AgriEngineering, Volume 7, Issue 10 (October 2025) – 36 articles

The main objective of the current work is to evaluate the effect of adding biochar obtained by pyrolysis of a mixture of greenhouse waste to agricultural soil, measuring its effectiveness as an amendment. A mixture of broccoli, zucchini, and tomato plant residues was pyrolyzed in a lab-scale reactor at 450 °C, obtaining a biochar yield of 35.6%. No carrier gas was used in the process. A thorough characterization of the biochar obtained was performed, including elemental and proximal analysis, density, pH, electrical conductivity, cation exchange capacity, surface area, and metal content. Since the raw material had a high percentage of ash (approximately 20%), the resulting biochar contained around 50% inorganic matter, with potassium and calcium being the major metals detected (10–11%). This biochar had a 29% fixed carbon content, a high heating value of 11.5 MJ kg⁻¹, a cation exchange capacity of 477 mmol kg⁻¹, and an electrical conductivity of 16 mS cm⁻¹.The biochar was mixed with greenhouse soil and fertilizer to form a substrate to grow bean seeds, the crop selected for the study. Different experiments were carried out, varying the biochar, fertilizer, and soil percentages. By adding 0.5% biochar to a substrate containing 1% fertilizer, the bean production was increased by 24.5%. It is worth noting that by adding only 0.5% biochar to soil, the bean production reached higher values than when adding 1% fertilizer. Biochar produced from the studied biomass improved the productivity of agricultural soils. The avoidance of selective collection by farmers as well as the non-use of carrier gas in the pyrolysis process made the implementation of the pyrolysis system in situ easier. Consequently, this research has great potential for practical application in modest agricultural areas. Full article

The electromagnetic induction (EMI) and frequency domain reflectometry (FDR) sensors, which measure the soil apparent electrical conductivity (ECa) in situ, have emerged as efficient and rapid tools for the indirect assessment of soil salinity, conventionally determined by the electrical conductivity of the saturated soil paste extract (ECe). However, the limitations of applying a single soil sensor and the ECa dependence on multiple soil properties, such as soil moisture and texture, can hinder the interpretation of ECe, whereas selecting the most appropriate set of sensors is challenging. To address these issues, this study explored the prediction ability of a noninvasive EM38-MK2 (EMI) and a capacitance dielectric WET-2 probe (FDR) in assessing topsoil salinity and texture within 0–30 cm depth across diverse soil and land-use conditions in Laconia, Greece. To this aim, multiple linear regression models of laboratory-estimated ECe and soil texture were constructed by the in situ measurements of EM38-MK2 and WET-2, and their performances were individually evaluated using statistical metrics. As was shown, in heterogeneous soils with sufficient wetness and high salinity levels, both sensors produced models with high adjusted coefficients of determination (adj. R² > 0.82) and low root mean square error (RMSE) and mean absolute error (MAE), indicating strong model fit and reliable estimations of topsoil salinity. For the EM38-MK2, model accuracy improved when clay was included in the regression, while for the WET-2, the soil pore water electrical conductivity (ECp) was the most accurate predictor. The drying soil surface was the greatest constraint to both sensors’ predictive performances, whereas in non-saline soils, the silt and sand were moderately assessed by the EM38-MK2 readings (0.49 < adj. R² < 0.51). The results revealed that a complementary use of the contemporary EM38-MK2 and the low-cost WET-2 could provide an enhanced interpretation of the soil properties in the topsoil without the need for additional data acquisition, although more dense soil measurements are recommended. Full article

Precision agriculture technologies (PATs) are revolutionizing the agricultural sector by minimizing the reliance on plant protection products (PPPs) in crop management. This approach integrates a broad range of advanced solutions employed to help farmers in optimizing PPP application, while minimizing input and maintaining effective crop protection. These technologies include sensors, drones, robotics, variable rate systems, and artificial intelligence (AI) tools that support site-specific pesticide applications. The objective of this review was to perform a bibliometric analysis to identify scientific trends and gaps in this field. The analysis was conducted using Scopus and Web of Science databases for the period of 2015–2024, by applying a data filtering process to ensure a clean and reliable dataset. The methodology involved citation, co-authorship, co-citation, and co-occurrence analysis. VOSviewer software (version 1.6.20) was used to generate maps and assess global research developments. Results identified AI, sensor, and data processing categories as the most central and interconnected scientific topics, emphasizing their vital role in the evolution of precision spraying technology. Bibliometric analysis highlighted that China, the United States, and India were the most productive countries, with strong collaborations within Europe. The co-occurrence and co-citation analyses revealed increasing interdisciplinarity and the integration of AI tools across various technologies. These findings help identify key experts and research leaders in the precision agriculture domain, thus underscoring the shift toward a more sustainable, data-driven, and synergistic approach in crop protection. Full article

Crop–livestock integration is widely adopted as a strategy for recovering degraded pastures. In this system, intercropping crops such as sorghum with tropical grasses enables the harvest of sorghum for silage while simultaneously establishing a new pasture. However, interspecific competition for resources can limit sorghum development and yield, potentially compromise the fermentation process and reduce the nutritional quality of the silage. Therefore, this study aimed to evaluate the agronomic performance, fermentative characteristics, and chemical–bromatological composition of silages produced from different biomass sorghum-grass intercropping systems. The experiment was conducted in a randomized block design with a 3 × 2 factorial arrangement: three cropping systems [sorghum monoculture, sorghum intercropped with Marandu grass (S + M), and sorghum intercropped with Zuri grass (S + Z)] and two sorghum row spacings (45 and 90 cm). The S + Z intercropping system with 90 cm row spacing showed the highest total dry matter yield (16.42 t/ha). It also presented better fermentative parameters, such as pH (4.02) and lactic acid (5.31%DM) and superior nutritional quality, with lower fiber content and higher concentrations of NFC (24.79%DM), TDN (59.75%DM), and digestibility. It is concluded that intercropping biomass sorghum with Zuri grass at 90 cm spacing is the most promising strategy for producing high-quality silage. Full article

The use of saline water (low-quality water) in irrigation is a reality in many regions, especially in areas where fresh water is scarce, like semi-arid regions. However, it is important to adopt strategies to minimize the damage caused by salt stress to plants. The use of soil conditioners can help improve soil structure and water retention capacity, reducing salinity effects. The objective was to analyze the potential of a soil conditioner (hydrogel) as a mitigator of salty stress by irrigation with saline water in ornamental sunflower. Two sunflower cycles were carried out in a protected environment with a factorial 4 × 4 consisting of four doses of hydrogel polymer (0.0, 0.5, 1.0, and 1.5 g kg⁻¹) and four different levels of irrigation with saline water (0.5, 2.0, 3.5, and 5.0 dS m⁻¹). Plant biomass and physiological parameters, such as chlorophyll fluorescence measurements and gas exchange parameters, stomatal conductance, transpiration, and photosynthesis, were evaluated. Ornamental sunflower showed better performance with a saline water of 0.5 dS m⁻¹ without the use of hydrogel. At higher salinity levels, with a hydrogel dose of 1.5 g kg⁻¹, the sunflower achieved favorable performance, promoting gains in some gas exchange variables in plants irrigated with saline water at 3.5 dS m⁻¹ and in fluorescence-related variables within the range of 2.0 to 3.5 dS m⁻¹. This positive effect of hydrogel indicates its potential as a mitigating strategy against the adverse effects of salinity, contributing to the maintenance of plant vigor and physiological functionality in saline environments. Full article

Sustainable fertilizers are needed to improve nutrient efficiency and reduce environmental impacts. Greenhouse experiments were conducted to evaluate matrix-based organo-mineral fertilizers (OMFs) for Zea mays over 60 days. The study took place during the dry season in Jaboticabal, São Paulo, using 5.5 dm³ plastic pots. Biosolids, deinked paper sludge (cellulose), and sawdust were used as organic matrices. Four treatments (n = 6) were tested: BC (biosolids/cellulose), BS (biosolids/sawdust), FF (uncoated NPK), and NF (no fertilizer). FF received 4.0 g NPK (4-14-8) per pot in two split doses; BC and BS each received 2.0 g NPK entrapped in 2.0 g matrix, applied once at sowing. BC provided the most controlled nutrient release and outperformed FF, increasing plant height by 20.4%, stem diameter by 13.7%, and leaf area by 5.3%. Considering nutrient uptake, BC exceeded FF by 22.5% for N, 38.6% for P, and 22.7% for K while using half the mineral fertilizer. Overall, matrix-based OMFs improved Zea mays growth and nutrient assimilation and may reduce nutrient losses relative to conventional split applications. Because the results derive from a single dry-season greenhouse trial with pots, field-scale validation to the production stage is required to confirm agronomic performance and quantify economic and environmental benefits. Full article

Although considered relatively sustainable, mushroom production generates significant waste at the end of cultivation. This study investigated the reuse of Spent Mushroom Substrate (SMS) to formulate new substrates for Pleurotus ostreatus cultivation. Substrates with high (higher bran content) and low (lower bran content) nitrogen levels were prepared and supplemented with 5%, 10%, or 20% SMS across three successive cycles P. ostreatus crops. Cultivation performance was evaluated based on biological efficiency, number of mushrooms, fresh weight, and number of clusters. Substrates were chemically characterized for total nitrogen, carbon, C/N ratio, electrical conductivity, and pH. The inclusion of SMS, along with reduced bran content, did not improve P. ostreatus yield and led to lower productivity compared to control substrates. No consistent correlations were observed between chemical variables and yield, although high-N substrates generally performed better. SMS reuse, under these conditions, is not viable, but results encourage further research. Full article

This paper presents a novel hybrid and hierarchical architecture of a Convolutional Neural Network (CNN), based on MobileNetV2 and Support Vector Machines (SVM) for the classification of crop diseases (SVMobileNetV2). The system feeds from multispectral images captured by Unmanned Aerial Vehicles (UAVs) alongside data from IoT nodes. The primary objective is to improve classification performance in terms of both accuracy and precision. This is achieved by integrating contemporary Deep Learning techniques, specifically different CNN models, a prevalent type of artificial neural network composed of multiple interconnected layers, tailored for the analysis of agricultural imagery. The initial layers are responsible for identifying basic visual features such as edges and contours, while deeper layers progressively extract more abstract and complex patterns, enabling the recognition of intricate shapes. In this study, different datasets of tropical crop images, in this case banana crops, were constructed to evaluate the performance and accuracy of CNNs in detecting diseases in the crops, supported by transfer learning. For this, multispectral images are used to create false-color images to discriminate disease through spectra related to the blue, green and red colors in addition to red edge and near-infrared. Moreover, we used IoT nodes to include environmental data related to the temperature and humidity of the environment and the soil. Machine Learning models were evaluated and fine-tuned using standard evaluation metrics. For classification, we used fundamental metrics such as accuracy, precision, and the confusion matrix; in this study was obtained a performance of up to 86.5% using current deep learning models and up to 98.5% accuracy using the proposed hybrid and hierarchical architecture (SVMobileNetV2). This represents a new paradigm to significantly improve classification using the proposed hybrid CNN-SVM architecture and UAV-based multispectral images. Full article

In Northeast Brazil, climatic factors and technology synergistically enhance melon productivity and fruit quality. However, the region requires further research on the efficient use of water resources, particularly in determining the crop coefficient (Kc), which comprises the evaporation coefficient (Ke) and the transpiration coefficient (Kcb). Air temperature affects crop growth and development, altering the spectral response and the Kcb. However, the direct influence of air temperature on Kcb and spectral response remains underemphasized. This study employed unmanned aerial vehicle (UAV) with RGB and Red-Green-NIR sensors imagery to extract biophysical parameters for improved water management in melon cultivation in semiarid northern Bahia. Field experiments were conducted during two distinct periods: warm (October–December 2019) and cool (June–August 2020). The ‘Gladial’ and ‘Cantaloupe’ cultivars exhibited higher Kcb values during the warm season (2.753–3.450 and 3.087–3.856, respectively) and lower during the cool season (0.815–0.993 and 1.118–1.317). NDVI-based estimates of Kcb showed strong correlations with field data (r > 0.80), confirming its predictive potential. The results demonstrate that UAV-derived NDVI enables reliable estimation of melon Kcb across seasons, supporting its application for evapotranspiration modeling and precision irrigation in the Brazilian semiarid context. Full article

Fast and precise monitoring of soil organic matter (SOM) during maize growth periods is crucial for real-time assessment of soil quality. However, the big challenge we usually face is that many agricultural soils are covered by crops or snow, and the bare soil period is short, which makes reliable SOM prediction complex and difficult. In this study, an unmanned aerial vehicle (UAV) was utilized to acquire multi-temporal hyperspectral images of maize across the key growth stages at the field scale. The auxiliary predictors, such as spectral indices (I), field management (F), plant characteristics (V), and soil properties (S), were also introduced. We used stepwise multiple linear regression, partial least squares regression (PLSR), random forest (RF) regression, and XGBoost regression models for SOM prediction, and the results show the following: (1) Multi-temporal remote sensing information combined with multi-source predictors and their combinations can accurately estimate SOM content across the key growth periods. The best-fitting model depended on the types of models and predictors selected. With the I + F + V + S predictor combination, the best SOM prediction was achieved by using the XGBoost model (R² = 0.72, RMSE = 0.27%, nRMSE = 0.16%) in the R3 stage. (2) The relative importance of soil properties, spectral indices, plant characteristics, and field management was 55.36%, 26.09%, 9.69%, and 8.86%, respectively, for the multiple periods combination. Here, this approach can overcome the impact of the crop cover condition by using multi-temporal UAV hyperspectral images combined with valuable auxiliary variables. This study can also improve the field-scale farmland soil properties assessment and mapping accuracy, which will aid in soil carbon sequestration and soil management. Full article

This study investigated the effectiveness of a bacteriostatic-coated blister in preserving swine semen quality and its impact on reproductive performance. Two experiments were conducted: an in vitro assessment of the blister’s bacteriostatic efficacy and semen quality during three days of storage (Experiment 1), and a seven-day commercial farm trial evaluating its effect on reproductive outcomes in artificially inseminated gilts and sows (Experiment 2). In Experiment 1, the bacteriostatic blister effectively controlled bacterial proliferation, maintaining counts below 2 log10, comparable to controls with added antibiotics. Sperm quality parameters, including total and progressive motility, consistently exceeded the critical threshold for artificial insemination. Experiment 2 demonstrated that the bacteriostatic coating did not negatively affect key reproductive performance indicators, such as farrowing rate, total piglets born, or live piglets under commercial conditions. These findings suggest that the bacteriostatic-coated blister offers a viable, potentially antibiotic-free, alternative for semen preservation, extending storage viability for up to seven days. This technology supports sustainable reproductive practices, representing a significant advancement in commercial swine production. Full article

Optimising spraying operations in coffee cultivation can enhance both application efficiency and effectiveness. However, no studies have specifically assessed droplet deposition on leaves adjacent to the spray application band—fraction of droplet deposition referred to as ‘transfer’ in this study. Therefore, this study aimed to quantify droplet deposition and transfer resulting from different application rates and nozzle types in coffee trees. The experiment was conducted in a factorial design including three application rates (200, 400, and 600 L ha⁻¹) and two nozzle types (hollow cone and flat fan), with four replicates. Deposition was quantified at multiple positions: two application sides (left and right), three sections of the plant (upper, middle, and lower), and two branch positions (inner and outer). Thus, all measurements across sides, plant sections, and branch positions were nested, resulting in correlated data that were analysed using linear mixed-effects models (lme4 package), with parameters estimated using the restricted maximum likelihood method. The flat fan nozzle achieved the highest reference deposition, particularly on outer canopy thirds, while spray transfer (~29% of total deposition) was mainly driven by operational factors. Hollow cone nozzles at 200 L ha⁻¹ minimized transfer while maintaining adequate deposition. Optimizing applications requires maximizing reference deposition and minimizing transfer, which can be achieved through operational adjustments, airflow management, and complementary strategies such as adjuvants, electrostatic spraying, or tunnel sprayers. Full article

Use of dicamba, an auxin-mimic herbicide, has increased in recent years. Both the effects of dicamba on non-target plants and the determination of a biological model to determine the dicamba ecotoxicity dynamics are important to monitor the correct and safe use of this herbicide. The objectives of this study were to determine the effects of low doses (simulating herbicide drift) and to determine the acute toxicity of dicamba to aquatic bioindicator species (Lemna minor, Pomacea canaliculate, Hyphessobrycon eques, and Danio rerio) and terrestrial non-target plants (Cucumis sativus, Solanum lycopersicum, and Lactuca sativa) in tropical conditions. Measurements of acute toxicity of dicamba at the concentrations that cause 50% of symptoms of injury (LC50) and other biometric variables were performed. Dicamba was virtually non-toxic to all aquatic bioindicator species (LC50 > 118.0 mg L⁻¹), while it was highly toxic to all terrestrial non-target plants (LC50 < 0.5 mg L⁻¹). Severe injury symptoms (70% to 100%) caused by application of low doses of dicamba were found for all non-target terrestrial plants. Severe injury symptoms (70% to 100%) caused by volatilization of dicamba were found only for S. lycopersicum. Since S. lycopersicum was found as the most sensitive non-target plant, showing high injury symptoms caused by dicamba and significant injury from volatilized dicamba, this species is suitable for environmental monitoring of dicamba applications. Full article

Citrus fruits and related processed products represent a major agricultural sector worldwide, contributing to food supply chains and to regional economies, particularly in Mediterranean and subtropical areas. Citrus processing generates significant amounts of post-processing waste, and their sustainable management is a critical challenge, driving growing scientific interest in exploring environmentally sustainable and profitable valorisation strategies. This study aimed at mapping the sustainability of post-processing citrus valorisation strategies documented in the scientific literature, through a scoping literature review based on the PRISMA-ScR model. Only peer-reviewed studies in English were selected from Scopus and Web of Science; in detail, 29 life cycle assessment studies (LCAs) focusing on the valorisation of citrus by-products have been analysed. Most of the studies were focused on essential oil extraction and energy production. Most of the biorefinery systems and valorisation aims proposed were found to be better than the business-as-usual solution. However, results are strongly influenced by the functional unit and allocation method. Economic allocation to the main product resulted in better environmental performances. The major environmental hotspot is the agrochemicals required for crop management. The analysis of LCAs facilitated the identification of valorisation strategies that deserve greater interest from the scientific community to propose sustainable bio-circular solutions in the agro-industrial and agricultural sectors. Full article

Background: Greenhouses are pivotal to sustainable agriculture as they provide suitable conditions to support the growth of crops in unusable land such as arid areas. However, conventional greenhouse cover materials such as glass, polycarbonate (PC), and polyethylene (PE) sheets are limited in regulating internal conditions in the greenhouses based on environmental changes. Quantum and nonlinear metamaterials are emerging materials with the potential to optimize the covers and ensure appropriate regulation. Objective: This comprehensive review investigated the performance optimization of greenhouse covers through the potential application of nonlinear and quantum metamaterials as nano-additives, examining their effects on electromagnetic radiation management, crop growth enhancement, and temperature regulation within greenhouse systems. Method: The scoping review method was used, where 39 published articles were examined. Results: The review revealed that integrating nano-additives ensured that the greenhouse covers would block harmful near-infrared (NIR) radiation that generated heat while also optimizing for photosynthetically active radiation (PAR) to promote crop yields. Conclusions: The insights also indicated that the high sensitivity of the metamaterials would facilitate the regulation of the internal conditions within the greenhouses. However, challenges such as complex production processes that were not commercially scalable and the recyclability of the metamaterials were identified. Future work should further investigate pathways to produce hybrid greenhouse covers that integrate metamaterials with conventional materials to enhance scalability. Full article

Phosphorus-based compounds play a crucial role in agricultural productivity. However, excessive phosphorus discharge into water bodies contributes to eutrophication. This study proposes a circular approach for phosphorus recovery and reuse through the thermal valorization of Posidonia oceanica residues, an abundant marine biomass along Mediterranean coasts. After energy recovery from this waste (12.3 MJ kg⁻¹), the resulting ash was assessed as an effective adsorbent for aqueous phosphorus removal. Batch experiments were conducted to evaluate adsorption kinetics and equilibrium, considering the influence of key operational variables, such as temperature, pH, and adsorbent dosage. Under optimal conditions, the material achieved a maximum retention of approximately 55–60 mgP g⁻¹. The Freundlich model successfully describes the equilibrium isotherm data, indicating a heterogeneous adsorbent and an overall endothermic process. Phosphorus removal was favored at basic pH values (9.5–10.5), where the monohydrogen phosphate predominates. Leaching tests further revealed that saturated material releases phosphorus and other minerals in a manner clearly dependent on the final pH, with higher phosphorus release under more acidic conditions. These results suggest that Posidonia ash could serve as a low-cost adsorbent while also acting as a potential phosphorus source in soils. Full article

Driving wheel slippage in agricultural tractors is influenced by soil moisture, density, and penetration resistance. These surface variations reflect post-tillage composition, enabling dynamic mapping via Remotely Piloted Aircraft (RPAs). This study evaluated ballast recommendations based on soil surface data and slippage percentages, correlating added wheel weights at different speeds for a tractor-reversible plow system. Six 94.5 m² quadrants were analyzed for slippage monitored by RPA (Mavic3M-RTK) pre- and post-agricultural operation overflights and soil sampling (moisture, density, penetration resistance). A 2 × 2 factorial scheme (F-test) assessed soil-surface attribute correlations and slippage under varying ballasts (52.5–57.5 kg/hp) and speeds. Results showed slippage ranged from 4.06% (52.5 kg/hp, fourth reduced gear) to 11.32% (57.5 kg/hp, same gear), with liquid ballast and gear selection significantly impacting performance in friable clayey soil. Digital Elevation Model (DEM) and spectral indices derived from RPA imagery, including Normalized Difference Red Edge (NDRE), Normalized Difference Water Index (NDWI), Bare Soil Index (BSI), Green–Red Vegetation Index (GRVI), Visible Atmospherically Resistant Index (VARI), and Slope, proved effective. The approach reduced tractor slippage from 11.32% (heavy ballast, 4th gear) to 4.06% (moderate ballast, 4th gear), showing clear improvement in traction performance. The integration of indices and slope metrics supported ballast adjustment strategies, particularly for secondary plowing operations, contributing to improved traction performance and overall operational efficiency. Full article

This paper presents a digital approach for the identification of apple varieties bred in Kazakhstan using deep learning methods and transfer learning. The main objective of this study is to develop and evaluate an algorithm for automatic varietal classification of apples based on color images obtained under controlled conditions. Five representative cultivars were selected as research objects: Aport Alexander, Ainur, Sinap Almaty, Nursat, and Kazakhskij Yubilejnyj. The fruit samples were collected in the pomological garden of the Kazakh Research Institute of Fruit and Vegetable Growing, ensuring representativeness and taking into account the natural variability of the cultivars. Two convolutional neural network (CNN) architectures—GoogLeNet and SqueezeNet—were fine-tuned using transfer learning with different optimization settings. The data processing pipeline included preprocessing, training and validation set formation, and augmentation techniques to improve model generalization. Network performance was assessed using standard evaluation metrics such as accuracy, precision, and recall, complemented by confusion matrix analysis to reveal potential misclassifications. The results demonstrated high recognition efficiency: the classification accuracy exceeded 95% for most cultivars, while the Ainur variety achieved 100% recognition when tested with GoogLeNet. Interestingly, the Nursat variety achieved the best results with SqueezeNet, which highlights the importance of model selection for specific apple types. These findings confirm the applicability of CNN-based deep learning for varietal recognition of Kazakhstan apple cultivars. The novelty of this study lies in applying neural network models to local Kazakhstan apple varieties for the first time, which is of both scientific and practical importance. The practical contribution of the research is the potential integration of the developed method into industrial fruit-sorting systems, thereby increasing productivity, objectivity, and precision in post-harvest processing. The main limitation of this study is the relatively small dataset and the use of controlled laboratory image acquisition conditions. Future research will focus on expanding the dataset, testing the models under real production environments, and exploring more advanced deep learning architectures to further improve recognition performance. Full article

Adverse climatic dynamics in recent years have intensified the need for resilient and multifunctional agricultural systems that integrate productivity, ecological sustainability, and socio-economic viability. This study evaluates the harvesting performance of three cropping systems: intercropping of cardoon and safflower (IT) and monocultures of cardoon (DC) and safflower (DS). Field trials were conducted during three following growing seasons to assess key harvesting parameters, including working speed, effective field capacity, harvesting costs, biomass yield, seed yield, seed losses, and seed moisture content. DC demonstrated the better performance, with a working speed of 6.35 ha h⁻¹ and a field capacity of 2.56 ha h⁻¹, also resulting in the lowest harvesting cost (EUR 70.24 ha⁻¹). In contrast, IT exhibited the lowest performance and the highest cost (EUR 98.61 ha⁻¹). DS achieved the highest effective seed yield (1.394 Mg ha⁻¹), while IT produced the greatest biomass (22.96 Mg ha⁻¹). Seed losses were lowest in DS (0.020 Mg ha⁻¹) and highest in IT (0.425 Mg ha⁻¹). Moisture content ranged from 5.82% in DC to 9.40% in DS. These findings highlighted the trade-offs between productivity, efficiency, and system complexity, offering valuable insights into the comparative performance and sustainability of innovative cropping systems under changing climatic conditions. Full article

Soil electrical conductivity (EC) and water content are key indicators of soil health, influencing nutrient availability, salinity stress, and crop productivity. Monitoring these parameters is critical for precision agriculture. However, most existing measurement systems are costly, which restricts their use in practical field conditions. The aim of this study was to develop and validate a low-cost, portable system for simultaneous measurement of soil EC, water content, and temperature, while maintaining accuracy comparable to laboratory-grade instruments. The system was designed with four electrodes arranged in two pairs and employed an AC bipolar pulse method with a constant-current circuit, precision rectifier, and peak detector to minimize electrode polarization. Experiments were carried out in sandy loam soil at water contents of 13%, 18%, and 22% and KNO₃ concentrations of 0, 0.1, 0.2, and 0.4 M. Measurements from the developed system were benchmarked against a professional impedance analyzer (E4990A). The findings demonstrated that EC increased with both frequency and water content. At 100 Hz, the mean error compared with the analyzer was 8.95%, rising slightly to 9.98% at 10 kHz. A strong linear relationship was observed between EC and KNO₃ concentration at 100 Hz (R² = 0.9898), and for the same salt concentration (0.1 M KNO₃) at 100 Hz, EC increased from ~0.26 mS/cm at 13% water content to ~0.43 mS/cm at 22%. In conclusion, the developed system consistently achieved <10% error while maintaining a cost of ~€55, significantly lower than commercial devices. These results confirm its potential as an affordable and reliable tool for soil salinity and water content monitoring in precision agriculture. Full article

Imaging in controlled agriculture helps maximize plant growth by saving labor and optimizing resources. By monitoring specific plant traits, growers can prevent crop losses by correcting environmental conditions that lead to physiological disorders like leaf tipburn. This study aimed to identify morphometric and spectral markers for the early detection of tipburn in two Romaine lettuce (Lactuca sativa) cultivars (‘Chicarita’ and ‘Dragoon’) using an image-based system with color and multispectral cameras. By monitoring tipburn in treatments using melatonin, lettuce cultivars, and with and without supplemental lighting, we enhanced our system’s accuracy for high-resolution tipburn symptom identification. Canopy geometrical features varied between cultivars, with the more susceptible cultivar exhibiting higher compactness and extent values across time, regardless of lighting conditions. These traits were further used to compare simple linear, logistic, least absolute shrinkage and selection operator (LASSO) regression, and random forest models for predicting leaf fresh and dry weight. Random forest regression outperformed simpler models, reducing the percentage error for leaf fresh weight from ~34% (LASSO) to ~13% (RMSE: 34.14 g to 17.32 g). For leaf dry weight, the percentage error decreased from ~20% to ~12%, with an explained variance increase to 94%. Vegetation indices exhibited cultivar-specific responses to supplemental lighting. ‘Dragoon’ consistently had higher red-edge chlorophyll index (CIrededge), enhanced vegetation index, and normalized difference vegetation index values than ‘Chicarita’. Additionally, ‘Dragoon’ showed a distinct temporal trend in the photochemical reflectance index, which increased under supplemental lighting. This study highlights the potential of morphometric and spectral traits for early detection of tipburn susceptibility, optimizing cultivar-specific environmental management, and improving the accuracy of predictive modeling strategies. Full article

The harvesting of dragon fruit remains challenging due to uneven clamping forces, high fruit damage rates, and low redundancy in conventional end-effectors. To address these issues, we developed a novel embracing end-effector with a Variable-Span Arch (VSA) structure. The VSA design enables adaptive clamping force distribution and effective torsional fruit separation, significantly reducing static pressure damage. Theoretical modeling, mechanical testing, and field experiments were conducted to evaluate its performance. Results show that the proposed end-effector achieves a 95% harvesting success rate, with an average picking time of 15 s per fruit, and can output a maximum torque of 18 kgf·cm, which is sufficient for dragon fruit detachment. These findings demonstrate that the VSA-based embracing end-effector offers a low-damage, efficient, and robust solution for dragon fruit harvesting, providing practical guidance for robotic applications in tropical fruit production. Full article

Sprayers for vineyards with solution recovery represent an important innovation, offering several advantages, the most important being the efficient use of pesticides and environmental protection. This paper presents the experimental equipment designed to study the treatment process of grapevine foliage, the applied research methods, and the results of optimizing key technological parameters (hydraulic pressure p of the working solution, speed V of the airflow at the nozzle outlet) and design parameters (surface area S of the central orifice of the diffuser) in different growth stages of grapevines with varying foliar density ρ, the response function being the recovery rate of the working solution. The construction of the SVE 1500 (Experimental model, manufactured at the Institute of Agricultural Technology “Mecagro”, Chisinau, Republic of Moldova) vineyard sprayer with solution recovery is presented, along with test results obtained in field conditions, which demonstrated that the experimental model of our machine ensures a 38% reduction in working solution consumption during the active vegetation phase while maintaining treatment quality in compliance with agrotechnical requirements. The SVE 1500 machine can be towed with a sufficient turning radius for use in modern vineyard plantations. Construction documentation has been developed for the production and delivery of the experimental batch of SVE 1500 machines to agricultural enterprises. Full article

Due to its benefits and efficiency, mechanized sugarcane harvest is a common practice in Brazil; however, continuous traffic of agricultural machinery leads to soil compaction at the end of each harvest cycle. Hence, this study evaluated whether machine traffic affects soil physical and hydraulic properties, root growth, and crop productivity in sugarcane areas during different harvest cycles. Four treatments were performed consisting of an area planted with different stages (years) of sugarcane crop: T1 = after the first harvest—plant cane (area 1); T2 = after the second harvest—first ratoon cane (area 2); T3 = after the third harvest—second ratoon cane (area 3); T4 = after fourth harvest—third ratoon cane (area 4). Five sampling sites were considered in each area, constituting five replicates collected from four layers. Two collection positions were considered: wheel track (WT) and planting row (PR). Soil physical properties, root system, productivity, and biometric characteristics of the sugarcane crop were evaluated at depths of 0.00–0.05 m, 0.05–0.10 m, 0.10–0.20 m, and 0.20–0.40 m. Traffic during the sugarcane crop growth cycles affected soil physical and hydraulic properties, showing sensitivity to the effects of the different treatments, producing variations in root growth and crop productivity. Plant cane cycle showed lower soil penetration resistance, bulk density, microporosity, higher saturated soil hydraulic conductivity, and macroporosity when compared with the other cycles studied. In the 0.10–0.20 m layer, all treatments produced higher soil penetration resistance and density, and lower saturated soil hydraulic conductivity. Dry biomass, volume, and root area were higher for the plant cane cycle in the 0.00–0.05 m and 0.05–0.10 m layers compared with the other crop cycles. Root dry biomass is directly related to crop productivity in layers up to 0.40 m deep. Sugarcane productivity was affected along the crop cycles, with higher productivity observed in the plant cane and first ratoon cane cycles compared with the second and third ratoon cane cycles. Full article

This study evaluated the impact of biochar on the growth of strawberry plants, combining visual and proximal sensing monitoring. The plants were rooted in soil enriched with biochar, derived from pyrolysis of soft wood at 550 °C and applied in two doses (2 and 15 g/L), and after physical activation with CO₂ at 900 °C; there was also a treatment with no biochar (unaltered). Visual monitoring was based on data logging twice per week of plants’ height and number of flowers and ripe fruits. Proximal sensing monitoring involved a system including a low-cost multispectral camera and a Raspberry Pi 4. The camera acquired nadiral images hourly in three spectral bands (550, 660, and 850 nm), allowing calculation of the normalized difference vegetation index (NDVI). After three months, control plants reached a height of 12.3 ± 0.4 cm, while those treated with biochar and activated biochar grew to 18.03 ± 1.0 cm and 17.93 ± 1.2 cm, respectively. NDVI values were 0.15 ± 0.11 for control plants, increasing to 0.26 ± 0.03 (+78%) with biochar and to 0.28 ± 0.03 (+90%) with activated biochar. In conclusion, biochar application was beneficial for strawberry plants’ growth according to both visual and proximal-sensed measures. Further research is needed to optimize the integration of visual and proximal sensing monitoring, also enhancing the measured parameters. Full article

As a global staple ensuring food security, maize incurs 15–20% annual yield loss from pests/diseases. Conventional manual detection is inefficient (>7.5 h/ha) and subjective, while existing YOLO models suffer from >8% missed detections of small targets (e.g., corn armyworm larva) in complex fields due to feature loss and poor multi-scale fusion. We propose YOLO-FDLU, a YOLO11s-based framework: LAD (Light Attention-Downsampling)-Conv preserves small-target features; C3k2_DDC (DilatedReparam–DilatedReparam–Conv) enhances cross-scale fusion; Detect_FCFQ (Feature-Corner Fusion and Quality Estimation) optimizes bounding box localization; UIoU (Unified-IoU) loss reduces high-IoU regression bias. Evaluated on a 25,419-sample dataset (6 categories, 3 public sources + 1200 compliant web images), it achieves 91.12% Precision, 92.70% mAP@0.5, 78.5% mAP@0.5–0.95, and 20.2 GFLOPs/15.3 MB. It outperforms YOLOv5-s to YOLO12-s, supporting precision maize pest/disease monitoring. Full article

Extended Reality (XR) technologies—including Virtual Reality, Augmented Reality, and Mixed Reality—are increasingly applied in the food industry to simulate sensory environments, support education, and influence consumer behavior, while robotics addresses labor shortages, hygiene, and efficiency in production. This review uniquely synthesizes their convergence through digital twin frameworks, combining XR’s immersive simulations with robotics’ precision and scalability. A systematic literature review and keyword co-occurrence analysis of over 800 titles revealed research clusters around consumer behavior, nutrition education, sensory experience, and system design. In parallel, robotics has expanded beyond traditional pick-and-place tasks into areas such as precision cleaning, chaotic mixing, and digital gastronomy. The integration of XR and robotics offers synergies including risk-free training, predictive task validation, and enhanced human–robot interaction but faces hurdles such as high hardware costs, motion sickness, and usability constraints. Future research should prioritize interoperability, ergonomic design, and cross-disciplinary collaboration to ensure that XR–robotics systems evolve not merely as tools, but as a paradigm shift in redefining the human–food–environment relationship. Full article

Detecting surface cracks on sugarcane is a critical step in ensuring product quality control, with detection precision directly impacting raw material screening efficiency and economic benefits in the sugar industry. Traditional methods face three core challenges: (1) complex background interference complicates texture feature extraction; (2) variable crack scales limit models’ cross-scale feature generalization capabilities; and (3) high computational complexity hinders deployment on edge devices. To address these issues, this study proposes a lightweight sugarcane surface crack detection model, SCS-YOLO (Surface Cracks on Sugarcane-YOLO), based on the YOLOv10 architecture. This model incorporates three key technical innovations. First, the designed RFAC2f module (Receptive-Field Attentive CSP Bottleneck with Dual Convolution) significantly enhances feature representation capabilities in complex backgrounds through dynamic receptive field modeling and multi-branch feature processing/fusion mechanisms. Second, the proposed DSA module (Dynamic SimAM Attention) achieves adaptive spatial optimization of cross-layer crack features by integrating dynamic weight allocation strategies with parameter-free spatial attention mechanisms. Finally, the DyHead detection head employs a dynamic feature optimization mechanism to reduce parameter count and computational complexity. Experiments demonstrate that on the Sugarcane Crack Dataset v3.1, compared to the baseline model YOLOv10, our model achieves mAP50:95 to 71.8% (up 2.1%). Simultaneously, it achieves significant reductions in parameter count (down 19.67%) and computational load (down 11.76%), while boosting FPS to 122 to meet real-time detection requirements. Considering the multiple dimensions of precision indicators, complexity indicators, and FPS comprehensively, the SCS—YOLO detection framework proposed in this study provides a feasible technical reference for the intelligent detection of sugarcane quality in the raw materials of the sugar industry. Full article

This article presents the development of a new pneumatic device for the precise application of mineral fertilizers, designed for use in precision agriculture systems involving farming robots. The proposed device is mounted on an autonomous agricultural platform and utilizes a machine vision system to determine plant coordinates. Its operating principle is based on accumulating a single dose of fertilizer in a chamber and delivering it precisely to the plant’s root zone using a directed airflow. The study includes a theoretical investigation of fertilizer movement inside the applicator tube under the influence of airflow and rotational motion of the tube. A mathematical model has been developed to describe both the relative and translational motion of the fertilizer. The equations, which account for frictional forces, inertia, and air pressure, enable the determination of optimal structural and kinematic parameters of the device depending on operating conditions and the properties of the applied material. The use of numerical methods to solve the developed mathematical model allows for synchronization of the device’s operating time parameters with the movement of the agricultural robot along the crop rows. The obtained results and the developed device improve the accuracy and speed of fertilizer application, minimize fertilizer consumption, and reduce soil impact, making the proposed device a promising solution for precision agriculture. Full article

The production of electricity from photovoltaics (PV) in the agricultural sector is expanding considerably, driven by ecological concerns and continuous technological development. Additionally, growing constraints on the use of arable land for PV energy production requires increased energy production per unit area of panels. Bifacial panels are one of the highest performing PV solutions currently available. The subject of this paper is the productivity modeling of mono- and bifacial PV panels. The aim is to develop a physically based model for PV productivity without the use of commercial software. For this purpose, Durisch’s model is modified and adapted for bifacial panels and the necessary empirical parameters are determined. The developed model was validated experimentally. A comparison of the performance of the front and rear side of a bifacial panel is presented. The influence of the type of reflective surface is also investigated. The productivity and efficiency of monocrystalline monofacial and bifacial panels are also compared. The experiments were carried out in real conditions typical of a temperate continental climate for the latitude of Sofia, Bulgaria under different meteorological conditions. Full article