AgriEngineering, Volume 7, Issue 10 (October 2025) – 43 articles

Background/Objectives: Strawberry crops are vulnerable to fungal diseases that severely affect yield and quality. Deep learning has shown strong potential for plant disease recognition; however, most architectures rely on tens of millions of parameters, limiting their use in low-resource agricultural settings. This study presents Light-MobileBerryNet, a lightweight and interpretable model designed to achieve accurate strawberry disease classification while remaining computationally efficient for potential use on mobile and edge devices. Methods: The model, inspired by MobileNetV3-Small, integrates inverted residual blocks, depthwise separable convolutions, squeeze-and-excitation modules, and Swish activation to enhance efficiency. A publicly available dataset was processed using CLAHE and data augmentation, and split into training, validation, and test subsets under consistent conditions. Performance was benchmarked against state-of-the-art CNNs. Results: Light-MobileBerryNet achieved 96.6% accuracy, precision, recall, and F1-score, with a Matthews correlation coefficient of 0.96, while requiring fewer than one million parameters (~2 MB). Grad-CAM confirmed that predictions focused on biologically relevant lesion regions. Conclusions: Light-MobileBerryNet approaches state-of-the-art performance with a fraction of the computational cost, providing a practical and interpretable solution for precision agriculture. Full article

To address the challenges of high model complexity, substantial computational resource consumption, and insufficient classification accuracy in existing soybean seed identification research, we first perform soybean seed segmentation based on polygon features, constructing a dataset comprising five categories: whole seeds, broken seeds, seeds with epidermal damage, immature seeds, and spotted seeds. The MobileViT module is then optimized by employing Depthwise Separable Convolution (DSC) in place of standard convolutions, applying Transformer Half-Dimension (THD) for dimensional reconstruction, and integrating Dynamic Channel Recalibration (DCR) to reduce model parameters and enhance inter-channel interactions. Furthermore, by incorporating the CBAM attention mechanism into the MV2 module and replacing the ReLU6 activation function with the Mish activation function, the model’s feature extraction capability and generalization performance are further improved. These enhancements culminate in a novel soybean seed detection model, MobileViT-SD (MobileViT for Soybean Detection). Experimental results demonstrate that the proposed MobileViT-SD model contains only 2.09 million parameters while achieving a classification accuracy of 98.39% and an F1 score of 98.38%, representing improvements of 2.86% and 2.88%, respectively, over the original MobileViT model. Comparative experiments further show that MobileViT-SD not only outperforms several representative lightweight models in both detection accuracy and efficiency but also surpasses a number of mainstream heavyweight models. Its highly optimized, lightweight architecture combines efficient inference performance with low resource consumption, making it well-suited for deployment in computing-constrained environments, such as edge devices. Full article

Ensuring global food security requires accurate and robust solutions for crop health monitoring, weed detection, and large-scale land-cover classification. To this end, we propose AgroVisionNet, a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) for local feature extraction, Vision Transformers (ViTs) for capturing long-range global dependencies, and Graph Neural Networks (GNNs) for modeling spatial relationships between image regions. The framework was evaluated on five diverse benchmark datasets—PlantVillage (leaf-level disease detection), Agriculture-Vision (field-scale anomaly segmentation), BigEarthNet (satellite-based land-cover classification), UAV Crop and Weed (weed segmentation), and EuroSAT (multi-class land-cover recognition). Across these datasets, AgroVisionNet consistently outperformed strong baselines including ResNet-50, EfficientNet-B0, ViT, and Mask R-CNN. For example, it achieved 97.8% accuracy and 95.6% IoU on PlantVillage, 94.5% accuracy on Agriculture-Vision, 92.3% accuracy on BigEarthNet, 91.5% accuracy on UAV Crop and Weed, and 96.4% accuracy on EuroSAT. These results demonstrate the framework’s robustness across tasks ranging from fine-grained disease detection to large-scale anomaly mapping. The proposed hybrid approach addresses persistent challenges in agricultural imaging, including class imbalance, image quality variability, and the need for multi-scale feature integration. By combining complementary architectural strengths, AgroVisionNet establishes a new benchmark for deep learning applications in precision agriculture. Full article

Ammonia poses a risk to human health and terrestrial and aquatic ecosystems. In Spain in 2022, the agricultural sector was responsible for 97% of ammonia emissions to the atmosphere, with the application of animal manure as fertilizer accounting for 24.4% of these emissions. The search for effective mitigation strategies in the application of animal manures is imperative to support the implementation of policies that contribute to the sustainability of the agricultural sector. The aim of this study is to evaluate three digestate application techniques, namely, acidification, open disc injection, and fertigation, in a wheat–maize rotation and compare them to traditional trail hose application. In spring wheat topdressing, acidification is the most efficient method for reducing ammonia emissions, followed by disc injection and, finally, fertigation. In the summer base dressing to maize, acidification is the best method, with more than 70% reduction compared with trail hoses. In terms of both base dressing and side-dressing fertilization, the most efficient method is fertigation, with a 70% reduction, followed by acidification and disc injection (>25%). Although the three methods reduce ammonia emissions, they have certain drawbacks: fertigation requires previous solid/liquid separation, acidification requires ad hoc equipment, and disc injection requires high mechanical traction. Full article

In pigeon farming, manure cleaning is predominantly manual, a method that is both slow and costly, and exposes workers to harsh conditions. Addressing these challenges, this paper introduces a cleaning robot for pigeon farms utilizing magnetic strip navigation combined with RFID signal recognition and derives the magnetic-navigation control model. This method can improve operational stability and accuracy. Given the farm’s unstable environment, a control algorithm based on fuzzy PID with Kalman filtering was developed. This algorithm mitigates input disturbances and measurement noise by integrating Kalman filtering into the fuzzy PID feedback loop, thereby refining signal accuracy. Numerical simulations conducted in Matlab/Simulink demonstrate that the inclusion of Kalman filtering reduces the time of target signal tracking by nearly 1 s compared to fuzzy PID and by almost 2 s relative to standard PID under identical disturbances. Experimental tests confirm that this algorithm significantly improves the robot’s operational stability and reduces magnetic-navigation deviation, underscoring its advancement and practicality over traditional PID and fuzzy PID methods. Full article

Recent advances in hyperspectral imaging (HSI) and multimodal deep learning have opened new opportunities for crop health analysis; however, most existing models remain limited by dataset scope, lack of interpretability, and weak cross-domain generalization. To overcome these limitations, this study introduces Agri-DSSA, a novel Dual Self-Supervised Attention (DSSA) framework that simultaneously models spectral and spatial dependencies through two complementary self-attention branches. The proposed architecture enables robust and interpretable feature learning across heterogeneous data sources, facilitating the estimation of spectral proxies of chlorophyll content, plant vigor, and disease stress indicators rather than direct physiological measurements. Experiments were performed on seven publicly available benchmark datasets encompassing diverse spectral and visual domains: three hyperspectral datasets (Indian Pines with 16 classes and 10,366 labeled samples; Pavia University with 9 classes and 42,776 samples; and Kennedy Space Center with 13 classes and 5211 samples), two plant disease datasets (PlantVillage with 54,000 labeled leaf images covering 38 diseases across 14 crop species, and the New Plant Diseases dataset with over 30,000 field images captured under natural conditions), and two chlorophyll content datasets (the Global Leaf Chlorophyll Content Dataset (GLCC), derived from MERIS and OLCI satellite data between 2003–2020, and the Leaf Chlorophyll Content Dataset for Crops, which includes paired spectrophotometric and multispectral measurements collected from multiple crop species). To ensure statistical rigor and spatial independence, a block-based spatial cross-validation scheme was employed across five independent runs with fixed random seeds. Model performance was evaluated using $R^2$, RMSE, F1-score, AUC-ROC, and AUC-PR, each reported as mean ± standard deviation with 95% confidence intervals. Results show that Agri-DSSA consistently outperforms baseline models (PLSR, RF, 3D-CNN, and HybridSN), achieving up to $R^2=0.86$ for chlorophyll content estimation and F1-scores above 0.95 for plant disease detection. The attention distributions highlight physiologically meaningful spectral regions (550–710 nm) associated with chlorophyll absorption, confirming the interpretability of the model’s learned representations. This study serves as a methodological foundation for UAV-based and field-deployable crop monitoring systems. By unifying hyperspectral, chlorophyll, and visual disease datasets, Agri-DSSA provides an interpretable and generalizable framework for proxy-based vegetation stress estimation. Future work will extend the model to real UAV campaigns and in-field spectrophotometric validation to achieve full agronomic reliability. Full article

In response to the poor soil loosening effect of the previous bionic four-bar deep loosening mechanism, this study optimized the bionic motion trajectory according to agronomic requirements, established a trajectory synthesis optimization model of the Stephenson III six-bar mechanism, and solved it using an improved differential evolution algorithm to design a six-bar deep loosening mechanism achieving the optimized trajectory. Based on the Box–Behnken experiment, a regression model of the mechanism’s process parameters and performance indicators was established, and multiple indicators were integrated into a single objective via a satisfaction function. The optimal process parameters obtained were: entry angle 99.61°, shovel distance 185 mm, forward speed 0.29 $\mathrm{m}/\mathrm{s}$, and input speed $5\pi$ rad/s. A comparative simulation using the Discrete Element Method (DEM) showed that, compared to the bionic four-bar mechanism, the six-bar mechanism reduces resistance by 9.91%, increases soil-breaking capacity by 4.23%, reduces shallow disturbance by 14.43%, increases deep disturbance by 29.54%, and improves overall disturbance effect by 42.71%, verifying the effectiveness of agronomic-driven bionic trajectory optimization. Indoor soil tank experiments measured an average resistance of 258.83 N, with a relative error of 8.67% compared to the simulation result (281.32 N). The experiments and simulations were consistent in soil-breaking layer range, soil layer disturbance range, and soil discharge state, validating the model and the six-bar deep loosening mechanism. Full article

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