analysis revealed consistent feature importance rankings between real and synthetic domains, with Aftertaste, Overall, and Flavor emerging as the top three most influential predictors. This study validates the use of statistically grounded synthetic data for training robust machine learning models in agricultural research, demonstrating that synthetic environments can effectively replicate empirical patterns and enable cross-domain generalizability. The complete code and datasets are publicly available for reproducibility.

AgriEngineering doi: 10.3390/agriengineering8070288

Authors: Jamile Raquel Regazzo Lilian Elgalise Techio Pereira Adriano Rogério Bruno Tech Cíntia Cristina Soares Bianca Martins Tintim Murilo Mesquita Baesso

The development of reliable computer vision systems for poultry slaughterhouses requires robust segmentation methods capable of handling challenging industrial conditions. This study evaluated the structural behavior, computational performance, and consistency of three segmentation pipelines for broiler chicken carcass inspection: (i) an edge-based approach using Canny detection and morphological operations, (ii) a threshold-based method using global binarization and contour extraction, and (iii) a deep learning-based approach for automatic background removal implemented with the rembg library. A dataset of 587 RGB images acquired in commercial slaughterhouses was analyzed. Segmentation performance was assessed using Intersection over Union, Dice coefficient, Overlap Error, Structural Similarity Index, Edge Preservation Index, and Fisher Discriminant Ratio, complemented by qualitative analyses of discordance maps and Sobel edge visualizations. Results showed that overlap-based metrics alone were insufficient, as high IoU and Dice values often concealed important boundary differences. Classical methods exhibited lower computational cost and processing times compatible with real-time applications but presented limitations in contour stability. The deep learning-based approach generated more continuous and structurally coherent boundaries, although at higher computational cost. These findings demonstrate that segmentation methods produce distinct structural representations that can directly affect the reliability of artificial intelligence systems for poultry inspection.

AgriEngineering doi: 10.3390/agriengineering8070287

Authors: Huijie Li Zhijie Hu Fangyuan Wu Jingbin Li Hewei Meng Hongfei Yang Zhentao Wang

During the growth phase, pumpkin crops are susceptible to a range of diseases. However, persistent bottlenecks remain in practical field operations, particularly the difficulty of detecting lesions at multiple scales and low identification efficiency. To address this, this study proposes an improved disease detection method based on YOLO11. Specifically, data augmentation techniques—including random flipping, scaling, brightness adjustment, and color jittering—are employed to diversify the training samples and enhance the model’s generalization capability under complex field conditions. Furthermore, by integrating the C2f-RepNCSPFPN structure to strengthen global semantic representation, incorporating the CBAM attention mechanism to suppress interference from complex backgrounds, and modifying the pyramid architecture with SimSPPF to increase sensitivity to small-scale lesions, a high-performance and lightweight detection model named YOLO11n-CCS is constructed. Experimental results demonstrate that the YOLO11n-CCS model achieves significant improvements in key metrics such as mAP@0.5 and recall. It effectively handles challenging scenarios involving leaf occlusion, varying illumination, and overlapping lesions. Concurrently, the model maintains a compact size of 3.6 M parameters and 14.0 G FLOPs, making it suitable for deployment on edge devices. The findings of this research offer a practical technical solution for real-time, precise disease identification in field crops and the development of crop protection robots.

AgriEngineering doi: 10.3390/agriengineering8070286

Authors: Pietro Picuno Roberto Puglisi George P. Spyrou George Papadakis Christina Stamataki Christine Stavropoulou Tina Papasideri Mercè Balcells Lluis Martin-Closas Camilla Tomao Anna Farrùs Cristina Mata José Carlos Rico Diogo José de Rezende Coelho Fátima Baptista Vasco Fitas da Cruz Teresa Batista Zoe Godosi Lili Arbrun Delphine Margout-Jantac

The adoption of Circular Economy approaches in agriculture offers opportunities to transform agricultural waste into valuable resources. This study was conducted within the TANGO-Circular project, which implemented Rural Lab training activities in four European countries: Greece, Italy, Portugal, and Spain. A structured questionnaire was administered to participants involved in these activities. Overall, 197 questionnaires were collected, of which 195 valid responses were included in the final quantitative analysis. Respondents included farmers, technicians, students, and other actors connected to agricultural and sustainability-related contexts. The results indicate that, despite differences in sample composition across countries, no statistically significant differences were observed in awareness and interest toward Circular Economy practices within the analyzed sample. Conversely, statistically significant differences emerged in gender distribution, professional profile, and perceived barriers among countries. Overall, respondents exhibited a generally positive attitude toward agricultural waste valorization and a high level of interest in further training activities. However, key barriers were identified, including lack of technical knowledge, limited institutional support, lack of awareness, and high initial costs. These findings suggest that the adoption of Circular Economy practices in agriculture requires not only positive attitudes, but also targeted training, accessible support services, and institutional and logistical frameworks adapted to local contexts.

AgriEngineering doi: 10.3390/agriengineering8070285

Authors: Rommel Angel Mayorga Vargas Yoisdel Castillo Alvarez Reinier Jiménez Borges Luis Angel Iturralde Carrera Perla Yazmín Sevilla-Camacho José Billerman Robles-Ocampo Juvenal Rodríguez-Reséndiz

Small-scale anaerobic digestion offers a decentralized pathway for organic waste recovery; however, its performance is often evaluated through fragmented modules that do not ensure formal traceability between design, operation, sustainability assessment, and decision-making. This study proposes an integrated five-stage methodological framework—diagnosis, design, operational control, economic-environmental assessment, and decision-making validation with feedback—whose ACT (Acceptance–Control–Traceability) decision block applies three sequential filters: data-quality control (Cdata≥0.90), multivariable operational stability (SR≥0.75), and comprehensive sustainability verification (Ω: GEInet>0, NPV>0, ϕ<1, and β≥0.10). A phased bibliographic mapping of 48 references confirmed the modular structure of the state of the art:60.4% address operation/optimization and 39.6% address economic-environmental evaluation, whereas only 2.1% incorporate a formal decision closure. The framework was inductively derived from two Peruvian case studies: Chillón, as a structural-predictive reference, and Huaycán, focused on active pH–EC control through stoichiometric NaHCO∗3 dosing. It was then prospectively evaluated on an independent 200-L co-digestion prototype in Lurín. The Lurín reactor delivered a mean biogas production of 96.9 L,d−1, a methane fraction of 54.2%, and a specific methane yield of 177.4 L,CH∗4,kgVS−1; nevertheless, the ACT decision yielded SR=0.33, classifying the system as not validated (Type A failure) due to ionic drift and thermal variability. Although the co-product scenario showed conditionally positive economic (NPV>0) and environmental (GEI∗net=38.2 kgCO2eq/a) indicators, Ω could not be evaluated because Stage 1 territorial inputs (E∗dem, P∗energy) were undefined. By rejecting a system that a fragmented modular assessment would have classified as viable, the framework demonstrates the practical value of enforcing operational-stability verification before any sustainability claim and prescribes targeted feedback to Stage 3 for ionic and thermal management.

AgriEngineering doi: 10.3390/agriengineering8070284

Authors: Pramod Pandey Aditya Pandey Jiang Huo Neeraj Chandrasekar Prachi Pandey Noelia Silva-del-Rio Bhim Charan Meikap Alejandro Castillo Wei Liao Jaya Shankar Tumuluru

Commercial-scale dairy farms produce large volumes of manure, posing environmental, animal health, and public health risks due to persistent pathogens and the accumulation of organic pollutants. This field-scale study evaluated the effects of covered anaerobic lagoons (CLs) and open facultative lagoons (OLs) on microbial reduction, solids stabilization, and manure biogeochemical characteristics on commercial dairy farms in California’s Central Valley during the summer months. Manure samples collected from lagoon inlets, outlets, and secondary lagoons were analyzed for Escherichia coli, total solids (TS), volatile solids (VS), and genomic DNA degradation. CL systems achieved substantially greater E. coli reductions (98.38%; 1.82 log) than OL systems (54.88%; 0.35 log), indicating enhanced pathogen suppression under anaerobic conditions. Progressive declines in genomic DNA concentrations and electropherogram signal intensities across treatment stages further demonstrated microbial biomass degradation during storage. In the CL system, TS concentrations decreased from approximately 0.985% at the inlet to 0.485% at the outlet, representing a 50.7% reduction, with an additional 56% reduction observed in the secondary lagoon. For moisture content, both CL and OL systems exhibited increases of approximately 0.37–0.5% from their respective inlets to outlets, with only marginal increases observed in the secondary lagoon. Comparable trends were observed in the OL system. Significant differences in TS, VS, moisture content, pH, electrical conductivity, and major cations (Na+, K+, Ca2+) occurred between CL and OL systems and among treatment stages. The secondary lagoon further enhanced microbial and solids reductions in both systems. Overall, the findings show that CL systems provide superior pathogen reduction and support biogas recovery, whereas OL systems demonstrate stronger VS stabilization. These results offer practical, field-based insights to inform improved manure management and pathogen mitigation strategies for commercial dairy operations.

AgriEngineering doi: 10.3390/agriengineering8070283

Authors: Yane Li Jiaqi Song Lijun Guo Xiang Weng Dalei Song

Accurate detection of grain pests is important for ensuring food security. The existing detection algorithms still face challenges such as low precision, high false positives, and missed detection when identifying storage pests due to the small size, complex backgrounds, and limited data availability. For this reason, this paper proposes a solution jointly driven by generative AI and analytical AI, named REGAN-BS-YOLOv8. Specifically, on the one hand, an image-generative AI method of Real_ESRGAN and the mosaic data augmentation method is introduced to improve the quality, resolution and number of grain pest images. On the other hand, a novel recognition algorithm is proposed by introducing Swin Transformer and BiFPN to YOLOv8n to obtain better perception ability, positioning accuracy, detail retention and edge clarity, improving the recognition accuracy, robustness, and generalization of stored-grain pest and the anti-interference capability in complex environments. In addition, a new dataset including 5818 images of nine types of grain pests at various scales and under diverse backgrounds was collected in this study. The experimental results show that the mAP@0.5, Precision and Recall are 97.7%, 96.2% and 95.4% respectively using the method proposed in this study, outperforming the other nine models. The performance of the model we proposed is excellent for different types of storage pests under different backgrounds, and the model identifies grain pests of different sizes in different environments with high precision and provides an important theoretical basis for the management of grain silos.

AgriEngineering doi: 10.3390/agriengineering8070282

Authors: Wenfei Feng Qiaolong Wang Liang Sun Gaohong Yu

Autonomous navigation of harvesters in hilly and mountainous terrain faces two major challenges: sensor discrepancies among multiple sources and depth distortion caused by terrain slopes. This paper proposes a tightly coupled vision–inertial–depth navigation and control system to address these issues. The system fuses visual features with inertial data within an adaptive extended Kalman filter framework that dynamically adjusts sensor weights to resolve conflicts from illumination changes and inertial drift. It also incorporates a real-time depth compensation model based on vehicle attitude to correct spatial mapping distortions during slope operations. Additionally, a multi-controller coordination strategy integrates steering, speed, and header height to align state estimation with control execution. Field experiments show that the system achieves a lateral positioning error of 3.4 cm—48.5% and 81.5% lower than pure-vision and pure-inertial approaches, respectively-and remains within 9.5 cm even in degraded scenarios. These results demonstrate the system’s ability to deliver high-precision navigation and stable operation on complex terrain.

AgriEngineering doi: 10.3390/agriengineering8070281

Authors: Alessandra Lepore Marco Limongiello Antonio Minervino Amodio Dario Gioia Carmine Ferrara Giovanni De Rosa Elèna Grobler Giuseppe Celano

Soil spatial variability is a key driver of tree development in perennial crops, and its characterisation is essential for precision orchard management. Against this background, soil–canopy relationships were investigated in a Citrus reticulata Blanco cv. Tango orchard under Mediterranean conditions. Electromagnetic induction (EMI), unmanned aerial vehicle (UAV) multispectral imagery, and mobile LiDAR data registered using a Simultaneous Localisation and Mapping (SLAM) workflow were integrated at individual-tree level. A previously validated EMI-derived apparent electrical conductivity (ECa) layer was used as a baseline descriptor of soil variability. UAV and mobile LiDAR acquisitions were harmonised for 40 trees: LiDAR point clouds were voxelised to derive canopy structural traits, while UAV imagery provided Soil-Adjusted Vegetation Index (SAVI) values. ECa at 14 kHz was negatively correlated with canopy volume (r = −0.605, R2 = 0.365) and canopy volume-to-projected area ratio (r = −0.571, R2 = 0.326), both significant at p < 0.001. Conversely, SAVI showed a weaker, non-significant relationship with ECa (r = −0.285, R2 = 0.081, p = 0.0749). The reduced multiple linear regression model explained canopy volume variability (R2 = 0.804), retaining canopy diameter and ECa as significant predictors. These findings highlight the value of LiDAR-derived structural traits as sensitive indicators of soil-related canopy variability, supporting the integration of structural, spectral, and soil-sensing data for site-specific orchard management.

AgriEngineering doi: 10.3390/agriengineering8070280

Authors: Joanne Magalhães Borges Gustavo Alves de Melo Maria Gabriela Mendonça Peixoto Jaqueline Severino da Costa Ana Daniela dos Santos Maria Cristina Angélico de Mendonça André Luiz Marques Serrano Matheus de Sousa Pereira Rafaela Fogaça Resende Thiago Henrique Nogueira

The broiler industry plays a crucial role in global food security and faces sustainability challenges that drive research aimed at assessing the environmental impacts of production. In this context, the objective of this study was to evaluate the environmental performance of different growth stages of broiler chickens in a Brazilian poultry industry. The analysis followed the Life Cycle Assessment (LCA) methodology and the ReCiPe 2016—Midpoint H calculation method. The data used were primary, provided by a company in the sector, and secondary, obtained from the Ecoinvent 3 inventory database. SimaPro version 9.5 software was used to perform the LCA analyses, with a functional unit of 1 kg of live broiler chicken and a system boundary at the farm gate. The results revealed that, at all stages, freshwater ecotoxicity had the greatest environmental impact, linked to the production of feed ingredients such as soybean and corn grains and crude soybean oil. Across the entire chain, the global warming potential value was 1.69 kg CO2-eq, with the final fattening stage showing the worst environmental performance. The study contributes to quantifying environmental performance and identifying critical inputs associated with environmental burdens in broiler production systems, providing evidence to support environmental management and sustainability assessment in poultry production.

AgriEngineering doi: 10.3390/agriengineering8070279

Authors: Rommel dos Santos Siqueira Gomes Rafael Tavares da Silva Hilderlande Florêncio da Silva Edcarlos Camilo da Silva Walter Esfrain Pereira Luciana Cordeiro do Nascimento

Following the application of biotic and abiotic systemic resistance inducers, which are enzymes related to plant defense, different responses are observed after infection with pathogens. Research into alternative methods using resistance inducers is a promising tool in the search for products with high potential for pathogen control. The application of the inducers acibenzolar-S-methyl, citrus biomass, K phosphite, silicate clay, and Ca and Mg silicate with or without the fungicide carbendazim showed greater potential to reduce anthracnose caused by Colletotrichum truncatum in lima bean plants, resulting in a reduced area under the disease progress curve and disease index. Disease progression promoted changes in enzymatic activity, where the application of acibenzolar-S-methyl, citrus biomass, K phosphite, silicate clay, and Ca and Mg silicate with or without the fungicide carbendazim resulted in the highest enzymatic activities and gas exchange rates compared to the other inducers. The use of biotic (citrus biomass) and abiotic (acibenzolar-S-methyl, K phosphite, silicate clay, and Ca and Mg silicate) inducers showed the highest potential to control anthracnose in the lima bean variety UFPB04. However, the resistance inducers promoted different plant responses when combined with fungicides and when applied in plants cultivated in different regions.

AgriEngineering doi: 10.3390/agriengineering8070278

Authors: Shenghe Bai Xin Dong Yulong Ding Weipeng Zhang Yanwei Yuan Kang Niu Liming Zhou Bo Zhao Lijing Liu Ran An Yuankun Zheng Bing Xue

Conventional wheat planters often suffer from poor uniformity and stability in sowing depth. To address these issues, a new sowing depth adjustment method based on soil coverage regulation was proposed, and the corresponding working parameters were established for a twin-shaft rotary tillage wheat planter. Following the “shallow rotation + deep rotation” twin-shaft plot preparation scheme, the sowing depth adjustment unit’s mechanical structure was designed, and the dynamic adjustment unit of sowing depth and the twin-shaft rotary planter were designed and verified. The experiments primarily involved parameter optimization testing of the sowing depth adjustment unit to determine both its optimal structural configuration and range of operating parameters. Using quadratic regression orthogonal testing, the optimal operational parameters were determined as follows: a secondary rotary tillage knife group rotation speed of 330 r/min, an operational speed of 4 km/h, and a soil coverage adjustment mechanism distance of 610 mm. The highest qualified rate for seeding depth reached 99.246%, while the minimum seeding depth variation coefficient was 6.521%. Field trials indicated that with a secondary rotary tillage knife group rotation speed of 330 r/min, a working speed of 4 km/h, and a compaction roller adjustment distance of 610 mm, the seeding depth qualified rate was 98.803%, and the variation coefficient of seeding depth was 6.881%. This method enables precise regulation of sowing depth in twin-shaft rotary planters, significantly enhancing the quality of wheat sowing operations.

AgriEngineering doi: 10.3390/agriengineering8070277

Authors: Fermín Martínez-Solís Mary de los Santos Córdova-Álvarez Reymundo Ramírez-Betancourt Erika V. Miranda-Mandujano Humberto Noverola-Gamas Jesus Lopez-Gomez

Image segmentation is a critical step in computer vision systems for phytosanitary diagnosis in cacao production. However, the reliability of classical segmentation methods remains insufficiently assessed under real field conditions, where images captured under non-standardized conditions are often affected by variable illumination, complex backgrounds, partial occlusions, and chromatic similarity between cacao pods and surrounding vegetation. This study compares global thresholding, K-means clustering, and GrabCut using 343 cocoa pod images captured in uncontrolled agricultural environments with non-standardized mobile devices; low-resolution images were retained to preserve external validity. Robustness was assessed on the full dataset using unsupervised structural metrics, including the segmented area ratio (AS), the largest component ratio (LCR), and the catastrophic failure rate (FC), while accuracy was validated on 50 manually annotated images using Intersection over Union (IoU). Wilcoxon signed-rank tests indicated statistically significant differences among methods. GrabCut achieved the best performance (IoU = 0.814), high structural coherence (LCR = 0.985), and a low catastrophic failure rate (FC = 1.7%). In contrast, K-means showed severe fragmentation and instability, whereas global thresholding was highly sensitive to illumination variability and complex backgrounds. These results indicate that GrabCut provides a robust training-free baseline for cocoa pod segmentation under uncontrolled field conditions, particularly for offline phytosanitary analysis where annotated datasets, supervised training, or GPU-based deployment are limited.

AgriEngineering doi: 10.3390/agriengineering8070276

Authors: Adebayo Adeyemi Ogundare Yetunde Oyebolaji Abiodun Olaitan David Oyefeso Sunday Joshua Ojolo Oludolapo Akanni Olanrewaju Olusegun M. Ilori Sikiru Oluwarotimi Ismail Joseph Ifeolu Orisaleye

The elimination of toxic and synthetic binders from the manufacture of fibreboards and particleboards is important, considering environmental and health implications. The utilization of agricultural waste for the manufacture of boards is also beneficial to waste management and enhances sustainability in the manufacturing of boards. In this study, corn stover was utilized in the manufacture of binderless bioboards. The influence of production factors, or manufacturing parameters, on the physical and mechanical properties of the bioboards was investigated. The bioboards had a density between 669.9 kg/m3 and 986.4 kg/m3, and the water absorption was between 165% and 237%. The bioboards had modulus of elasticity ranging between 13.3 and 85.6 MPa, whilst the modulus of rupture varied between 0.149 and 0.835 MPa. Also, the internal bond strength of the corn stover boards varied between 0.43 and 1.17 MPa. Particle size emerged as the dominant parameter governing density, hygroscopic behaviour, and mechanical performance, indicating that fibre packing and interlocking outweighed thermal softening effects with the investigated processing parameters. Further treatment of the corn stover is required to improve the application of the boards for load-bearing and structural applications.

AgriEngineering doi: 10.3390/agriengineering8070275

Authors: Kaan Arik Burak Ağgül

Accurate tomato detection in greenhouse imagery is essential for robotic harvesting, yield estimation, and crop monitoring, yet visual clutter, fruit overlap, partial occlusion, and variable illumination remain challenging for object detectors. Although attention modules are frequently used in agricultural vision studies to improve feature discrimination, their practical contribution is often reported without controlled comparison against strong baseline detectors. This study presents a reproducible and deployment-aware benchmark for single-class greenhouse tomato detection using 895 images with 4930 annotated tomato instances in PASCAL VOC format. The first experimental block used a fixed 70/20/10 split to compare Faster R-CNN, four attention-augmented Faster R-CNN variants, Cascade R-CNN with ResNet101-DCN-FPN, and YOLOv11s attention variants. A second extended protocol converted the annotations to YOLO format and evaluated YOLO-family detectors and RT-DETR-l under a stratified 70/15/15 split, including ablation, robustness, seed-stability, and deployment analyses. The annotation audit confirmed valid bounding boxes, no empty images, and a high proportion of small tomato instances. In the first block, attention integration did not consistently improve detection performance, whereas Cascade R-CNN achieved the highest accuracy with 92.80% mAP0.5 and 90.80% F1-score. In the extended protocol, RT-DETR-l obtained the highest test accuracy with 91.49% mAP0.5 and 58.59% mAP0.5:0.95, while Final-YOLO11s achieved comparable performance with lower latency, reaching 91.42% mAP0.5, 58.37% mAP0.5:0.95, and 86.19% F1-score. Across three seeds, Final-YOLO11s obtained a stable mean mAP0.5 of 90.84%. Robustness analysis showed that motion blur and Gaussian noise caused the largest degradation, whereas compact YOLO models exported reliably to ONNX and TensorRT. Overall, the results indicate that localization quality, robustness, latency, model size, stability, and export capability should be considered together, and that adding attention modules by default is less reliable than evidence-driven detector selection.

AgriEngineering doi: 10.3390/agriengineering8070274

Authors: Pimlapat Suwannasing Methee Kaewnern Wara Taparhudee Roongparit Jongjaraunsuk

This study proposed an artificial intelligence framework for red tilapia weight-range classification from UAV-based aerial imagery using two hybrid deep learning models, namely Hybrid CNN-XGBoost and Hybrid EfficientNet-B0-XGBoost. Two image sizes were evaluated: 5 × 5 m images, which preserved the spatial context of the cage culture system, and 2 × 2 m images, which focused on areas with high fish aggregation density. For the 5 × 5 m images, Hybrid CNN-XGBoost achieved the highest performance, with a mean accuracy of 0.988 ± 0.008 (98.8%) at 20 tuning units, whereas Hybrid EfficientNet-B0-XGBoost achieved 0.977 ± 0.021 (97.7%) at 40 tuning units. In addition, Hybrid CNN-XGBoost exhibited a substantially shorter average computational workflow time per image (0.038 ± 0.001 s) than Hybrid EfficientNet-B0-XGBoost (65.007 ± 6.141 s). In contrast, under the 2 × 2 m image condition with limited spatial context, Hybrid EfficientNet-B0-XGBoost outperformed Hybrid CNN-XGBoost, achieving the highest mean accuracy of 0.900 ± 0.010 (90.0%) at 30 tuning units, compared with 0.850 ± 0.022 (85.0%) at 50 tuning units. These findings indicate that larger images improve classification accuracy by preserving spatial context, whereas EfficientNet-B0 enhances deep feature extraction capability and improves classification accuracy when spatial context is limited.

AgriEngineering doi: 10.3390/agriengineering8070273

Authors: Luara Patrícia Lopes Morais Norlan Leonel Ramos Cruz Daniel Valadão Silva José Francismar de Medeiros Frederico Ribeiro do Carmo Luiz Fernando de Sousa Antunes Eulene Francisco da Silva Caio Alisson Diniz da Silva Palloma Vitória Carlos de Oliveira Simone Cristina Freitas de Carvalho Stefeson Bezerra de Melo Gustavo Lopes Muniz Claudia Alves de Sousa Muniz Rafael Oliveira Batista

Diluted solar saltworks effluent, applied via fertigation, can contribute to circular economy strategies by recycling nutrients and reducing the environmental impact associated with the disposal of hypersaline effluents. However, its adoption in drip irrigation systems is still limited, as are its effects on emitter performance. This study investigated whether electronic pulses could mitigate emitter clogging by applying dilution of saltworks bittern. Three systems (freshwater + saltworks bittern + electronic pulses; freshwater without electronic pulses; and freshwater + saltworks bittern without electronic pulses) were evaluated in Mossoró, Brazil, using three emitter designs over 0–320 h. Water physicochemical properties and hydraulic performance were monitored, and deposits were characterized by SEM—EDS and FTIR. Electronic pulses did not change bulk water chemistry but were associated with lower total suspended solids. Clogging risk was mainly related to alkaline pH, high electrical conductivity, and elevated Ca2+ and Mg2+ concentrations, which kept effluent dilutions within a high-risk range. Untreated effluent reduced irrigation uniformity, whereas treated effluent performed similarly to supply water. Electronic pulses reduced deposit complexity and the severity of critical events but did not eliminate clogging, and responses dependent on the emitter labyrinth’s geometry.

AgriEngineering doi: 10.3390/agriengineering8070272

Authors: Leandro Candido Gordin Amanda Alves da Silva dos Santos Joyce Gueiros Wanderley Siqueira Ariédenes Bandeira Rodrigues Alex Luís Bernardo da Silva Rafael Barros de Souza Ênio Farias de França e Silva Emmanuel Damilano Dutra Jorge Luiz Silveira Sonego

Sweet sorghum is a promising crop for bioethanol production in semi-arid regions, due to its tolerance to drought and salinity, where conventional energy crops face limitations. This study aimed to evaluate the agronomic performance of sweet sorghum irrigated with treated domestic wastewater (TDW) and its application as a substrate for bioethanol production under high-gravity (HG) and very-high-gravity (VHG) fermentation conditions. Field experiments were conducted in the Brazilian semi-arid using a 5 × 5 full factorial design consisting of five irrigation depths (40–160% crop evapotranspiration, ETc) combined with five potassium fertilization doses (0–80 kg·ha−1), totaling 25 treatments. Agronomic performance, biomass production, and total reducing sugar accumulation were evaluated in both plant cane and ratoon crops. Sweet sorghum juice was subsequently combined with sugarcane molasses and fermented using Saccharomyces cerevisiae in batch and fed-batch processes. Irrigation with TDW associated with moderate potassium fertilization enhanced plant development, biomass yield, and sugar accumulation, particularly at irrigation depths between 100% and 130% of ETc, reaching up to 1908 kg·ha−1 of TRS. Bioethanol production achieved fermentation efficiencies of 91.83% and 84.80% and productivities of 4.63 and 4.21 g·L−1·h−1 under HG and VHG conditions, respectively. These findings indicate that sweet sorghum irrigated with TDW is a promising feedstock for bioethanol production under high-gravity fermentation conditions while supporting the use of alternative water resources in semi-arid environments.

AgriEngineering doi: 10.3390/agriengineering8070271

Authors: Angela Vacaro de Souza Camilla da Silva Pereira Ana Laura Silva Silvério Giseli Boiam Dall’Antonia

This study presents a critical historical, bibliometric, and technological overview of the evolution of postharvest science, emphasizing the transition from classical physiology-based approaches to emerging predictive and technology-driven systems. Scientific production related to postharvest research was analyzed using the Scopus and Web of Science databases, while bibliometric mapping and co-occurrence networks were generated using VOSviewer to identify thematic trends, emerging research areas, and structural scientific clusters. In parallel, a technological foresight analysis was conducted through the Lens.org platform to investigate the temporal evolution of patent deposits, the geographical distribution of innovation, the leading institutional applicants, and the predominant technological domains according to the Cooperative Patent Classification (CPC). The results revealed a substantial global expansion of postharvest research over recent decades. This growth was accompanied by increasing technological diversification and stronger integration between scientific knowledge and intellectual property protection. The analysis also highlighted the progressive incorporation of advanced methodologies into postharvest science, including biochemical approaches, non-destructive technologies, artificial intelligence, predictive modeling, and digital tools for quality assessment and shelf-life management. Overall, the study demonstrates that postharvest science is undergoing a paradigmatic transition toward integrated, multidisciplinary, and data-driven systems aligned with current demands for sustainability, food security, innovation, and reduction of postharvest losses.

AgriEngineering doi: 10.3390/agriengineering8070270

Authors: Rasha M. Abou Samra Rafat Ramadan Ali

Recent advances in artificial intelligence (AI), machine learning (ML), deep learning (DL), and remote sensing technologies have transformed agricultural monitoring, precision farming, and climate-resilient decision-making. However, the widespread adoption of AI-driven agricultural systems remains constrained by the black-box nature of advanced predictive models, particularly deep neural networks. Explainable Artificial Intelligence (XAI) has emerged as a critical solution for improving transparency, interpretability, accountability, and trust in AI-based agricultural remote sensing systems. This review provides a comprehensive synthesis of the recent developments in XAI applications within smart agriculture, with emphasis on interpretable remote sensing analytics and sustainable decision-making. The review discusses the evolution of AI in agriculture, major remote sensing platforms, explainability frameworks, and the integration of XAI with satellite imagery, unmanned aerial vehicles (UAVs), Internet of Things (IoT), and geospatial big data. Key agricultural applications, including crop classification, yield prediction, disease detection, soil property assessment, irrigation management, carbon monitoring, and climate adaptation, are critically evaluated. Furthermore, the review compares intrinsic and post hoc explainability methods such as attention mechanisms, saliency maps, and counterfactual explanations. The interpretation of model outputs and reported results from recent studies is discussed to demonstrate how XAI improves model reliability and stakeholder confidence. Challenges related to data heterogeneity, scalability, uncertainty, ethics, fairness, and computational complexity are also analyzed. Finally, future perspectives are presented regarding hybrid explainable frameworks, physics-informed AI, edge computing, digital twins, and trustworthy autonomous agricultural systems. The review emphasizes the central role of XAI in enabling transparent and sustainable agricultural intelligence under rapidly changing climatic and environmental conditions.

AgriEngineering doi: 10.3390/agriengineering8070269

Authors: Domagoj Zimmer Mladen Jurišić Luka Šumanovac Anamarija Banaj Edita Štefanić Pavo Lucić

The next generation of agriculture mechanisation lies in the integration of autonomy, connectivity, and intelligence. This systematic review presents a conceptual, integrated engineering framework for safe and intelligent autonomy in agriculture tractors based on a systematic survey that covers major technical advances between January 2020 and May 2026, including research trends, key authors, conceptual clusters, etc. mapped by AI-assisted tools like Bibliometrix, Litmaps, and Elicit. We have a particular focus on the integration among three key research domains related to swarm robotics, secure telematics, and navigation through artificial intelligence. Important technical trends were identified, including but not limited to decentralised consensus algorithms for multi-vehicle coordination, lightweight telemetry secure protocols (CoAp/Oscore, DTLS), neural networks, and a fuzzy logic hybrid approach that adapts complex data to unstructured field constraints. Through the research reviewed, it was found that safety, decision-making, and collaboration are not designed and evaluated together as part of the overall proposed structure, which inhibits future expandability and safety. Based on current research in both areas and its shortcomings, an integrated concept was created that combines elements of decision-making, in-field telemetry, and safe decision-making from farm to cloud using security as the binding approach. At the end of the paper, three practical engineering guidelines are presented.

AgriEngineering doi: 10.3390/agriengineering8070268

Authors: Daniela Losacco Roberto Puglisi Carlo Salvemini Stefano Convertini

Climate change is increasingly constraining agricultural productivity by intensifying drought, accelerating soil degradation, and increasing pest and disease pressure. In this context, biostimulants are emerging as sustainable tools to improve crop resilience and maintain yield under suboptimal conditions. This study evaluated the effects of microbial and non-microbial biostimulants on chickpea (Cicer arietinum L.) growth, grain yield, seed quality, root traits, and soil properties under low-fertility and water-limited conditions in a marginal field in southern Italy. Treatments included an untreated control and biostimulants based on microelements, arbuscular mycorrhizal fungi (AMF), microbial consortia, ozonated oil, and humic substances. Biostimulants significantly affected agronomic traits. Humic substances increased plant height, while microelements markedly enhanced reproductive performance, with pod number increasing from 13 in the control to 23 pods plant−1. Root traits were also improved, particularly under microbial, humic, and AMF treatments. Grain yield was highest in the ozonated oil treatment (430.6 kg ha−1), whereas seed nutritional composition showed only limited variation among treatments. Biostimulants also induced treatment-specific changes in soil fertility indicators. Overall, the results indicate that selected biostimulants can improve chickpea performance and modulate soil fertility under marginal conditions, although multi-year studies are needed to confirm the stability of these responses under variable environments.

AgriEngineering doi: 10.3390/agriengineering8070267

Authors: Chandrasekhar Manikala Thanet Khomphet Noer Rahmi Ardiarini

Thai aromatic coconut faces persistent breeding challenges arising from limited genetic diversity, complex trait trade-offs, and increasing climate vulnerability. These constraints highlight the need for comprehensive phenotypic characterization to improve understanding of trait variation and support the identification of key traits associated with yield and quality improvement. This study aimed to dissect trait architecture and associations in Thai aromatic coconut using an integrated multivariate and machine learning framework. Two populations of Thai aromatic coconut, Ratchaburi (RB) and Pak Phanang (PP), were evaluated through comprehensive phenotypic characterization. Thirty-seven morphological, reproductive, and soil-influenced traits were evaluated using analysis of variance, broad-sense heritability estimates, Pearson correlation analysis, principal component analysis (PCA), hierarchical clustering, and machine learning models. The PP population exhibited superior water yield, indicated by a strong positive correlation between water content and TWW, and larger fruit size, but showed a pronounced trade-off with kernel weight. High phenotypic variability was observed for key traits (CV > 39%), accompanied by moderate to high heritability estimates. Principal component analysis revealed that PC1, PC2, and PC3 explained 32.2%, 13.0%, and 11.1% of the total phenotypic variation, respectively, accounting for a cumulative 56.3% of the observed variation among accessions. Random Forest models achieved high predictive accuracy for total water weight (R2 = 0.942), with water content (WC), fruit weight (FW), fruit diameter (FD), fruit length (FL), and hole spacing (HS) identified as the most influential predictors. Overall, the findings provide a non-destructive phenotypic framework for germplasm evaluation and trait-based selection in Thai aromatic coconut.

AgriEngineering doi: 10.3390/agriengineering8070266

Authors: Roxana B. Recio-Colmenares Carolina L. Recio-Colmenares Robin F. Conchas-Cedano Isaac Pilatowsky-Figueroa Eduardo Juárez-Carrillo Edith Xio Mara García Valeria N. Gómez-García César A. García-García

Charal (Chirostoma spp.) is a small pelagic fish of high nutritional and economic importance in central Mexico. However, its high moisture content and rapid post-harvest deterioration result in substantial losses in artisanal fisheries. Solar drying represents a sustainable preservation alternative, particularly in regions with limited access to refrigeration. This study investigates the drying kinetics of whole charal under field-realistic mild-to-moderate solar drying scenarios, including forced convection, natural convection, and open-air exposure. Experimental drying curves were modeled using classical thin-layer formulations, and neural network models were evaluated as complementary one-step-ahead predictors of experimental moisture ratio. Among the evaluated thin-layer models, the Modified Page formulation consistently provided the most reliable empirical description of the drying curves, with coefficients of determination greater than 0.97. An ablation-style comparison of ANN, CNN, LSTM, and CNN-LSTM architectures showed that the CNN model achieved the highest global predictive accuracy in the present dataset, with R2 = 0.987 and MSE = 4.3 × 10−4. Because the dataset contained a limited number of independent drying curves, the deep-learning results are interpreted as exploratory and complementary to thin-layer modeling rather than as a replacement for classical empirical models. The proposed framework may support future drying-endpoint estimation and decision-support tools for artisanal fish processing, provided that additional validation is performed with standardized sample masses, environmental covariates, and product-quality indicators.

AgriEngineering doi: 10.3390/agriengineering8070265

Authors: Jun You Xin Du Qixin Sun Shufa Chen Yue Jiang Ziming Lu

To address the low efficiency and high subjectivity of manual inspection of Astragalus slices, as well as the limited fine-grained detection accuracy caused by the visual similarity between the characteristic radial “chrysanthemum heart” texture and minor defects such as insect damage and mold, this study proposes a lightweight intelligent detection model named Astra-YOLO. A dataset consisting of 622 original Astragalus slice images from four categories was divided into training, validation, and test sets at a ratio of 8:1:1. Data augmentation was applied exclusively to the training set, resulting in a total of 3110 images. Based on YOLOv11n, three targeted improvements were introduced: GhostConv lightweight convolution was employed to reduce model parameters and computational cost; the parameter-free SimAM attention mechanism was integrated to suppress interference from complex textures and enhance defect feature representation; and Wise-IoU v3 was adopted to improve bounding box regression for precise localization of small defects. The experimental results demonstrate that Astra-YOLO achieves superior performance with only 2.53 million parameters and 6.20 GFLOPs. The model attains an mAP@0.5 of 92.7%, an mAP@0.5:0.95 of 73.8%, a precision of 92.4%, and a recall of 92.1%. These results indicate that Astra-YOLO effectively balances lightweight design and detection accuracy, outperforming the baseline model and other improved variants, thereby providing reliable technical support for industrial online inspection and automated quality grading of Astragalus slices.

AgriEngineering doi: 10.3390/agriengineering8070264

Authors: Elia Giacobazzi Pietro Orlandi Giorgia Franchini Filippo Muzzini Mattia Neri Matteo Roffilli

Efficient deep learning systems are increasingly essential for automated quality inspection in the agri-food industry. This work systematically investigates the impact of optimized data-loading and augmentation pipelines on both training efficiency and predictive performance of convolutional neural networks for fruit defect classification. Benchmark experiments compare CPU-based preprocessing, multithreaded tf.data pipelines, GPU-accelerated workflows, and NVIDIA DALI, showing up to a 16× reduction in training time together with significantly improved GPU utilization. Building on these findings, the optimized pipeline is deployed on a large-scale industrial dataset comprising more than 3 million tangerine image patches. Carefully designed augmentation strategies—including geometric transformations and color perturbations—are introduced to enhance data diversity while preserving the intrinsic visual characteristics of the product. The substantial reduction in training time enables a more efficient exploration of candidate architectures through a tailored Neural Architecture Search (NAS) framework designed for resource-constrained industrial settings. The proposed framework explores internal CNN hyperparameters while preserving architectural depth to satisfy real-time inference constraints. To reduce the computational cost of NAS, a Random Forest–based performance predictor is trained on early-epoch indicators such as the F1-score and used to rapidly screen candidate models. A genetic algorithm is then employed to efficiently explore the search space and identify high-performing configurations. Experimental results demonstrate that the proposed end-to-end workflow significantly accelerates the model development cycle while maintaining or modestly improving classification accuracy. While the reductions in training time are substantial, the predictive-performance improvements observed through NAS are comparatively modest and should be interpreted primarily as evidence that the proposed framework can identify competitive configurations under industrial deployment constraints. The resulting framework provides a practical and scalable workflow for developing and deploying automated visual inspection systems in industrial agri-food production lines.

AgriEngineering doi: 10.3390/agriengineering8070263

Authors: Suyun Nam Rhuanito Soranz Ferrarezi

Supplemental light-emitting diode (LED) lighting is essential for greenhouse crop production when solar radiation is insufficient, but it also contributes substantially to operating costs. Conventional strategies based on fixed photosynthetic photon flux density (PPFD) do not accurately reflect plant photosynthetic status, often leading to inefficient use of light energy. A chlorophyll fluorescence (CF)-based biofeedback system offers a plant-driven approach that dynamically adjusts light output to maintain target photosynthetic parameters. This system has been successfully tested in growth chambers with controlled environmental conditions, but no research has been conducted in greenhouses yet. This study developed and tested a greenhouse-compatible biofeedback lighting system using ‘Casey’ lettuce (Lactuca sativa) to evaluate its performance compared with conventional light controls. Two biofeedback control logics were applied: electron transport rate (ETR)-based (target ETR of 85 or 120 µmol·m−2·s−1) and quantum yield of photosystem II (ΦPSII)-based control (target ΦPSII of 0.735), with constant PPFD- and timer-based lighting as reference treatments. Both biofeedback logics maintained their target values, confirming stable performance under dynamic greenhouse conditions. Despite successful real-time light regulation in greenhouse conditions, shoot biomass and energy-use efficiency did not differ among treatments under moderate greenhouse conditions (p > 0.05). This study establishes a functional prototype of a real-time physiological biofeedback system for greenhouse supplemental lighting control.

AgriEngineering doi: 10.3390/agriengineering8070262

Authors: Jadde Milena Guedes Secundino Daniela Silva Gomes Moreira do Valle Marcélio Alves Teotônio Juscélia da Silva Ferreira Jéssica Rafaella de Sousa Oliveira Kaline Amaral Wanderley Ana Dolores Santiago de Freitas Allan Almeida Albuquerque Paula Renata Muniz Araújo Rômulo Simões Cezar Menezes

Sustainable crop production in nutrient-poor sandy soils requires fertilization strategies that improve nutrient uptake while reducing environmental impact. This study evaluated anaerobic cattle manure digestate and carbon dot biostimulants as alternatives to conventional mineral NPK (nitrogen–phosphorus–potassium) fertilizer for corn (Zea mays L., cv. AG 1051) during vegetative development. A randomized greenhouse experiment compared nine treatments over three successive 45-day cycles, assessing shoot-tissue macronutrient content (N, P, K) and morphological parameters (shoot dry weight, stem diameter, and plant height). Digestate delivered approximately 1.4× more phosphorus and 8.4× more potassium per pot than mineral NPK, although nitrogen inputs were matched (~77 mg pot−1). Digestate-based treatments achieved shoot dry weight 132% above control and 63% above mineral fertilizer (p < 0.001), with biomass advantages sustained across all three cycles while mineral fertilizer effects dissipated entirely by Cycle 3. Phosphorus content was the strongest biomass predictor (r = 0.86, p < 0.001), and a significant nitrogen–phosphorus antagonism (r = −0.59, p < 0.001) revealed relevant nutrient interaction dynamics. The higher biomass observed under digestate-based treatments reflects both the higher total P and K inputs from digestate and the beneficial effects of organic matter on nutrient bioavailability in this phosphorus-limited system. Carbon dot biostimulants did not improve biomass when applied alone (values at or below control), but they contributed to intermediate biomass gains when combined with nutrient sources, functioning as nutrient uptake enhancers rather than standalone fertilizers. Principal component analysis (74.3% variance explained) classified the nine treatments into three distinct treatment clusters. These findings support digestate valorization as a circular-economy alternative to conventional mineral fertilization, offering higher biomass under N-equivalent application and sustained residual effects in nutrient-poor sandy soils.

AgriEngineering doi: 10.3390/agriengineering8070261

Authors: Ricardo Macedo da Silva Mario Adriano Ávila Queiroz Thieres George Freire da Silva Juliana Caroline Santos Santana Stela Antas Urbano Juliana Cantalino dos Santos Wagner Martins dos Santos Antonio Leandro Chaves Gurgel Felipe Pontes Teixeira das Chagas Fábio dos Anjos Rezende João Virgínio Emerenciano Neto

The use of nondestructive technologies combined with machine learning has emerged as a promising approach for estimating structural and productive traits in agricultural systems. This study evaluated the potential of Unmanned Aerial Vehicle (UAV) imagery integrated with the Random Forest algorithm to predict structural, physiological and productive variables of forage cactus cultivated under semi-arid conditions. The experiment was conducted over two years using four varieties: Orelha de Elefante Mexicana (OEM), Miúda, IPA Sertânia and IPA 20. RGB and red–green–near-infrared (RGNir) orthomosaics, along with a digital elevation model, were used to derive spectral and structural variables, which were related to field measurements. Model performance was assessed using the coefficient of determination (R2). The models showed high predictive performance for dry mass production, particularly for OEM, IPA Sertânia and IPA 20 (R2 = 0.85, 0.85 and 0.83). Physiological variables, such as chlorophyll A and B, also showed consistent fits (R2 = 0.70 and 0.83), while structural variables, including height and volume, exhibited lower stability. Differences among varieties affected model accuracy, especially for Miúda, due to its architectural characteristics. The integration of UAV imagery and machine learning provides a reliable approach for monitoring forage cactus, although model performance depends on plant structure.

AgriEngineering doi: 10.3390/agriengineering8070260

Authors: Noura Ouled Sihamman Assia Ennouni My Abdelouahed Sabri Abdellah Aarab

Herbicide overuse remains a major challenge in sugar beet production because of its environmental and economic impacts. This study addresses three key gaps in UAV-based weed mapping: the lack of leakage-aware benchmarks for North African sugar beet imagery, the limited controlled comparison of one-stage and two-stage detectors under identical experimental conditions, and the limited translation of detection outputs into decision-support layers for site-specific spraying. We develop a reproducible UAV-based deep learning pipeline and present a field case study from Beni Mellal, Morocco. Fast R-CNN, YOLOR, YOLOv7, and YOLOv5 were compared under a unified protocol using identical data partitions, input resolution, augmentation strategies, and evaluation metrics, with locally acquired RGB imagery, COCO-format annotations, and leakage-aware field/flight splits. Under the tested conditions, YOLOv5 achieved the strongest performance, with 97.82% precision, 83.05% recall, 91.61% mAP@0.5, and 72.63% mAP@0.5:0.95. Error analysis indicated that missed detections were mainly associated with small weeds, partial occlusion by sugar beet leaves, and visually similar broadleaf weeds. Detector outputs were further organized into weed-intensity maps and used in a pilot scan-guided spot-treatment workflow on the surveyed parcels. This pilot implementation demonstrates the feasibility of translating UAV detections into prescription layers, but it should not be interpreted as a complete multi-season agronomic or economic validation. The main contribution is therefore a leakage-aware, unified benchmarking protocol and a reproducible end-to-end workflow from UAV detections to field-ready prescription maps. Future work should quantify herbicide savings, treatment efficacy, yield response, economic return, edge-device throughput, and transferability across regions and seasons.

AgriEngineering doi: 10.3390/agriengineering8070259

Authors: Apidul Kaewkabthong Jedsada Saijai Pisitwitthaya Sriphuk Agustami Sitorus Vasu Udompetaikul

Sugarcane harvester performance varies substantially with field geometry, crop, and operator factors, yet separating these sources from telematics data while preserving engineering interpretability remains a methodological gap. This study models field efficiency (Eff) and harvesting capacity (Ca) separately from JDLink telematics, aligning model structure with each target’s response behavior. Operational data covered 105 plots across four seasons (2019/20–2022/23) from three John Deere CH570 chopper harvesters in eastern Thailand. Six engineering-relevant predictors were retained after multicollinearity screening, and linear (MLR), additive nonlinear (GAM), and tree-based models were compared under 5-fold grouped cross-validation by BaseField (87 groups). Eff was assigned to GAM (R2CV = 0.621 ± 0.114) on the basis of its threshold-like response to turning frequency; Ca was retained for MLR (R2CV = 0.681 ± 0.121), with GAM essentially tied. Train–validation gaps were substantially smaller for additive models (0.096–0.118) than for tuned tree-based candidates (GBR 0.210–0.302, RF 0.322–0.358). Turning frequency (TF) and perimeter-to-area ratio (PAR) were the strongest predictors, and a constant-turn-time partial-out test indicated that TF’s univariate effect on Eff is largely mediated by the time-budget identity. Tactical interventions (path planning, operator training, machine–field allocation) are immediately feasible, although strategic field-layout change remains constrained by smallholder land tenure.

AgriEngineering doi: 10.3390/agriengineering8070258

Authors: Alexey P. Dolgalev Alexander A. Smirnov Yuri A. Proshkin Pavel V. Tikhonov Dmitry A. Burynin Inna V. Knyazeva Alina S. Ivanitskikh Alexander V. Sokolov

Stevia (Stevia rebaudiana Bertoni) is an important source of natural sweeteners. Since its commercial value depends on steviol glycosides, quality assessment primarily involves quantifying these compounds in leaves and shoots. While chromatography is the standard analytical method, it is labor-intensive and time-consuming; it involves multiple processing steps that may cumulatively introduce errors and remains relatively expensive. Although chromatography remains the most accurate method, this exploratory study evaluates the potential of using spectroscopy as an auxiliary method for the approximate assessment of steviol glycoside content. Leaf reflectance spectroscopy could be a simpler and more cost-effective approach. However, relationships between leaf reflectance and steviol glycoside content are indirect and mediated by physiological processes. To account for these indirect dependencies, cumulative UVB exposure was included as an additional feature because it influences both leaf optical properties and plant metabolic processes. A low-cost spectrometer was utilized as the measuring instrument. The study was conducted over a period of three months on 77 S. rebaudiana clones, divided into four groups based on their level of UVB irradiance (control without irradiation, 400, 600, and 800 μW m−2). Based on the collected data, linear and polynomial regression, Random Forest, XGBoost, PLSR, and ElasticNetCV models were trained. Cumulative UVB exposure was found to be the most important feature. Of the spectral features, the most informative for assessing the content of steviol glycosides were spectral indicators in the far-red and near-infrared (NIR) ranges. Our results indicate a detectable relationship, with Random Forest being the best-performing model and achieving a moderate predictive performance (R2 = 0.66). Despite their limited predictive performance, the models demonstrate that leaf reflectance spectra combined with cumulative UVB exposure contain information related to steviol glycoside content. These findings support further investigation of remote sensing approaches for crop quality assessment.

AgriEngineering doi: 10.3390/agriengineering8070257

Authors: Thiri Shoon Wai Naoki Maruyama Napassawan Wongmongkol Chatchawan Chaichana Smith Eiamsa-ard Masafumi Hirota

Rather than cooling an entire facility, local area cooling—achieved by placing simple-shaped heat exchangers near plants—could be an effective, low-energy climate regulation strategy for agricultural fields in greenhouses. In this study, a comparative analysis was performed for three serpentine-shaped heat exchangers varying in pipe diameter (12.7 and 15.88 mm) and pipe spacing (50 and 100 mm). Their heat transfer performance and air temperature distribution were measured in terms of local area cooling. Local air temperatures below the heat exchanger were also measured, whereas temperatures above the unit served as a reference. Both heat transfer performance and the pressure drop in the heat exchangers were investigated as well. Cooling experiments were conducted with inlet fluid temperatures from −5 to 10 °C and flow rates from 0.3 to 3.0 L/min (Re = 50–1394). The results showed that local air temperature reductions reached approximately 9 °C for the 12.7 mm pipe with 50 mm spacing, 10 °C for the 15.88 mm pipe with 50 mm spacing, and 5 °C for the 15.88 mm pipe with 100 mm spacing. Heat flux for the 15.88 mm pipe was two-thirds lower at a spacing of 50 mm and 1.5 times higher at a spacing of 100 mm compared to the pipes smaller in diameter. Moreover, pressure drops for the large-diameter pipes were about half those of the smaller pipes. The results from this experimental study are expected to contribute to practical greenhouse cooling applications and provide useful guidance for configuration selection for heat exchangers.

AgriEngineering doi: 10.3390/agriengineering8070256

Authors: Lucas Gabryel Maciel dos Santos Lucas Santos Santana Marcos David dos Santos Lopes Josiane Maria da Silva Carmem Lúcia da Silva Surmani Celine Russo Daniele Sarri Giuseppe Rossi Andrea Pagliai

Digital Elevation Models (DEMs) provide essential information for decision-making in precision agriculture. This study evaluated the altimetric quality of DEMs generated by Remotely Piloted Aircraft (RPA) platforms, the influence of flight direction, and the effect of mechanically disturbed soil surface conditions. We obtained data from a 900 m2 area. Flights were conducted under pre- and post-mechanization conditions using a reversible plow, with flights in both longitudinal and transverse directions. We processed images using Structure-from-Motion (SfM) techniques to generate dense point clouds and DEMs. Statistical analyses relied on raster statistics and elevation cross-section transects of microtopography, were evaluated via descriptive statistics, ANOVA, Tukey’s HSD tests, and spatialization with micro-variation classification. Significant differences emerged among the evaluated models (p < 0.001), with Phantom-derived DEMs showing systematically higher elevations than Mavic models (617.31 ± 0.16 m vs. 605.41 ± 0.23 m, respectively). Post-plowing longitudinal flights showed the least variation, indicating greater altimetric consistency after secondary soil preparation. Conversely, the pre-plowing transverse flight (Mavic Flight 2) produced the largest errors. Quantitative assessment of topographic profiles revealed high morphological correspondence between platforms, with Pearson correlation coefficients ranging from 0.84 to 0.96 after vertical normalization, confirming that terrain morphology was preserved despite systematic vertical offsets. The effect of flight direction was more pronounced before soil preparation; after harrowing (a homogeneous surface), the difference between directions decreased, but longitudinal flights maintained an advantage, while transverse flights (especially Mavic) tended to overestimate elevations spatially.

AgriEngineering doi: 10.3390/agriengineering8070255

Authors: Maria Pereira de Araújo Alessandro Torres Campos Milson Evaldo Serafim Bruna Campos Amaral Luzia Batista Moura Romário de Sousa Almeida Bruno Montoani Silva Leônidas Canuto dos Santos Tadayuki Yanagi Junior Sarah Emília Ieno Reis Victor Buono da Silva Baptista Diego Bedin Marin Felipe Schwerz

Pastures are the primary food source for cattle, yet their productivity is often limited by management practices and water scarcity. In this context, approaches capable of representing nonlinear relationships and handling uncertainties can support sustainable water management. The objective of this study was to develop and compare fuzzy inference systems (FISs) to predict the instantaneous water use efficiency (iWUE) in a forage species subjected to organic and mineral fertilization under different levels of water deficit. The models were built in MATLAB R2024a using Mamdani and Sugeno inference methods. Input variables (fertilization and water deficit) were represented by triangular, trapezoidal, and Gaussian membership functions, while the output variable (iWUE) was modeled using triangular, trapezoidal, and Gaussian membership functions in the Mamdani system and singleton functions in the Sugeno system. Different defuzzification strategies were evaluated, resulting in 21 fuzzy systems. The results showed satisfactory model performance, with coefficients of determination above 0.90 and strong agreement between observed and simulated values. The Mamdani system with trapezoidal membership functions and centroid defuzzification achieved the best predictive performance (R2 = 0.9846, NSE = 0.9887, RMSE = 0.0923). The response surface generated by the best-performing fuzzy system indicated a smaller reduction in iWUE under organic fertilization compared to mineral fertilization as water deficit intensified. The developed fuzzy systems demonstrated potential to represent the interaction between nutritional management and water availability, supporting decision-making in forage production systems.

AgriEngineering doi: 10.3390/agriengineering8070254

Authors: Sinesipho Fose Adolph Nyamugama Naledzani Ndou

Timely detection and monitoring of diseases in sweet potato crops are important for hunger alleviation and food security. This study aimed to evaluate the efficacy of the optimized field spectrometric reflectance thresholds in spatially partitioning sweet potato crops on the unmanned aerial vehicle (UAV) multispectral imagery based on infection types. A field survey was carried out to sample deformed leaves for laboratory diagnosis of possible identification of sweet potato leaf infection types. Laboratory analysis results revealed nutrient deficiency, SPVC-positive, fungi isolates (i.e., alternaria, bipolaris, fusarium, phoma), and mechanical damage as the causes of leaf deformation. Overlap analysis results revealed reflectance overlaps across all leaf deformation types, making it difficult to spatially partition sweet potato crops based on deformation types. Instead, sweet potato crops were spatially partitioned by considering the minimum and maximum thresholds acquired from the whole dataset. Area covered by deformed sweet potato leaves in blue, green, red, red edge and NIR were found to be 11.91%, 28.71%, 43.66%, 46.41% and 30.6% respectively. Coefficient of determination results revealed poor classification results, with R2 value of 0.23, 0.19, 0.28, 0.17 and 0.63 for blue, green, red, red edge and NIR respectively. However, the NIR spectral band yielded R2 value closer to the acceptable value of 0.7.

AgriEngineering doi: 10.3390/agriengineering8060253

Authors: Komil Astanakulov Berdiyar Kalimbetov Azamat Rasulov Zulfiya Kannazarova Sayyora Mannobova Fengxin Yan Xu Mao Fakhriddin Karshiev Asroriddin Kosimov Mukaddas Mamasalieva

This study presents a comprehensive bibliometric analysis of research trends in grain cleaning devices from 1998 to 2025. Grain cleaning equipment plays a critical role in post-harvest processing by improving grain quality, reducing losses, and enhancing overall efficiency in agricultural systems. The analysis is based on bibliographic data retrieved from the Scopus database. Various bibliometric tools and indicators, including publication trends, citation analysis, co-authorship networks, and keyword co-occurrence, were employed to identify patterns of development, major contributors, and emerging research themes in this field. The results reveal a significant growth in publications in recent years, reflecting increasing global interest in advanced cleaning technologies, including energy-efficient systems, intelligent sorting, and automation. Key research hotspots include vibration-based separation, pneumatic systems, and smart sensor-based cleaning technologies. This study provides a systematic overview of the intellectual structure and evolution of grain cleaning device research, offering valuable insights for researchers and practitioners. The findings also highlight existing research gaps and suggest future directions for the development of more efficient, sustainable, and intelligent grain processing technologies.

AgriEngineering doi: 10.3390/agriengineering8060251

Authors: Antônio de Azevedo Perleberg Taís Amanda Mundt Vívian Ebeling Viana Latóia Eduarda Maltzahn Ariano Martins de Magalhães Júnior Antonio Costa de Oliveira Luciano Carlos da Maia Camila Pegoraro

The quality of rice grain impacts milling yield, market acceptance, and product value. Physical quality is determined by many traits, such as chalkiness, whiteness, vitreous whiteness, caryopsis length, and width. Breeding for these traits is challenging due to their quantitative nature, environmental effects, and time and labor requirements to evaluate these traits. The digital grain analyzer (S21) equipment determines rice grain physical quality by image-based analysis; however, its use remains restricted. Thus, here we aimed to evaluate S21 efficiency to determine the physical quality of rice grains and estimate the genetic diversity of the trait using a Brazilian panel of 152 irrigated rice genotypes as a working model. We accessed total whiteness, vitreous whiteness, chalkiness degree, chalky grain rate, white belly, grain length, width, and length/width ratio. Our results demonstrated that S21 allowed the characterization of the genotypes according to physical traits, facilitating grouping and separation of accessions and correlation analyses between quality traits. It was also possible to estimate the heritability of quality traits. S21 was efficient in characterizing the physical quality of rice grains and determining their genetic diversity. The equipment is an effective tool exhibiting potential application by breeder programs.

AgriEngineering doi: 10.3390/agriengineering8060250

Authors: Ziyuan Zhao Yu Wang Jill Domel Ziteng Xu

Woody breast (WB) myopathy is a major quality defect in modern broiler production, but its complex and heterogeneous pathophysiology continues to challenge objective and biologically meaningful detection. This review synthesizes 53 studies identified through a systematic search (January 2020 to May 2026), together with foundational pre-window works cited for context, organized across three main areas: physical and mechanical measurements, biochemical and physiological indicators, and imaging- and artificial intelligence-based approaches. Physical methods provide relatively interpretable measures of tissue properties, including stiffness, electrical behavior, and water mobility. Biochemical and physiological approaches offer greater insight into the mechanisms underlying WB development and may support earlier prediction, although their routine application remains limited. Imaging and AI-based methods appear to be the most scalable options for automated assessment, but their performance is still constrained by limited datasets and imperfect reference standards. Overall, no single modality fully captures the structural, functional, and metabolic complexity of WB. Future advances will require improved quantitative reference frameworks, more robust validation under commercial conditions, and multimodal strategies that better integrate biological relevance with practical applicability.

AgriEngineering doi: 10.3390/agriengineering8060252

Authors: Yasiel Pérez Vera Melissa Kristel Chambi Flores Santiago Alonso Avilés Córdova Irvin Estuardo Cazorla Macedo Percy Aarón Luján Biamonte Edgardo Alfredo Rivero Callohuanca

Manual classification of rice grain defects remains a persistent challenge in the Peruvian rice industry, as it relies heavily on human inspection, leading to variability, inconsistency, and reduced efficiency when processing large volumes of product. This study evaluates the effectiveness of transfer learning and convolutional neural networks (CNNs) for the automatic classification of four rice grain categories relevant to quality assessment: Whole, Stained, Broken, and Chalky. A dataset comprising 6599 RGB images was employed. To ensure a reliable evaluation protocol, the dataset was first partitioned into training (70%), validation (15%), and test (15%) subsets, after which data augmentation was independently applied within each partition to balance class distributions. Five pretrained CNN architectures were evaluated: MobileNetV2, EfficientNetB0, ResNet50, DenseNet121, and InceptionV3, all of which share a common classification head. Models were trained using transfer learning and early stopping based on validation loss. Performance was assessed using accuracy, precision, recall, F1-score, confusion matrices, 95% confidence intervals, and pairwise McNemar statistical tests. The results showed that ResNet50 achieved the highest classification accuracy (84.71%), followed by EfficientNetB0 (83.60%) and DenseNet121 (83.20%). Statistical analysis indicated that performance differences among the top-performing architectures were relatively small, with significant differences observed only for selected model pairs. Across all evaluated models, the discrimination between Whole and Chalky grains remained the most challenging classification task due to their high visual similarity. Overall, the findings demonstrate that transfer learning-based CNNs provide an effective and scalable approach for automated rice grain defect identification and quality assessment in agricultural environments.

AgriEngineering doi: 10.3390/agriengineering8060249

Authors: Wan D. Bae Shayma Alkobaisi Muhammad Farhan Safdar Prachitee Chouhan

Precision agriculture increasingly relies on machine learning applied to high-resolution data acquired by unmanned aerial vehicles (UAVs) to support crop monitoring, stress detection, and yield forecasting. This survey presents a structured review of machine learning methods for UAV-enabled precision agriculture and organizes over 100 peer-reviewed studies within a unified four-dimensional taxonomy defined by sensing modality, data type, model family, and analytical task. The taxonomy enables systematic comparison across RGB, multispectral, hyperspectral, LiDAR, and IoT data sources and across classical machine learning, deep learning, hybrid sequential models, and emerging transformer-based architectures. We analyze how modeling choices interact with data characteristics to influence robustness, cross-environment generalization, computational efficiency, and deployment feasibility on UAV and edge platforms. Recurring challenges include limited labeled data, domain shift across seasons and fields, multimodal heterogeneity, occlusion, and real-time processing constraints. We identify emerging research directions, including data-efficient learning, representation-level multimodal fusion, domain adaptation, lightweight architectures for embedded deployment, and uncertainty aware decision support. By formalizing the landscape through a unified taxonomy, this survey provides a foundation for designing scalable, robust, and deployable machine learning systems for next-generation precision agriculture.

AgriEngineering doi: 10.3390/agriengineering8060248

Authors: Shiny Rajendrakumar Rajashekarappa

Global agriculture is seriously threatened by plant diseases, which result in large losses in both productivity and quality. Timely and accurate disease detection is essential for effective crop management and food security. This work presents an improved MobileNetV2 architecture with Modified Lite Attention (MLA) Model for detecting plant leaf disease. Our methodology incorporates pre-processing, feature extraction through attention model, convolution layers, and classifying into diseased or healthy categories. Further, multiclassification of diseases is performed on a dataset comprising 4432 samples including whitefly, leaf spot, leaf curl, yellowish and healthy leaves. The proposed attention model is compared with existing attention models like CBAM (Convolutional Block Attention Model), SE (Squeeze and Excitation), ECA (Efficient Channel Attention) and SDMnet (Spatially Dilated Multi-Scale Network) to validate our hybrid MLA feature extraction technique. Customizing the categorization with fully connected layers and utilisation of a pre-trained MobileNetV2 model allow the system to achieve excellent results. Findings show encouraging accuracy, surpassing 97% compared to existing techniques for multiclass dataset classification. The integration of MobileNetV2 with custom dense layers enables robust detection even with limited datasets, making it ideal for use in mobile or low-resource agricultural environments. Further, the proposed method is tested on the PlantVillage dataset consisting of 10,836 samples using K-Fold cross-validation for K = 5 and K = 4 to obtain an average accuracy of 98.4% and 98.69%, respectively.

AgriEngineering doi: 10.3390/agriengineering8060247

Authors: Marat Aldabergenov Tokhtar Abilzhanuly Nursultan Orynbayev Sergey Sakhnov

Year-round production of high-quality green forage using resource-efficient technologies is an important challenge for sustainable livestock farming. This study developed and experimentally evaluated an automated multi-tier hydroponic system integrating a sapropel-based bioactive medium, recirculating irrigation, and energy-efficient LED lighting. Experimental trials were conducted using feed barley (Hordeum vulgare L.) during a 10-day cultivation cycle. Resource consumption was assessed under light in-tensities of 200, 300, and 400 μmol m−2 s−1, while biomass productivity was evaluated using sapropel extract concentrations of 1.0%, 2.0%, and 2.5%. The highest biomass productivity was obtained at a 2.5% concentration, where fresh green mass reached 44.8 kg per tray (25.45 kg m−2), representing a 1.6-fold increase compared with the control treatment, which consisted of identical hydroponic cultivation conditions without sapropel extract addition. The recirculating irrigation system reduced specific water consumption, while optimized LED lighting improved energy-use efficiency. The results demonstrate that integration of natural bioactive supplementation with automated environmental control can significantly enhance hydroponic forage productivity while reducing specific resource inputs.

AgriEngineering doi: 10.3390/agriengineering8060245

Authors: Anna Kujawa Natalie Hanrieder Sergio González Rodríguez Lyubomir Hristov Manuel Jesus Blanco Leontina Berzosa Álvarez Ana Martínez Gallardo Adoración Amate González Marina Casas Fernandez Francisco Javier Palmero Luque Manuel López Godoy María del Carmen Alonso-García José Antonio Carballo Luis Fernando Zarzalejo Tirado Cristina Cornaro Robert Pitz-Paal

Watermelons account for 7% of the world’s fruit vegetable production. In the European market, Spain contributes around 35% of total watermelon supply, with the majority grown in greenhouses in Almería, Southern Spain. This study presents experimental results from the first agrivoltaic watermelon trial conducted in a raspa-y-amagado greenhouse during the 2024 growing season in Almería, Spain. Watermelons were cultivated under two shading treatments with 30% and 50% of the roof area covered with PV modules and compared against an unshaded control group. Throughout the experiment, temperature values in the 30% and 50% zones were 2.2 °C and 4.3 °C lower than in the control zone, respectively. The unshaded control zone and the 30% shading treatment maintained DLI conditions within the optimal range between 21 mol m−2 d−1 and 32 mol m−2 d−1 for most of the crop cycle, while the 50% shading zone remained largely above the minimum threshold of 15 mol m−2 d−1 required for adequate crop growth. No statistically significant differences were observed in fruit weight, rind width, fruit firmness, or soluble solids content at harvest. In addition, the experimentally measured irradiance data from this study were compared with simulations from a previously established irradiance model. The model was applied to the raspa-y-amagado greenhouse, and the experimental data were used to perform a long-term comparison between simulated and measured irradiance for 265 days of data. The irradiance model accurately reproduced shading effects from both the PV modules and greenhouse structure, achieving nRMSE values of 0.09, 0.18, and 0.27 for the control, 30% shading, and 50% shading zones, respectively.

AgriEngineering doi: 10.3390/agriengineering8060246

Authors: Michail Semenišin Tadas Jomantas Aurelija Kemzūraitė Dainius Savickas Albinas Andriušis Dainius Steponavičius

Perennial crop systems (e.g., orchards) require frequent spraying with plant protection products. Equipment plays a crucial role in assessing energy efficiency, productivity, economic performance, and the environmental impact of orchard production. In recent years some farmers have replaced conventional tractor-mounted air-blast sprayers (TMABS) and switched to unmanned ground vehicles (UGVs) or unmanned aerial vehicles (UAVs). However, there has been a lack of comparative studies on the energy and environmental assessment of these systems. This study aimed to evaluate the overall viability of different orchard spraying technologies in terms of energy efficiency, economic costs, and environmental impact. A life cycle assessment (LCA) of five sprayers was performed: a TMABS, a UGV, and three UAVs. The CML-IA methodology and SimaPro 9.5 software with the Ecoinvent v3 database were used to determine the environmental impact of the compared machines. Energy efficiency was calculated using fuel consumption data, human labor energy, and the energy embodied in the machinery. Economic viability was evaluated through capital depreciation, labor, energy consumption, consumable and maintenance cost per hectare calculation models. The results indicate that UAV systems, as compared to TMABS, can significantly reduce operational energy consumption, water use, and environmental impacts. The GWP of UAV systems was about 67% lower compared to the TMABS, while the UGV, due to lower performance efficiency, exhibited a 4% larger GWP (kg CO2eq ha−1). The findings of this study highlight that UAVs can produce the optimal results in comparison to other application methods.

AgriEngineering doi: 10.3390/agriengineering8060244

Authors: Victor Hugo Almeida Lima Elton Fialho dos Reis Ivano Alessando Devilla Josué Gomes Delmond Eduardo Henrique da Silva Santana

Most studies in the field of application technology have focused on the interaction between adjuvants and agrochemicals, highlighting the need for further research to evaluate the behavior of adjuvants in association with other classes of crop protection products. In this context, the objective of this study was to evaluate the influence of adjuvants and air velocity on spray drift deposition in simulated applications conducted in a wind tunnel using a bioinsecticide based on Bacillus thuringiensis. The experiment was carried out in an open-circuit, blower-type wind tunnel installed at the Agricultural Machinery Laboratory of the State University of Goiás—Central Campus. The study was conducted in a completely randomized design arranged in a 5 × 4 × 4 factorial scheme, with three replications. Treatments consisted of five horizontal distances from the spraying point (0.45, 0.75, 1.05, 1.35, and 1.65 m), four wind speeds inside the tunnel (1 m s−1, 2 m s−1, 3 m s−1, and 4 m s−1), and four spray solution formulations (water; Dipel®, Dipel® + Veget’Oil®, and Dipel® + Break Thru®). Artificial targets positioned transversely to the airflow were used to collect spray deposition and, after spraying, were divided into lower, middle, and upper thirds according to the height of the test section. Data were obtained by spectrophotometry and, after verification of the ANOVA assumptions, were subjected to analysis of variance (p < 0.05). When significant effects were observed, regression analyses were applied. Statistical analyses were conducted using the R and Sisvar software packages. Mean deposition values were converted into deposition percentage as a function of the total sprayed volume. The experimental data were also subjected to geostatistical analysis using GS+ software (Version 7®). After confirming spatial dependence, contour maps were generated using kriging. Higher wind speeds led to higher deposition percentages. The use of adjuvants affected spray deposition in the upper and middle thirds, with responses depending on the spray solution composition. Spray deposition in the wind tunnel can be analyzed using geostatistics, as this variable showed a high degree of spatial variability across all treatments evaluated.

AgriEngineering doi: 10.3390/agriengineering8060243

Authors: Diego F. Restrepo Enrique M. Combatt Manuel Palencia

A comprehensive multiregional characterization of the spectral response of cassava leaves across different ontogenetic stages was performed. For this, ultraviolet (UV), visible (VIS) and shortwave near-infrared (UV-VIS-NIR; 200–900 nm) regions were used to identify spectral signatures and indices for their potential use as biomarkers of leaf development and physiological status of plants under induced senescence conditions. Manihot esculenta Crantz (HMC-1 variety) was used as a model. Spectral signatures were obtained from leaves at two phenological stages (4 and 6 months after planting) using UV-VIS-NIR spectroscopy by the diffuse reflectance technique. Classical and experimental spectral indices were evaluated, and their discriminatory power through different ontogenies was assessed using ANOVA/Kruskal–Wallis and post hoc tests. Senescence effects were further examined by postharvest monitoring (1–20 days), with temporal, ontogenetic, and interaction effects validated using linear mixed models (LMMs), while multivariate structure and spectral convergence were explored via principal component analysis and hierarchical clustering (PCA-HCA). Functionally Enhanced Derivative Spectroscopy (FEDS), comparative analysis, and spectral correlation mapping allowed signal’s selective enhancement and the identification of phenolic compounds, photosynthetic pigments, and structural molecular components. Results showed high ontogenetic stability of UV-associated phenolic signals (~210–220 nm), whereas the VIS region (420–600 nm) clearly differentiated young leaves. The NIR region was stable across ontogeny but highly sensitive to temporal degradation, reflecting changes in water status and internal structure. UV-VIS-NIR indices effectively differentiated young leaves and changes by stress. It is concluded that multiregional characterization of the spectral response supported by FEDS allows the extraction of robust indices with strong potential as biomarkers of leaf maturation and senescence in cassava.

AgriEngineering doi: 10.3390/agriengineering8060242

Authors: Vicente López-Sacanell Lluís Miquel Plà-Aragonés

Precision Livestock Farming (PLF) requires real-time control systems that connect high-level Decision Support Systems with resource-constrained edge devices. This paper presents a hybrid Multi-Agent System (MAS) architecture for swine precision feeding designed to address the trade-off between semantic interoperability and real-time operational efficiency. The proposed Controlling Module uses a dual-layer communication strategy: a lightweight character-delimited TCP/IP protocol ensures deterministic performance for embedded controllers, while an XML-serialized format that maps to the FIPA Agent Communication Language preserves semantic interoperability. A custom serialization/deserialization algorithm was developed to process this XML structure within LabVIEW while avoiding the overhead typically associated with generic DOM/SAX parsers. The architecture was validated in a 120 h laboratory test that combined a Digital Twin simulation of 50 virtual feeders with Hardware-in-the-Loop testing of key sensing components. Under these test conditions, no communication failures were observed, all simulated network interruptions were recovered from, and the system operated with a modest resource footprint, including an average CPU use of 15% and a peak memory use of 350 MB. The platform also processed 2590 consumption events without reported data loss during the validation period. These results indicate that the proposed hybrid MAS architecture is a feasible solution for integrating interoperable decision support and deterministic edge control in PLF applications.

AgriEngineering doi: 10.3390/agriengineering8060241

Authors: Anton Kuvaev Alexey Derepaskin Ivan Tokarev Yurij Binyukov Yurij Polichshuk Pavel Ivanchenko Alexander Semibalamut

The experimental determination of the relationships between the stress distribution zone in the soil layer and the parameters of tillage working bodies is a labor-intensive process. Therefore, preliminary mathematical modeling of this process is recommended to minimize the total number of experiments. The research was conducted using the principles of classical mechanics and soil mechanics. Using an equation proposed by J. Boussinesq, a graphical–analytical method was developed to evaluate the stress state in the soil layer induced by a dihedral wedge. This method incorporates both the geometric parameters of the dihedral wedge and the physico-mechanical properties of the soil. A direct proportional relationship was established between the length of the dihedral wedge and the total area of the deformed soil mass. Specifically, increasing the length of the dihedral wedge by 83% (from 0.05 to 0.30 m) resulted in an 80% increase in the area of the deformed soil mass (from 0.02 to 0.10 m2). The proposed graphical–analytical method can be employed in the design of tillage implements. The results we obtained are consistent with the patterns previously reported by other researchers. The findings were used in the development of various types of flat-cutting working tools for shallow and deep tillage.

AgriEngineering doi: 10.3390/agriengineering8060240

Authors: Yuankun Zheng Yulong Ding Jizhong Wang Hanlu Jiang Weipeng Zhang Hongze Guo Shenghe Bai Liming Zhou Kang Niu Lijing Liu

To address the difficulty of real-time detection of seed-filling performance in pneumatic suction seed metering devices under high-speed operation—where seed targets are tiny, prone to adhesion, and affected by motion blur—this paper proposes a lightweight online detection algorithm, YOLOv8n-MA. First, according to the seed adsorption characteristics of the suction holes, the detection targets are divided into three categories: none, one, and two. Second, based on YOLOv8n, the backbone network is replaced with MobileNetV1 to reduce computational cost, and an ACmix attention module is integrated into the Neck to enhance feature representation for the three suction-hole states. Finally, to meet the demand for low-latency inference on resource-constrained devices, the model is deployed on an edge computing controller to achieve real-time detection. Experimental results show that, compared with the original YOLOv8n, the parameters and FLOPs of YOLOv8n-MA are reduced by 34.4% and 59.8%, respectively, while the mean average precision (mAP) is improved by 2.0% to 96.8%, achieving a superior trade-off between accuracy and efficiency over other detection models of the same category, such as YOLOv5n, YOLOv9n, and YOLOv10n. In field tests, the detection accuracy reaches 95.02% at 12 km/h and 92.65% at 15 km/h. The proposed method provides effective technical support for the intelligent monitoring and control of precision seeding under high-speed operation.

AgriEngineering doi: 10.3390/agriengineering8060239

Authors: Marco Bietresato Adriano Biason Rino Gubiani Angelo Montanari

Italian “Agricultural Engineering”, while evolving toward the broader, interdisciplinary field of “Biosystems Engineering” (which also includes the study of biomasses/biomaterials, field and forest mechanization in difficult contexts and advanced post-harvest agri-food technologies), suffers from a structural critical issue due to its historical academic placement within the Agricultural rather than the Engineering departments. This positioning limits the depth of the technical subjects proposed to the students and does not facilitate the necessary collaboration with core engineering disciplines in research and didactics activities, thereby potentially slowing innovation in crucial fields like agro-bio-energies, precision agriculture and field robotics. To address this misalignment and foster inter-departmental synergy, this study proposes adopting the Contamination Lab (C-Lab) model as the archetype of a possible framework of academic and professional networking involving and centered on Agricultural Engineering. C-Labs (transdisciplinary platforms proposed by the Italian Ministry of University and Research) function as experiential laboratories, gathering students from Engineering, Agronomy, Computer Science, and Economics to collaboratively develop solutions to real-world challenges posed by industry stakeholders. The integration of a permanent, thematic C-Lab focused on agri-forestry and food machinery, supported by methodologies for enhancing creativity in technical fields, such as design thinking, represents an effective (and necessary) strategy to give Agricultural Engineering greater visibility in the Italian (and international) scenario and, prospectively, relocate it to the center of any research involving the technological and technical aspects of agriculture, forestry and food production. It is concluded that this initiative can serve as an institutional bridge for hybrid training, which is essential for aligning academic competencies with the growing demands for innovation and multidisciplinary professionalism in the national agri-food tech sector.

AgriEngineering doi: 10.3390/agriengineering8060238

Authors: Khambay Phomphakdy Rapeepat Techarungruengsakul Ratsuda Ngamsert Haris Prasanchum Jirawat Supakosol Kantiya Sanusan Ounla Sivanpheng Phetyasone Xaypanya Anongrit Kangrang

Sustainable irrigation planning under increasing water scarcity requires quantitative optimization tools to balance land and water resources. This study develops a linear programming (LP)-based framework to determine optimal cropping patterns under variable seasonal water availability in three irrigation projects in Lao PDR: Nam Tong 2 (1000 ha; ≈48.16 million m3 (MCM)), Nam Hin (80 ha; ≈0.73 MCM), and Xe Salalong (1530 ha; ≈30.80 MCM). Six major crops were analyzed for each project, with crop water requirements ranging from 4000 to 12,000 m3 ha−1 and gross revenues from 1200 to 41,322 US$ ha−1. Eight irrigation scenarios were constructed by combining land suitability (suitable vs. unsuitable), crop water requirement levels, and gross revenue assumptions. The model maximizes total gross revenue subject to seasonal water and land constraints. The results indicate that under limited water availability (e.g., 5.35–6.20 MCM in Nam Tong 2), crops with lower water demand (≤6000 m3 ha−1) and higher economic return per unit of water are prioritized, improving water-use efficiency. As water availability increases, high-value but water-intensive crops expand until land suitability becomes the dominant constraint. Expanding irrigation on unsuitable land produces diminishing economic returns. The framework enhances the realism of irrigation planning and supports economically efficient, water-sustainable crop allocation in water-scarce regions.

AgriEngineering doi: 10.3390/agriengineering8060237

Authors: Youwen Yang Seyit Uguz Pradeep Kumar Robert Thaler Xiaoyu Feng Xufei Yang

As livestock production continues to consolidate into fewer but larger operations, odor complaints from neighboring communities have become a major challenge to industry growth, making the establishment of appropriate odor setback distances essential. This paper reiterates the development procedure of odor footprint tools for setback determination based on AERMOD, a regulatory air dispersion model, using North Dakota as an example. Specifically, we developed North Dakota Odor Footprint Tool (NDOFT), an Excel-based calculator designed to estimate odor setback distances between animal production facilities and surrounding communities. The tool utilizes county-specific meteorological data to predict odor concentrations at various distances and directions relative to an established annoyance threshold of 75 OU m−3. Setback distances are determined based on the percentage of time during which modeled odor concentrations remain below this threshold, corresponding to annoyance-free frequencies ranging from 91% to 99%. Facility characteristics, including livestock types, source areas, and odor control measures, are incorporated to enable scenario-based assessments. The influence of complex terrain on setback determination was also evaluated, revealing that no simple correction factors adequately capture terrain effects for valleys and hilltops. Overall, the use of county-specific meteorological inputs substantially improves the accuracy of predicted setback distances compared with area-representative approaches, providing an updated and more robust framework for odor setback planning and environmental evaluation. This work is expected to guide future efforts in developing and refining odor setback tools.

AgriEngineering doi: 10.3390/agriengineering8060236

Authors: Adil Ibrayev Amangeldy Sarsenov Zhanna Kubasheva Yerzhan Arystanov Khozhakeldi Tanbayev Nazgul Khairova Arailym Tureshova

The paper outlines soil–tool interaction investigations according to parameters of the trencher disc cutter with a variable installation angle relative to the rotation axis that ensure the required trench shape and dimensions. The research results make it possible to improve the quality of the technological process for obtaining the needed trench shape. The movement of soil particles on the knife surface and after their removal was considered using the principles of soil mechanics, mathematical analysis, and computer (3D) modelling taking into account centrifugal force, gravity and friction. Research has shown that the soil particles’ movement is spatially complex and can be described by parabolic dependencies when projected onto coordinate planes. It is proved that changing the angle of installation of the disc in the range of 90–80° allows the furrows’ width to be adjusted within the range of 0.1–0.5 m while maintaining the required depth of cultivation. The reduction indicators of trench depth that are dependent on changing the disc installation angle were also determined. The obtained dependencies, design and technological recommendations can be used in designing of planting machines for garden and forest crops, as well as in the justification of rational operating modes for them in intensive horticulture conditions.

AgriEngineering doi: 10.3390/agriengineering8060235

Authors: Anindita Das Yong Yang Vinitha Hannah Subburaj

Weed detection is an important part of precision agriculture because it allows farmers to manage weeds more efficiently and reduce unnecessary herbicide use. With the use of UAVs, it is now possible to capture high-resolution images of agricultural fields, but identifying weeds from these images is still challenging due to complex backgrounds, lighting variations, and the visual similarity between crops and weeds. In this study, an improved YOLOv7-based approach is developed to address these challenges using UAV imagery collected from rainfed cotton fields in the Texas Panhandle. The original dataset consisted of high-resolution UAV images, which were divided into smaller patches and manually annotated to label weed and cotton classes. After cleaning the dataset and applying simple augmentation techniques, a total of 8396 images were used for training and testing. To improve detection performance, two modifications were introduced: Convolutional Block Attention Module (CBAM) to help the model focus on important features and Bidirectional Feature Pyramid Network (BiFPN) to improve how information is shared across different scales. Three models—YOLOv7-CBAM, YOLOv7-BiFPN, and the combined CB-YOLOv7—were evaluated. The results show that CBAM helps detect more weed instances, BiFPN reduces false detections, and the combined model gives the best overall performance, achieving an mAP@0.5 of 0.89 and an F1-score of 0.84. Overall, the study shows that improving both the dataset and the model can lead to more reliable weed detection under real field conditions. The proposed approach can be useful for identifying weeds in cotton fields using UAV imagery and can support better crop management and more efficient use of herbicides in precision agriculture.

AgriEngineering doi: 10.3390/agriengineering8060234

Authors: Ruxiao Bai Haixiu He Zhibo Zhong Limin Yu Xiuqing Fu Qifeng Wu

Global climate change and soil salinization pose challenges to licorice cultivation. Evaluating seed vigor based on the dynamic changes in radicle morphology is crucial for screening and cultivating licorice varieties that are tolerant to low temperatures and salts. Traditional manual measurement of licorice radicle characteristics suffers from issues such as high cost, long time consumption, and large errors. The YOLOv11 instance segmentation model in the field of deep learning offers advantages including a simple architecture, strong lightweight properties, and a unified detection-segmentation framework. Therefore, this study selected the YOLOv11 model to build a deep learning framework and used the continuous time-series crop growth vitality monitoring system to collect full-time-series images of 18 groups of licorice seeds germinating under different temperature and salt stress conditions. The YOLOv11-seg model was improved by adding a Spatial Strip Attention mechanism (SSA) to enhance the spatial correlation of radicle features, replacing ordinary convolutions with a Multi-scale Edge Detail Enhancement Module (MEEM) to optimize multi-scale feature extraction capabilities, and embedding a Normalized Weighted Distance (NWD) loss function to strengthen the segmentation ability for tiny targets. The YOLOv11-LicoSeg model was constructed for segmenting and extracting licorice radicle features and calculating root length. The experimental results showed that the mAP50 of the model’s detection reached 97.4%, mAP50–95 reached 81.7%, the mAP50 of the segmentation mask reached 97.0%, and mAP50–95 reached 78.2%. Compared with the unimproved YOLOv11-seg, the mAP50 of detection increased by 0.7%, mAP50–95 increased by 1.3%, the mAP50 of segmentation increased by 0.7%, and mAP50–95 increased by 0.8%. The linear regression coefficient between manual measurement and machine-vision measurement was 0.94218, and the goodness of fit R2 was 0.94408. Using this model and the monitoring system, the morphological evolution of the licorice radicle contour characteristics over the germination time was obtained. The study indicated that the growth of licorice radicles was optimal under salt stress of 1200 µs/cm and 1800 µs/cm. YOLOv11-LicoSeg accurately segmented licorice radicles and calculated radicle length, with the performance to segment 100 licorice radicle images within 7 s. After deployment, it significantly reduced the labor cost and time consumption for acquiring licorice radicle phenotypes. In conclusion, YOLOv11-LicoSeg provides a rapid and accurate method for variety screening in licorice breeding and cultivation.

AgriEngineering doi: 10.3390/agriengineering8060233

Authors: Kyler C. Bingham Taher Deemyad

Unmanned aerial vehicles (UAVs) are widely used for monitoring and payload transport; however, their application in autonomous physical interaction remains limited due to payload constraints, stability challenges, and the complexity of integrating manipulation systems. This study presents the design and development of a lightweight foldable robotic arm based on the ten-bar Kempe Kite Inversor II linkage for UAV aerial manipulation. The mechanism generates precise straight-line motion using a single degree of freedom. Kinematic modeling and simulation validated a maximum end-effector reach of approximately 0.42 m. Structural optimization using additive manufacturing and honeycomb cellular architectures significantly reduced system weight while maintaining mechanical reliability. A passive compliant gripper, counterbalance mechanism, onboard storage net, and landing gear were integrated to evaluate the arm in a practical harvesting scenario using cherries as the test object. The final integrated system weighs 0.351 kg during operation, remaining approximately 16% below the experimentally determined UAV payload limit of 0.4185 kg. Proof-of-concept flight demonstrations confirmed successful aerial grasping of cherries, validating the feasibility of the proposed lightweight manipulation approach for agricultural applications.

AgriEngineering doi: 10.3390/agriengineering8060232

Authors: Isabel Felizardo Chambingo Matheus de Godoi Bertin Wilson Manuel Castro Silupu Murilo Mesquita Baesso Lilian Elgalise Techio Pereira Adriano Rogério Bruno Tech

Smartphone-based proximal sensing has emerged as a promising low-cost approach for pasture monitoring. A critical component of this methodology is accurate leaf segmentation, as it directly affects the reliability of subsequent image-based analyses. Despite advances in computer vision, the role of preprocessing strategies in segmentation performance remains insufficiently explored, particularly under resource-constrained conditions. This study presents a systematic comparative evaluation of three preprocessing pipelines based on HSV and CIELab color spaces for the segmentation of Urochloa grass leaves (Urochloa hybrid Mavuno and Urochloa decumbens) using smartphone imagery acquired field conditions. The pipelines were assessed using a multi-criteria framework, including the Fisher Discriminant Ratio (FDR), Intersection over Union (IoU), Overlap Error (OE), Structural Similarity Index (SSIM), and Edge Preservation Index (EPI), complemented by discordance map analysis. The results demonstrate that preprocessing design significantly influences segmentation stability, boundary preservation, and robustness to illumination variability. Pipelines based on HSV channels showed high sensitivity to shadows and non-uniform lighting, leading to reduced segmentation consistency. In contrast, the CIELab-based pipeline relying on the a* channel achieved superior performance, with higher discriminative capacity, improved edge preservation, and lower computational cost. These findings highlight that carefully designed classical preprocessing strategies remain highly effective for low-cost, real-time applications, even in the absence of computationally intensive models. This work establishes a robust segmentation foundation for future integration with advanced analytical methods, including machine learning approaches, and supports the development of scalable smartphone-based tools for pasture monitoring.

AgriEngineering doi: 10.3390/agriengineering8060231

Authors: Wei Zhao Bangbo Liu Yang Pan Xiaobiao Shang Tianle Shi Xi Xu Hongfu Zhang

This paper proposes a multi-sensor fusion autonomous navigation method integrating a nine-axis IMU, the Leishen C16 mechanical LiDAR, and the LakiBeam1L single-line LiDAR, aimed at addressing issues such as track slippage and positioning drift that commonly occur in tracked chassis operating under continuously changing conditions on hilly slopes and farmland. IMU-derived slope and attitude information is used as a terrain prior and incorporated into adaptive ground segmentation, slope-cross-slope path cost modeling, and velocity regulation. Leishen C16 LiDAR point clouds are used for NDT scan-to-map localization and spatial obstacle representation, while the LakiBeam1L LiDAR establishes a velocity-dependent near-field safety zone for dynamic obstacle triggering and local avoidance. Python simulations were conducted in simple, general, and complex environments under five slope conditions, forming 15 environment-slope combinations. Three representative scenarios were further validated in ROS/Gazebo. To strengthen statistical reliability, 10 repeated trials were performed for each environment-slope-algorithm combination, and additional stress tests included obstacle-position perturbation, sensor noise perturbation, initial-pose perturbation, dynamic obstacle speed perturbation, and variable slope/local undulation perturbation. An isolated no-LakiBeam1L ablation, significance tests, IMU perturbation tests, planning-weight sensitivity analysis, and stronger-baseline comparison were also added. In the repeated-trial dataset, the proposed method improved the arrival rate from 23.3% to 94.7%, reduced tracking RMSE by 61.46%, reduced localization RMSE by 60.62%, and increased obstacle recall by 26.32%. Under mixed perturbations, the arrival rate of the proposed method was 81.3%, compared with 29.3% for the baseline. These results indicate improved simulation-level stability and perception reliability, while the applicability to real hilly farmland still requires hardware and field validation.

AgriEngineering doi: 10.3390/agriengineering8060230

Authors: Rihab Boukouba Dalenda Ben Aissa Amira Guidara Nadia Smaoui Chantal Ebel

Drought stress significantly impacts barley (Hordeum vulgare L.) production, necessitating early and accurate detection systems for precision agriculture. Traditional monitoring approaches rely on manual inspection or single-modality sensing, which often fail to capture the complex physiological responses to water deficit. This study presents a novel multimodal deep learning framework that integrates RGB imaging with environmental sensor data (temperature and humidity) for real-time drought stress classification in barley plants. The proposed architecture employs EfficientNetV2-S for visual feature extraction, coupled with a dedicated sensor encoding branch, unified through a cross-modal attention mechanism and gated multimodal fusion strategy. To address the computational constraints of agricultural IoT systems, we implemented comprehensive CPU optimization techniques and model compression via TensorFlow Lite INT8 quantization, achieving a 68.5% reduction in training time and 90% reduction in model size. The system was validated on a custom greenhouse dataset (379 samples, 80/20 split) and the PlantVillage dataset (26,000 images, binary reformulation). A 10-seed evaluation protocol demonstrated that the full multimodal model achieves 98.3 ± 1.5% accuracy, outperforming both an image-only baseline (97.4 ± 1.8%) and a sensor-only MLP (73.8 ± 3.5%). Across seeds, the model also achieved an F1-score of 98.34 ± 1.48% and ROC-AUC of 99.93 ± 0.13%. Ablation analysis with ANOVA (F(4,36) = 4.44, p = 0.005) confirmed that multimodal fusion improves accuracy by 0.92% over image-only models, with the full gated cross-modal attention mechanism outperforming all simplified baselines, including AgriFusionNet (75.22%), Shallow CNN (92.54%), Logistic Regression multimodal (92.11%), and Random Forest multimodal (89.91%). These results further show that relying on environmental data alone is insufficient, reinforcing the benefit of multimodal fusion. External validation on PlantVillage achieved 99.97% accuracy, demonstrating strong generalization capabilities. The optimized model operates efficiently on CPU-only hardware (training time: 9.1 min/epoch), making it suitable for edge deployment in resource-constrained agricultural environments. This work demonstrates that a low-cost, CPU-compatible multimodal deep learning system can reliably detect drought stress in barley under real greenhouse conditions and provides a practical and scalable solution for early stress monitoring in precision agriculture.

AgriEngineering doi: 10.3390/agriengineering8060229

Authors: Shaoran Li Weiquan Zhao Miao Hao Sisi Lv Hongliang Zhang Jiafang Yang Jiayi Li Zhaowei Cui

Accurate identification of tea diseases and pests is a key challenge in smart agriculture. Current approaches to tea disease and pest identification suffer from a scarcity of high-quality annotated image data and poor generalization of existing models in real-world field environments. To address these issues, this paper first constructs and releases a dataset of images of tea diseases and pests captured in real-world field scenarios. The dataset uses leaf-level annotations and covers six common tea disease and pest categories in Guizhou Province, China. It contains 549 high-resolution images covering varying lighting conditions, backgrounds, and disease severity levels. Based on this dataset, we propose a convolutional neural network model named GatedFCA-YOLO, which integrates a small-object detection layer with an adaptive attention mechanism. Specifically, the small-object detection layer preserves high-resolution details, effectively improving recall of minute lesions. Meanwhile, the GatedFCA module is designed to fuse a spatial gating mechanism with FCAttention. It enables adaptive feature enhancement and significantly boosts the model’s recognition robustness under complex backgrounds. Experimental results on our dataset show that GatedFCA-YOLO achieves 78.9% mAP@0.5, which is 3% increased compared to the baseline model YOLO11n, thereby verifying the effectiveness of the proposed method.

AgriEngineering doi: 10.3390/agriengineering8060228

Authors: Sagar Regmi Cody M. Allen

Crop residue management is an important factor in sustainable agriculture as it impacts soil erosion, water retention, soil organic matter, and crop yield. Accurately measuring the crop residue cover helps in the strategic planning, control, and monitoring of crop residue. While advancements in machine learning have allowed for significant progress in crop residue classification work, a major challenge still exists in the creation of an accurately annotated dataset for crop residue and the application of segmentation-based models to accurately segment crop residues. This study aims to develop an efficient image annotation framework and evaluate deep learning models for crop residue cover estimation. For this, the Residue Segmentation Tool, a standalone graphical user interface, was designed to facilitate accurate and efficient image annotation that enables flexible and high-throughput annotation of residue images. The tool is publicly available and supports multiple segmentation modes, which include classical and modern computer vision algorithms such as Otsu, Canny, and manual thresholding, as well as the Segment Anything Model and user-guided mask refinement through manual editing options. This tool was also utilized to create annotated datasets for machine learning training and testing of crop residue cover estimation. Three different sizes of datasets (100, 250, and 500 images) were utilized for machine learning training and testing to evaluate the performance of the models trained using U-Net and DeepLabV3. U-Net consistently outperformed DeepLabV3 across most metrics, particularly on smaller datasets, showing better Dice, IoU, and Recall scores. The best-performing model had Dice, IoU, and Accuracy scores of 0.748, 0.627, and 0.864, respectively. The findings demonstrate that the Residue Segmentation Tool enables scalable and reproducible dataset creation and supports effective segmentation for crop residue cover estimation.

AgriEngineering doi: 10.3390/agriengineering8060227

Authors: Samin Dahal Bidur Paneru Anjan Dhungana Lilong Chai

US egg production is undergoing a transition to cage-free (CF) housing systems. This transition has increased the need for automated monitoring tools to support welfare management and reduce production costs. While CF houses allow hens to perform natural behaviors such as dust bathing and foraging, a persistent challenge is severe feather pecking. Pecking block enrichment is used as a managemental approach to control severe feather pecking. However, manual quantification of such behavior is subjective and labor-intensive. This study evaluated the performance of small and large variants of both YOLOv10 and YOLO11 models for automatic detection of enrichment block pecking behavior in CF research environment. A total of 1061 color images were used to train and evaluate the models using 70:20:10 split for training, validation, and testing. Performance was assessed using precision, recall, mean average precision at 50% intersection over union (mAP50), confusion matrices, and F1–confidence curve. All models demonstrated robust performance, with precision, recall and mAP50 values greater than 0.94. YOLO11l achieved the highest precision with 0.969 and mAP50 with 0.988, while YOLOv10s achieved the highest recall of 0.962. Evaluation on test datasets showed robust generalization capability of the model, with high confidence detections. Overall, the findings show that YOLO models provide a consistent, objective, and scalable method for automatic quantification of pecking enrichment block related pecking behavior in a CF system. It offers potential as an automated monitoring tool for poultry researchers and may support future development of tools for commercial CF system.

AgriEngineering doi: 10.3390/agriengineering8060226

Authors: Taranjeet Singh Sodhi Shekhar Thapa Canicius Mwitta Glen C. Rains

The objective of this study was to design, fabricate, and test an automated inter-row robotic system for the precision management of Palmer amaranth (Amaranthus palmeri) in cotton. A Farm-ng robotic platform with custom-designed weed pulling and cutting attachments was used to achieve weed control. The pulling system consisted of two counter-rotating rollers with a frictional cover to uproot weeds, followed by a cutting operation to shred the weeds into smaller pieces, preventing regrowth. A deep learning model, YOLOv11s, was used for weed identification, while point cloud data from a stereo camera was used to estimate weed height in real-time for dynamic adjustment of the puller height. The system was evaluated at three forward speeds (0.06, 0.15, and 0.25 m/s), two roller speeds (107 and 161 RPM), and three attachment configurations (puller-only, cutter-only, and combined). The combined configuration consistently outperformed individual operations, achieving 80% control at 0.15 m/s and a roller speed of 161 RPM. Optimal performance was observed when the angular puller velocity was 15–25 times the forward speed of the rover. This approach demonstrates the potential of integrating mechanical weed removal with real-time computer vision to improve weed management and reduce labor requirements.

AgriEngineering doi: 10.3390/agriengineering8060225

Authors: Reda Hassanien Emam Hassanien Mohamed M. Ibrahim Gang Pei Eid N. Abd El Rahman

Providing optimal temperatures in greenhouses is essential for cultivating high-temperature-demand crops in winter. Therefore, this study aimed to investigate the feasibility of utilizing a flat plate solar collector (FPC) for heating greenhouses. A field experiment was conducted, complemented by simulations using the PolySun V2023.11 software. The FPC system comprised two collectors, each with an aperture area of 2.24 m2, connected to a 300 L hot water tank. The water tank had an internal electric backup heater (2 kW) and a thermostat to regulate the hot water temperature. The experiment consisted of two greenhouses, each with an area of 50 m2. The first unheated greenhouse (UHGH) was used as the control, while the second heated greenhouse (HGH) was heated by a closed-loop system comprising copper pipes installed along the internal perimeter. Results revealed that the FPC significantly increased air temperature by 2.7 °C, and reduced relative humidity by 9.7% in the HGH compared to the UHGH. Simulated results showed that the annual generated energy of the FPC was 4830 kWh with a reduction of CO2 emission by ≈2.9 tones. The average thermal efficiency of the FPC was 44%, with a payback period of 8.5 years. In conclusion, the FPC could protect plants from low temperatures in winter.

AgriEngineering doi: 10.3390/agriengineering8060224

Authors: Matheus de Freitas Souza Éder Vaz de Almeida Junior Eugenio Borkowski Franco de Paula Basílio Guilherme Braga Pereira Braz Lais Tereza Rego Torquato Reginaldo Eduardo Lima do Carmo Hamurábi Anízio Lins

Spectral sensors have expanded the opportunities for the non-destructive monitoring of crops and weeds. However, the lack of standardized and accessible analytical pipelines remains a major limitation for data reproducibility and integration in this field. EasySpectra was developed to address these challenges by providing a unified environment that integrates data import, radiometric calibration, geometric alignment, spectral pre-processing, region-of-interest selection, feature extraction, vegetation index computation, and dataset construction. A graphical user interface guides users through the entire analytical workflow, reducing technical barriers for non-experts. EasySpectra supports heterogeneous data sources, including single-band images, spectral cubes and georeferenced orthomosaics. Across 100 sampled areas, the correction + normalization workflow in EasySpectra produced NDVI values very close to Pix4DFields (0.70 ± 0.052 vs. 0.69 ± 0.055), with a pixel-wise correlation of up to 0.98 and low bias (MBE = 0.05). In an independent UAV dataset, EasySpectra also showed close agreement with WebODM, with NDVI values ranging from 0.09 ± 0.10 to 0.42 ± 0.08 versus 0.08 ± 0.13 to 0.43 ± 0.10, across 13 sampled areas. In addition, hyperspectral species classification using EasySpectra-extracted profiles achieved a Macro F1-score of 0.880, with class-wise accuracies ranging from 0.83 for canola to 0.95 for redroot pigweed. Overall, EasySpectra enables reproducible, transparent, and standardized spectral analysis.

AgriEngineering doi: 10.3390/agriengineering8060223

Authors: Antonios Chatzisavvas Minas Dasygenis

The A* algorithm is widely adopted across agriculture, robotics, and GPS navigation for efficient route planning, yet it faces challenges in balancing search efficiency with path quality. To address these limitations, we introduce Octile–Annealed, a novel heuristic that augments the classic Octile distance with a distance-dependent annealing weight. Specifically, Octile–Annealed scales the Octile metric by a smooth function of the current node’s Euclidean distance to the final location, yielding a heuristic that is gentle near the target and more directive when far away. This design retains the geometric fidelity of Octile, accelerates search convergence in open regions, and preserves guidance in constrained corridors. Beyond discrete planning, we incorporate adaptive Bézier smoothing to post-process the grid path into a collision-free, curvature-friendly trajectory. This is particularly relevant in agricultural environments (e.g., orchard rows and cross-aisles), where machines must follow efficient routes without abrupt turns that could slow operations or risk crop damage. We benchmark Octile–Annealed against three established baselines—Euclidean and Octile—on orchard-like grids of varying sizes and obstacle patterns. The results show that Octile–Annealed consistently reduces computation time while maintaining competitive raw path lengths and producing short, smooth Bézier trajectories. Overall, the proposed heuristic enhances A*’s operational efficiency and route quality, making it well-suited for complex, structured agricultural layouts and for general navigation tasks that benefit from smooth post-processing. However, it must be acknowledged that these comparative performance metrics are strictly limited to simulated grid cases; consequently, comprehensive validation using actual field data remains necessary to fully confirm their practical applicability under real-world agricultural conditions.