AgriEngineering, Volume 7, Issue 11 (November 2025) – 41 articles

The separation of walnut kernels from shells is a crucial step in walnut processing. Pneumatic sorting is the mainstream method. However, due to the overlapping suspension speeds of half-shells and eighth-shells, complete separation was not achieved. This paper proposes using a toothed vibrating screen to separate the two. Using EDEM to simulate and analyze the motion forms, collision processes, and stress conditions of walnut shells and kernels on the vibrating screen, the effectiveness of this method was demonstrated, and the mechanisms of shell–kernel retention and loss during the separation process were revealed. Results indicate that 1/8 kernels, being smaller, easily fall into tooth grooves and move upward step by step under the excitation force during reciprocating vibration. The 1/2 shells, being larger, are difficult to fall into the teeth grooves, and their smooth surfaces cause them to slide easily, moving downward continuously under the action of reciprocating vibration and gravity. Using the cleaning rate and loss rate as evaluation indicators, it was found that as the inclination angle of the vibrating screen increased step by step, the cleaning rate consistently increased monotonously. The loss rate initially rose slowly, then surged sharply after reaching 22°, at which point the loss rate was at its lowest, around 10%, and the cleaning rate was at its maximum, at 95%. The shortest retention time of walnut shells on the screen is 2.85 s, and the longest is 10.6 s, with the number of collisions being 458 and 2619, respectively; the collisions between the shells and the kernels account for 51.8%. The failure to thoroughly separate is due to the shell and kernel entangling within the separation area, making it impossible to segregate them. They enter the opposite region, collide, and cause loss and retention phenomena. Full article

Given the current climate change scenario, it is essential to find strategies to reduce environmental risks and obtain economically sustainable agricultural productions. This study investigated the impact of various agronomic treatments on an almond orchard in northeastern Portugal, focusing on their relationships with crop growth/vigour and yield. The experiment was conducted using a factorial design that combined three variables: almond cultivar (Constantí and Vairo), irrigation regime (full and regulated deficit irrigation), and kaolin application (with or without application). These combinations resulted in eight distinct treatments, each replicated across two experimental plots. To monitor the crop physiological status, two drone flights equipped with a multispectral camera were flown during the kernel-filling stage (3 and 30 August 2021). Vegetation indices (VI) derived from the multispectral images were used to assess the crop vigour. In relation to the production data, including kernel and in-shell almond weights, these were collected in 14 representative trees of each treatment. Lastly, parametric and nonparametric regression analyses were performed to better understand relationships between VI and crop yields and derive predictive models. The main results can be summarised as follows: (a) cv. Vairo was more vulnerable to the regulated deficit irrigation strategy with striking repercussions on almond production, translating into an average reduction per tree of 22% and 16% in almond kernel and in-shell almonds compared to full irrigation, respectively; (b) kaolin application did not reflect statistically significant differences in the mean crop yield, as Tukey’s pairwise comparisons involving kaolin as a differentiating factor (e.g., C100+k—C100, V100+K—V100) showed confidence intervals with central value close to zero; and (c) regression analysis using the nonparametric random forest model and individualised treatments demonstrated a better agreement with the observed data (R² > 0.7). This research provided valuable insights into how cultivar selection, irrigation strategy, and kaolin application can influence the almond crop performance. When integrating multispectral aerial monitoring and advanced statistical modelling, it enables an effective assessment of both crop vigour and expected yield, supporting the development of more informed and adaptive management practices to face emerging environmental challenges. Full article

Artificial intelligence (AI) is driving the evolution of autonomous agriculture towards multi-agent collaborative control, breaking through the limitations of traditional isolated automation. Although existing research has focused on hierarchical control and perception-decision-making technologies for agricultural machinery, the overall integration of these elements in building a resilient physical perception collaborative system is still insufficient. This paper systematically reviews the progress of AI-driven tractor-implement cooperative control from 2018 to 2025, focusing on four major technical pillars: (1) perception-decision-execution hierarchical architecture, (2) distributed multi-agent collaborative framework, (3) physical perception modeling and adaptive control, and (4) staged operation applications (such as collaborative harvesting). The research reveals core challenges such as real-time collaborative planning, perception robustness under environmental disturbances, and collaborative control and safety assurance under operational disturbances. To this end, three solutions are proposed: an AI framework for formalizing agronomic constraints and mechanical dynamics; a disturbance-resistant adaptive tractor-implement cooperative control strategy; and a real-time collaborative ecosystem integrating neuromorphic computing and FarmOS. Finally, a research roadmap is summarized with agronomic constraint reinforcement learning, self-reconfigurable collaboration, and biomechanical mechatronic systems as the core. By integrating the scattered progress in AI, robotics and agronomy, we provide theoretical foundation and practical guidance for scalable and sustainable autonomous farm systems. Full article

Energy expenditure constitutes a significant portion of total operational costs in greenhouse crop production. Developing accurate energy consumption prediction models presents crucial theoretical foundations for optimizing the environmental control strategies aimed at energy efficiency enhancement. This study focuses on steel-frame solar greenhouses without back slopes in Xinjiang’s Tianshan North Slope region. A physical model was established using thermodynamic equilibrium analysis, elucidating the energy exchange mechanisms between internal and external environments. Key parameters, including outdoor temperature and solar radiation, were identified as primary input variables through systematic energy flow characterization. Building upon this theoretical framework, we developed an enhanced prediction model (WOA-ELM) by integrating the Whale Optimization Algorithm (WOA) with an Extreme Learning Machine (ELM). The WOA’s global optimization capabilities were employed to refine the connection weights between input-hidden layers and optimize hidden neuron thresholds. Comparative evaluations against conventional artificial neural networks (ANNs), radial basis function neural networks (RBFNN), and baseline ELM models were conducted under diverse meteorological conditions. Experimental results demonstrate the superior performance of WOA-ELM across multiple metrics. Under overcast conditions, the model achieved a root mean square error (RMSE) of 0.423, coefficient of determination (R²) of 0.93, and mean absolute error (MAE) of 0.252. In clear weather scenarios, performance further improved with RMSE = 0.27, R² = 0.96, and MAE = 0.063. The comprehensive evaluation ranked model effectiveness as WOA-ELM > ELM > BP > RBF. These findings substantiate that the hybrid WOA-ELM architecture, combining physical mechanism interpretation with intelligent parameter optimization, delivers enhanced prediction accuracy across varying weather patterns. This research provides valuable insights for energy load management in backslope-less steel-frame greenhouses, offering theoretical guidance for thermal environment regulation and sustainable operation. Full article

Post-harvest food loss significantly threatens global food security, and solar drying offers a sustainable preservation solution. The effectiveness of solar drying depends on consumer acceptance, in which color is a critical quality attribute. This study investigated how solar irradiance and temperature affect color degradation during the drying of pineapple (Ananas comosus), orange (Citrus × sinensis), and beet (Beta vulgaris L.). Experiments conducted in Zacatecas, Mexico, compared a Solar Dryer with Dynamic Irradiation Control (SDIC), which limited irradiance to 700 W/m², against an uncontrolled Cylindrical Solar Dryer (CSD). The results indicate that the controlled SDIC environment promotes gradual and uniform color preservation by minimizing rapid thermal stress. In contrast, the fluctuating high irradiance and temperature of the CSD caused faster, less uniform color changes. Statistical analyses confirmed that both irradiance and temperature significantly impacted color parameters (p < 0.05). The SDIC method reduced the total color change (ΔE) by 30–47% in pineapple and beet compared to the CSD. Regression models identified temperature as the primary driver of redness (a*) degradation, while irradiance was strongly correlated with changes in yellowness (b*). This research highlights the necessity of optimizing solar drying conditions to enhance the quality of dried produce. By improving visual appeal, this optimized green technology can help reduce food waste and support the transition to more sustainable fod processing systems. This controlled approach reduced the total color change (ΔE) by 30–47% in pineapple and beet compared to the CSD, demonstrating its significant potential for quality preservation. Full article

This study investigates the impact of digital transformation on a Brazilian post-harvest engineering company. The manuscript examines how digital technologies impact performance and competitive advantages, providing actionable insights for practitioners. The methodology is a twofold case study. First, it describes the company’s technology development process, examining the journey from initial implementations to its current state over the past ten years. Then, it focuses on the recommendations for future advancements, provided by a leading technology research institute located in Germany. Data collection involved observation, interviews (personnel, clients, experts), document analysis, and facility tours. The findings include qualitative (grain quality, agility) and quantitative impacts (EBITDA increase, 84% storage efficiency). Barriers included mechanical adaptation and costs. Opportunities related by BIBA include advanced technologies (such as AI and digital twins), aligning with a proposed six-level digital maturity model for post-harvest systems. Regarding practical implications, the findings emphasize the need for a strategic vision for digital technology adoption in the post-harvest industry, which is crucial for addressing labor shortages, reducing losses, and promoting sustainability, with potential annual gains of $700 million. The main novelty introduced by this study is a novel, empirically derived six-level digital maturity model. It provides comprehensive qualitative/quantitative impact analysis, highlighting advanced technologies for industry challenges. Full article

Yield maps from combine harvesters are essential in precision agriculture for capturing within-field variability and guiding variable-rate input management. However, in large-scale systems such as those in the Brazilian Cerrado, these maps are often inconsistent due to calibration errors, use of multiple harvesters, and complex post-processing. Orbital remote sensing offers an alternative by providing consistent vegetation index (VI) data for crop monitoring and yield estimation. This study developed regression models relating Sentinel-2 VIs (EVI, TVI, NDVI, and NDRE) to cotton yield data obtained from combine harvesters across 30 commercial plots in Mato Grosso, Brazil, over six cropping seasons (2019–2024), totaling 76 plot-season datasets. Vegetation indices were grouped into 15-day intervals based on days after sowing, and a logistic growth function was applied in the regression modeling. Model performance evaluated using 15 independent plot-seasons showed good pixel-level accuracy, with RMSE of 0.695 t ha⁻¹ and R² of 0.78, with EVI performing slightly better. At the plot scale, mean yield predictions across all datasets achieved an RMSE of 0.41 t ha⁻¹, reflecting the higher reliability of module-based yield measurements. These results demonstrate the potential of Sentinel-2 VIs combined with logistic regression to predict cotton yields in the Cerrado, complementing or replacing harvester-based monitoring. Full article

The curation of large-scale, diverse datasets for robust weed detection is extremely time-consuming and resource-intensive in practice. Generative artificial intelligence (AI) opens up opportunities for image generation to supplement real-world image acquisition and annotation efforts. However, it is not a trial task to generate high-quality, multi-class weed images that capture the nuances and variations in visual representations for enhanced weed detection. This study presents a novel investigation of advanced stable diffusion (SD) integrated with a module with image prompt capability, IP-Adapter, for weed image generation. Using the IP-Adapter-based model, two image feature encoders, CLIP (contrastive language image pre-training) and BioCLIP (a vision foundation model for biological images), were utilized to generate weed instances, which were then inserted into existing weed images. Image generation and weed detection experiments are conducted on a 10-class weed dataset captured in vegetable fields. The perceptual quality of generated images is assessed in terms of Fréchet Inception Distance (FID) and Inception Score (IS). YOLOv11 (You Only Look Once version 11) models were trained for weed detection, achieving an improved mAP@50:95 of 1.26% on average when combining inserted weed instances with real ones in training, compared to using original images alone. Both the weed dataset and software programs in this study will be made publicly available. This study offers valuable perspectives into the use of IP-adapter-based SD for generating weed images and weed detection. Full article

The rapid development of unmanned aerial vehicles (UAVs) and deep learning has accelerated the application of semantic segmentation in precision agriculture (SSPA). A key driver of this progress lies in multimodal fusion, which leverages complementary structural, spectral, and physiological information to enhance the representation of complex agricultural scenes. Despite advancements, the efficacy of multimodal fusion in SSPA is limited by modality heterogeneity and the difficulty of simultaneously retaining fine details and capturing global context. To address these challenges, we propose AgriFusion, a dual-encoder framework based on convolutional and transformer architectures for SSPA tasks. Specifically, convolutional and transformer encoders are first used to extract crop-related local structural details and global contextual features from multimodal inputs. Then, an attention-based fusion module adaptively integrates these complementary features in a modality-aware manner. Finally, a MLP-based decoder aggregates multi-scale representations to generate accurate segmentation results efficiently. Experiments conducted on the Agriculture-Vision dataset demonstrate that AgriFusion achieves a mean Intersection over Union (mIoU) of 49.31%, Pixel Accuracy (PA) of 81.72%, and F1 score of 67.85%, outperforming competitive baselines including SegFormer, DeepLab, and AAFormer. Ablation studies further reveal that unimodal or shallow fusion strategies suffer from limited discriminative capacity, whereas AgriFusion adaptively integrates complementary multimodal features and balances fine-grained local detail with global contextual information, yielding consistent improvements in identifying planting anomalies and crop stresses. These findings validate our central claims that modality-aware spectral fusion and balanced multi-scale representation are critical to advancing agricultural semantic segmentation, and establish AgriFusion as a principled framework for enhancing remote sensing-based monitoring with practical implications for sustainable crop management and precision farming. Full article

Shellfish aquaculture is considered a major pillar of the seafood industry for its high market value, which increases the value for global food security and sustainability, often constrained in terms of conventional, labor-intensive practices. This review outlines the importance of automation and its advances in the shellfish value chain, starting from the hatchery operations to harvesting, processing, traceability, and logistics. Emerging technologies such as imaging, computer vision, artificial intelligence, robotics, IoT, blockchain, and RFID provide a major impact in transforming the shellfish sector by improving the efficiency, reducing the labor costs and environmental impacts, enhancing the food safety, and providing transparency throughout the supply chain. The studies involving the bivalves and crustaceans on their automated feeding, harvesting, grading, depuration, non-destructive quality assessments, and smart monitoring in transportation are highlighted in this review to address concerns involved with conventional practices. The review puts forth the need for integrating automated technologies into farm management and post-harvest operations to scale shellfish aquaculture sustainably, meeting the rising global demand while aligning with the Sustainability Development Goals (SDGs). Full article

Plant disease recognition is a critical technology for ensuring food security and advancing precision agriculture. However, challenges such as class imbalance, heterogeneous image quality, and limited model interpretability remain unresolved. In this study, we propose a Synergistic Dual-Augmentation and Class-Aware Hybrid (SDA-CAH) model designed to achieve robust and interpretable recognition of plant diseases. Our approach introduces two innovative augmentation strategies: (1) an optimized MixUp method that dynamically integrates class-specific features to enhance the representation of minority classes; and (2) a customized augmentation pipeline that combines geometric transformations with photometric perturbations to strengthen the model’s resilience against image variability. To address class imbalance, we further design a class-aware hybrid sampling mechanism that incorporates weighted random sampling, effectively improving the learning of underrepresented categories and optimizing feature distribution. Additionally, a Grad-CAM–based visualization module is integrated to explicitly localize lesion regions, thereby enhancing the transparency and trustworthiness of the predictions. We evaluate SDA-CAH on the PlantVillage dataset using a pretrained EfficientNet-B0 as the backbone network. Systematic experiments demonstrate that our model achieves 99.95% accuracy, 99.89% F1-score, and 99.89% recall, outperforming several strong baselines, including an optimized Xception (99.42% accuracy, 99.39% F1-score, 99.41% recall), standard EfficientNet-B0 (99.35%, 99.32%, 99.33%), and MobileNetV2 (95.77%, 94.52%, 94.77%). For practical deployment, we developed a web-based diagnostic system that integrates automated recognition with treatment recommendations, offering user-friendly access for farmers. Experimental evaluations indicate that SDA-CAH outperforms existing approaches in predictive accuracy and simultaneously defines a new paradigm for interpretable and scalable plant disease recognition, paving the way for next-generation precision agriculture. Full article

The bio-conversion of agricultural biomass into value-added products via solid-state fermentation (SSF) represents a cost-effective and eco-friendly approach, though it is often limited by low efficiency and prolonged processing times. While low-intensity magnetic fields (LMFs) have shown potential to enhance microbial metabolism and improve mass and heat transfer during SSF, the effects of conventional inhomogeneous magnetic fields remain inconsistent and may even cause localized microbial damage due to uneven field distribution. In this study, we designed and optimized a Helmholtz coil system capable of generating a highly homogeneous low-intensity magnetic field to overcome this limitation. Through electromagnetic simulation and experimental validation, an optimized aluminum profile-supported coil configuration was developed, achieving an average magnetic field intensity of 142.77 G under 70% power load with high spatial homogeneity (maximum deviation: ±1.32%). Applied to the solid-state fermentation of peanut meal, the homogeneous LMF treatment (40 G, 4 h) significantly increased peptide content by 77.76% compared to non-treated samples, and by 42.95% over traditional inhomogeneous LMF treatment. This work establishes homogeneous magnetic-field-assisted SSF as a novel, efficient, and scalable bioprocessing strategy, providing both a robust technological framework and new insights into the role of field uniformity in the magneto-fermentation of agricultural biomass. Full article

The study analyzed energy, environmental impact, and costs in intensive broiler production systems in the southeast of the state of Minas Gerais, Brazil, comparing scenarios with and without photovoltaic systems. Four configurations were evaluated, considering different ventilation types (positive and negative pressure) and photovoltaic generation. The Life Cycle Assessment (LCA), with a functional unit of 1 kg of live weight of chicken and a cradle-to-gate approach, indicated that photovoltaic systems reduce between 2.58 t and 4.96 t of CO₂-eq annually, in addition to offering better energy efficiency. Economically, sheds with positive pressure ventilation have the lowest cost–benefit ratios, while the feeding subsystem was the one that contributed the most to global warming, among the environmental impact categories evaluated in the LCA. Photovoltaic systems demonstrated the potential to reduce electricity costs between 19.4% and 26.5% per year. However, coffee husks used as chicken litter accounted for 36.5% of production costs, highlighting the need for more economical alternatives. It was concluded that photovoltaic systems are a viable solution to reduce environmental impacts and increase profitability, reinforcing the importance of resource-use optimization strategies in poultry farming. Full article

Background: The growing global demand for grains and the pursuit of greater efficiency in agroindustrial production processes have fueled scientific interest in technologies for automatic visual inspection of agricultural grains (AVIAG). Despite the increasing number of studies on this topic, few have addressed the practical implementation of these technologies within industrial environments. Objective: This study aims to investigate the technological demands, analyze the potential applications, and identify the challenges for technology transfer of AVIAG technologies to the agroindustrial sector. Methods: The methodological approach combined a comprehensive literature review, which enabled the mapping of AVIAG technology applications and technological maturity levels, with a structured survey designed to identify practical demands, challenges, and barriers to technology transfer in the agricultural sector. Results: The results show that most of the proposed solutions exhibit low technological maturity and require significant adaptation for practical application, which undermines the discussion on technology transfer. Conclusions: The main barriers to large-scale adoption of AVIAG technologies include limited dissemination of scientific knowledge, a shortage of skilled labor, high implementation costs, and resistance to changes in production processes. Nonetheless, the literature highlights benefits, such as increased automation, enhanced operational efficiency, and reduced post-harvest losses, which reinforce the potential of AVIAG technologies in advancing the modernization of the agroindustrial sector. Full article

This study develops a practical, on-farm methodology for optimizing cotton cultivation through Variable Rate Seeding (VRS), utilizing existing farm data and remote sensing, while minimizing operational interference. The methodology involved an experimental design across five rainfed cotton fields on a Brazilian commercial farm, testing four seeding rates (90%, 100%, 110%, 120%) within grid cells using a 4 × 4 Latin square design. Management zones (MZs) were defined using existing soil clay content and elevation data, augmented by twelve vegetation indices from Sentinel-2 satellite imagery and K-Means clustering. Statistical analysis evaluated plant population density’s effect on cotton yield and its association with MZs. For the 2023/2024 season, results showed no positive yield response to increasing plant density above field averages, with negative responses in many plots (e.g., 84% in Field A), suggesting potential gains from reducing rates. The association between population density effect classes and MZs was highly significant with moderate to relatively strong Cramer’s V values (up to 0.47), indicating MZs effectively distinguished response areas. Lower clay content consistently correlated with yield losses at higher densities. This work empowers farm managers to conduct their own site-specific experimentation for optimal seed populations, enhancing precision agriculture and resource efficiency. Full article

The use of sustainable and efficient practices is important for high crop yields. This study aimed to determine the effects of microorganisms and fertilizers on the growth, yield, and fruit quality of two strawberry cultivars in Cañete, Peru. The experiment was set up in a randomized complete block design with a split-plot arrangement, where the main plots were the fertilizer doses (0, 50, 100, and 150%) and the subplots were arranged in a factorial scheme of 2 × 4, with two strawberry varieties, three microorganisms (Azospirillum brasilense, Rhizophagus spp., and Trichoderma sp.), and the control. Growth variables included the number of leaves, crowns, and flowers; petiole length and diameter; foliar area; number of fruits; and yield. Nutrient concentrations of nitrogen, phosphorus, potassium, calcium, and magnesium were determined. For fruit quality, the variables of pH, Brix grade, and acidity were measured. The results indicated that the Sabrina cultivar had higher growth and yield (+15%). All fertilizer doses promoted yield and firmness, especially the 50% dose. All microorganisms promoted growth, yield (+60%), and fruit quality, demonstrating their importance in improving fruit production in this crop in Peru. Full article

This study introduces an IoT-enabled smart greenhouse system tailored for rice cultivation and designed as a controlled experimental platform to evaluate fertilizer application methods. Traditional greenhouse farming often struggles with unpredictable weather, pest infestations, and inefficient resource use. To overcome these challenges, the proposed system optimizes environmental conditions and enables precise monitoring and control through the Thingsboard IoT platform, which tracks temperature, humidity, and sunlight intensity in real time. The cultivation process involved Inceptisol soil preparation, slurrying, fertilization, seeding, transplantation, and continuous monitoring. The novelty lies in its dual-purpose design, enabling both cultivation and structured agronomic experimentation under identical environmental conditions. The system enables both rice cultivation and comparative testing of nano-silica fertilizer applied via root (soil) and foliar (leaf) methods. Automated temperature control (maintaining 20–36.5 °C) and humidity regulation (10–85% RH) with a mist blower response time under 5 s ensured consistent conditions. Sensor accuracy was validated with deviations of 0.4% (±0.11 °C) and 0.77% (±0.5% RH). Compared to conventional setups, this system achieved superior environmental stability and control precision, improving experimental reproducibility. Its integration potential with machine learning models opens new possibilities for forecasting plant responses based on historical data. Overall, the study demonstrates how advanced technology can enhance agricultural precision, sustainability, and research reliability. Full article

The use of Remotely Piloted Aircraft (RPA) for spraying coffee crops has expanded due to their practicality and cost reduction. This study aimed to evaluate spray rate effects on coffee crops using two RPA (T10 and T20). The study was conducted on a commercial farm with 10-year-old Coffea arabica Catucaí Amarelo. Two aircraft were used, T1 (hydraulic) and T2 (rotary nozzles). The application rates were established at 25 and 50 L ha⁻¹. The application quality was obtained by attaching Water-Sensitive Papers (WSPs) to the upper, middle, and lower parts of coffee trees, inside and outside the plants, in addition to the inter-row areas. The statistical Nested Crossed Design was applied to analyze the dataset for the experimental field with three distinct factors (RPA, application rate, and WSP position) and four replications. WSP position was the most determinant factor across all design effects, followed by RPA. The external layers of leaves received more droplets than internal parts of coffee trees. The WSP position information indicated that no droplets reached the middle interior parts of the plants or underneath them. The inter-row positions (soil) received significantly more drops than the coffee plants, regardless of application rate or RPA. The potential for drift to the soil was high in both applications. The Potential Drift Risks were more significant for RPA T2. Future studies may deepen understanding of the relationship between coverage and specific application models for coffee farming, as traditional application methods require improvements. Full article

The global agricultural sector faces escalating labor shortages and post-harvest losses, particularly in delicate crop handling. This study introduces an integrated soft robotic harvesting system addressing these challenges through four key innovations. First, a low-cost, high-yield fabrication method for silicone-based soft grippers is proposed, reducing production costs by 60% via compressive-sealing molds. Second, a decentralized IoT architecture with edge computing achieves real-time performance (42 fps to 73 fps) on affordable hardware (around $180 per node). Third, a lightweight vision pipeline combines handcrafted geometric features and contrast analysis for crop maturity assessment and gripper tracking under occlusion. Fourth, a Neo-Hookean-based statics model incorporating circumferential stress and variable cross-sections reduces tip position errors to 5.138 mm. Experimental validation demonstrates 100% gripper fabrication yield and hybrid feedforward–feedback control efficacy. These advancements bridge the gap between laboratory prototypes and field-deployable solutions, offering scalable automation for perishable crop harvesting. Full article

Thailand’s mango export industry faces significant challenges in meeting stringent international quality standards, particularly the costly phytosanitary X-ray irradiation process. Current fixed-dose irradiation methods result in substantial energy waste due to variations in fruit size. This research presents a low-cost, real-time system that integrates computer vision and artificial intelligence (AI) to optimize this process. By capturing a single top-view 2D image, the system accurately estimates the three-dimensional characteristics (width, height, and depth) of ‘Nam Dok Mai Si Thong’ mangoes. This dimensional data is crucial for dynamically adjusting the radiation dose for each fruit, leading to significant reductions in energy consumption and operational costs. Our novel approach utilizes a Linear Regression combined with Co-Kriging (LR + CoK) model to precisely estimate fruit depth from 2D data, a key limitation in previous studies. The system demonstrated high efficacy, achieving a dimensional estimation error (RMSE) of less than 0.46 cm and a size grading accuracy of up to 93.33 percent. This technology not only enhances sorting and grading efficiency but also offers a practical solution to lower the economic and energy burden of phytosanitary treatments, directly improving the sustainability of fruit export operations. Full article

Rising global temperatures pose a significant threat to agricultural productivity, making the early detection of plant stress crucial for minimizing crop losses. While hyperspectral remote sensing is a powerful tool for monitoring plant health, the precise spectral regions and most effective machine learning models for detecting thermal stress remain an open research question. This study presents a robust framework that utilizes eight state-of-the-art machine learning algorithms to classify the reflectance response of thermal-induced stress in two cultivars of bean plants. Our controlled experiment measured hyperspectral data across two growth stages and three stress conditions (pre-stress, during stress, and post-stress) using a spectroradiometer. The results demonstrate the high performance of several algorithms, with the Artificial Neural Network (ANN) achieving an impressive 99.4% overall accuracy. A key contribution of this work is the identification of the most contributory spectral ranges for thermal stress discrimination: the green region (530–570 nm) and the red-edge region (700–710 nm). This framework is a feasible and effective tool for modelling the hyperspectral response of thermal-stressed bean plants and provides critical guidance for future research on stress-specific spectral indices. Full article

Reliable differentiation of crops and weeds is essential for precision agriculture, where real-time detection can minimize chemical inputs and support site-specific interventions. This study presents the large-scale and systematic benchmark of 19 YOLO-family variants, spanning YOLOv3 through YOLOv11, for cotton–weed detection using the Cotton–8 dataset. The dataset comprises 4440 annotated field images with five categories: broadleaf weeds, grass weeds, and three growth stages of cotton. All models were trained under a standardized protocol with COCO-pretrained weights, fixed seeds, and Ultralytics implementations to ensure reproducibility and fairness. Inference was conducted with a confidence threshold of 0.25 and a non-maximum suppression (NMS) IoU threshold of 0.45, with test-time augmentation (TTA) disabled. Evaluation employed precision, recall, mAP@0.5, and mAP@0.5:0.95, along with inference latency and parameter counts to capture accuracy–efficiency trade-offs. Results show that larger models, such as YOLO11x, achieved the best detection accuracy (mAP@0.5 = 81.5%), whereas lightweight models like YOLOv8n and YOLOv9t offered the fastest inference ( 27 msper image) but with reduced accuracy. Across classes, cotton growth stages were detected reliably, but broadleaf and grass weeds remained challenging, especially under stricter localization thresholds. These findings highlight that the key bottleneck lies in small-object detection and precise localization rather than architectural design. By providing the first direct comparison across successive YOLO generations for weed detection in cotton, this work offers a practical reference for researchers and practitioners selecting models for real-world, resource-constrained cotton–weed management. Full article

This study developed identification models for five domestic rice varieties—Akitakomachi (Akita 31), Hitomebore (Tohoku 143), Hinohikari (Nankai 102), Koshihikari (Etsunan 17) and Nanatsuboshi (Soriku 163)—using fluorescence spectroscopy, near-infrared (NIR) spectroscopy, and machine learning. Two-dimensional fluorescence images were generated from excitation emission matrix (EEM) spectra in the 250–550 nm and 900–1700 nm ranges. Four machine learning hybrid models combining a convolutional neural network (CNN) with k-nearest neighbor algorithm (KNN), random forest (RF), logistic regression (LR), and support vector machine (SVM), were constructed using Python (ver. 3.13.2) by integrating feature extraction from CNN with traditional algorithms. The performances of KNN, RF, LR, and SVM were compared with NIR spectra. The NIR+KNN model achieved 0.9367 accuracy, while the fluorescence fingerprint+CNN model reached 0.9717. The CNN+KNN model obtained the highest mean accuracy (0.9817). All hybrid models outperformed individual algorithms in discrimination accuracy. Fluorescence images revealed at 280 nm excitation/340 nm emission linked to tryptophan, and weaker peaks at 340 nm excitation/440 nm emission, likely due to advanced glycation end products. Hence, combining fluorescent fingerprinting with deep learning enables accurate, reproducible rice variety identification and could prove useful for assessing food authenticity in other agricultural products. Full article

Most fruit classification studies rely on color-based features, but shape-based analysis provides a promising alternative for distinguishing subtle variations within the same variety. Tomato shape classification is challenging due to irregular contours, variable imaging conditions, and difficulty in extracting consistent geometric features. In this study, we propose an efficient and structured workflow to address these challenges through contour-based analysis. The process begins with the application of a Mask Region-based Convolutional Neural Network (Mask R-CNN) model to accurately isolate tomatoes from the background. Subsequently, the segmented tomatoes are extracted and encoded using Elliptic Fourier Descriptors (EFDs) to capture detailed shape characteristics. These features are used to train a range of machine learning models, including Support Vector Machine (SVM), Random Forest, One-Dimensional Convolutional Neural Network (1D-CNN), and Bidirectional Encoder Representations from Transformers (BERT). Experimental results observe that the Random Forest model achieved the highest accuracy of 79.4%. This approach offers a robust, interpretable, and quantitative framework for tomato shape classification, reducing manual labor and supporting practical agricultural applications. Full article

Chemical defoliation is an important management practice in cotton to facilitate mechanical harvesting and leaf removal and maintain lint quality. Recent advances in precision agriculture have enabled the development of autonomous robotic platforms with a targeted side-spraying system that can achieve good canopy penetration while preventing soil compaction and crop mechanical damage. A side-wise spraying system allows for application of defoliant at different canopy heights. However, information on the effects of staggered defoliation on cotton fiber quality is limited. Thus, field research was conducted to evaluate the effects of various staggered application timing intervals (15, 10, 8, 5, and 3 days) on fiber quality and compare them with standard over-the-top broadcast applications. Staggered defoliation affected fiber length, with significant differences observed for upper half mean length, fiber length based on weight, and upper quartile length. Fiber maturity was also influenced by staggered defoliation timing, with a 15-day interval resulting in the lowest micronaire and higher immature fiber content. The effects of staggered defoliation on other parameters, such as strength, uniformity, and trash characteristics, varied across locations. The findings highlight the potential of robotic systems for chemical spraying and emphasize the need for further research on more precise and targeted application of defoliants to improve fiber quality. Full article

Clogging is a major constraint to the agricultural reuse of produced water; however, ultra-low-frequency dynamic electronic pulses (EPs) can help control biofouling in drip emitters. This study aimed to evaluate the reduction in clogging in non-self-compensating emitters applying onshore oil-and-gas-produced water treated with EP. Three experimental benches were assembled using drip irrigation units supplied with different water sources: water supply (WS), produced water with EP (OPW + EP), and produced water without treatment (OPW). Hydraulic performance was monitored every 40 h for 400 h using average flow rate variation (AFVR), flow variation coefficient (FVC), and distribution uniformity (UD) indices. Data were analyzed using RT-1 analysis with Bonferroni post hoc tests. Results showed that the interaction between water sources and evaluation times significantly (p ≤ 0.01) affected the hydraulic indices. After 400 h, the indices ranked as UD and FVC: WS > OPW + EP > OPW, and AFVR: OPW + EP = WS > OPW. Although OPW presented a low risk of clogging, the application of EP mitigated the obstruction and maintained higher uniformity by reducing clogging. These findings demonstrate that ultra-low-frequency electronic pulses are an innovative anti-clogging technology and provide insights for the sustainable application of produced water. Full article

This study presents the development of a portable DOBI meter prototype designed for the rapid, low-cost evaluation of crude red palm oil (RPO) quality. The device employs two narrow-spectrum LEDs (UV at 269 nm and visible at 446 nm) as light sources, paired with a broadband photodiode (PD) detector to measure light absorption in a quartz cuvette containing 95% hexane-diluted oil samples. Dedicated LED driver circuits, a PD receiver module, and microcontroller-based data acquisition and display systems were integrated into a compact enclosure. Calibration procedures involved the measurement of LED emission spectra and PD responses, followed by standard curve generation using known RPO concentrations. The results from the DOBI meter were validated against a commercial spectrophotometer (Merck Prove 600), demonstrating high accuracy with less than 5% deviation. Further analysis of RPO extracted from microwave-treated mesocarps showed consistent DOBI values and carotenoid concentrations across both instruments. The developed device offers a reliable, accessible alternative for assessing palm oil quality, particularly in field or small-scale industrial settings. Full article

Accurate detection of apples in orchards under variable weather and illumination remains a key challenge for precision horticulture. This study presents a flexible framework for automated ensemble selection and optimization of convolutional neural network (CNN) inference. The system integrates eleven ensemble methods, dynamically configured via Pareto-based multi-objective optimization balancing accuracy (mAP, F1-Score) and performance (FPS). A key innovation is its pre-deployment benchmarking whereby models are evaluated on a representative field sample to recommend a single optimal model or lightweight ensemble for real-time use. Experimental results show ensemble models consistently outperform individual detectors, achieving a 7–12% improvement in accuracy in complex scenes with occlusions and motion blur, underscoring the approach’s value for sustainable orchard management. Full article

The mechanized harvesting of peanut crops results in both visible and invisible losses. Therefore, monitoring and accurately determining pod maturation are essential to minimizing such losses. The objectives of this study were to (i) identify the most relevant variables for estimating peanut pod maturation and (ii) estimate two maturation indices (brown and black classes; orange, brown, and black classes) using Remote Sensing (RS) and Artificial Neural Networks (ANN), while assessing the generalization potential of the models across different areas. The experiment was carried out in two commercial peanut fields in the state of São Paulo, Brazil, during the 2021/2022 and 2022/2023 growing seasons, using the IAC 503 cultivar. Data collection began one month before the expected harvest date, with weekly intervals. Spectral variables and vegetation indices were obtained from orbital remote sensing (PlanetScope), while climatic data were retrieved from NASA POWER. For analysis, two ANN architectures were employed: Multilayer Perceptron (MLP) and Radial Basis Function (RBF). The dataset from the Cândido Rodrigues site was split into 80% for training and 20% for testing. The model was then evaluated and generalized using data from the Guariba site. Variable selection involved filtering via Principal Component Analysis (PCA) followed by the Stepwise method. Both models demonstrated high accuracy (R² ≥ 0.90; MAE between 0.06 and 0.07). Generalization tests yielded promising results (R² between 0.59 and 0.64; MAE between 0.13 and 0.17), confirming the robustness of the approach under different conditions. Full article

Sorghum (Sorghum bicolor L.) is a resilient crop with high relevance in tropical and semi-arid regions, where nutritional deficiencies, particularly of nitrogen (N) and potassium (K), limit yield. This study evaluated the potential of RGB imagery combined with machine learning to detect N and K deficiencies in sorghum at different phenological stages. The traditional models showed significant limitations in distinguishing nutritional status, especially at the early V4 stage, where accuracies remained below 40%. At the flowering stage, their performance improved for nitrogen detection, reaching up to 58% accuracy, but remained insufficient for potassium (below 30%). In stark contrast, the CNN demonstrated substantially superior performance, effectively identifying even subtle visual symptoms. For nitrogen deficiency, the CNN achieved high accuracies of 76% at the V4 stage and 87% at flowering. While potassium classification proved more challenging overall, the CNN still outperformed traditional models, reaching 55% accuracy at flowering. These results indicate that deep learning is a powerful and viable low-cost tool for the early and accurate diagnosis of nutrient deficiencies in sorghum, overcoming the limitations of conventional machine learning approaches. Full article