Returning straw to fields is a fundamental agricultural practice that enhances soil structure, mitigates erosion, improves fertility and productivity, and supports subsequent crop cultivation. To address environmental imbalances, inconsistent growth, and pest, disease, and weed proliferation caused by the uneven distribution of existing straw return machinery, this study introduces a uniform straw cover detection and control system utilizing machine vision and deep learning segmentation. An optimized YOLOv11s-seg network was developed to extract geometric and positional data from scattered straw. Using an upper computer, the system calculates the straw center deviation, coverage ratio, and preset differences. Simultaneously, a uniform coverage control system was designed around a single-chip microcomputer and a hydraulic system. Utilizing visual inspection data, the system regulates the straw scattering angle and width in real time. The optimized segmentation model demonstrates exceptional performance, achieving an average accuracy of 96.7% for chaff detection. Field tests indicated that, across various operational speeds, the qualified scattering width rate exceeds 80% following system optimization. Furthermore, the average scattering unevenness surpassed the national standard requirement of 20%, confirming highly effective closed-loop control. The enhanced YOLOv11s-seg model satisfies the rigorous accuracy and efficiency demands for real-time field detection. This vision-based control system accurately regulates straw spreading direction and width, significantly improving uniformity and operational efficiency. These findings provide essential technical support for the automation and intelligent development of straw shredding and returning machinery.

Accurate identification and quantitative assessment of fish feeding intensity are pivotal for enhancing aquaculture production efficiency. Currently, feeding intensity is mainly assessed based on fish school feeding images with a single feature, overlooking the interdependencies between individual fish and the fish school’s behavior. Therefore, this paper presents a method based on detecting individual fish heads to characterize the feeding aggregation speed and the average swimming speed of the fish school, thereby quantifying the fish school’s feeding intensity. First, the improved YOLOv11n-ALL model was employed to detect individual fish heads, resulting in improved detection performance, increasing inference speed, and reducing computational complexity. Additionally, feeding aggregation speed and average swimming speed indices for fish schools were constructed by combining the YOLOv11n-ALL model with the ByteTrack algorithm to track and extract the centers of individual fish heads’ detection boxes. Finally, the fish school feeding kinetic energy was assessed using the feeding aggregation speed and average swimming speed dual indices, and the fish school feeding intensity levels were classified according to the feeding kinetic energy. Experimental results reveal that the improved YOLOv11n-ALL model achieved an average detection precision (mAP50) of 94.13% for detecting fish heads, reduced the parameter count by 22.09%, and exhibited a computational complexity of 6.4 GFLOPs. Furthermore, the classification model of fish school feeding intensity, quantified by the dual indices of average swimming speed and feeding aggregation speed, achieved a detection accuracy of 97.41%. This method digitizes detection results, enabling rapid classification of fish school feeding intensity and demonstrating its effectiveness for feeding intensity assessment and the development of scientific feeding strategies.

In orchard inter-rows, weeds often grow unevenly in patches owing to terrain cluttered with stones, branches, and debris. Empirical operating parameters can cause cutting and drive motors of mowing robots to stall under sudden loads, reducing efficiency and quality and risking damage. This study introduces a load-adaptive control strategy that links blade rotation and chassis forward speed. A fuzzy controller regulates forward speed based on blade motor deviation and its rate of change, while a sliding-mode controller incorporating a torque-feedforward state observer controls the cutting motor. The chassis employs a Smith-radial basis function (RBF)-proportionalintegral-derivative (PID) controller to suppress phase lag, with parameters optimized by an improved chimp optimization algorithm (ICOA) employing tent reverse learning, distance-weighted updating, and Gaussian mutation. Simulations indicate that ICOA converges in 15 iterations with a target function value of 0.2413, reducing error by 13.57 % over the baseline. The ICOA-Smith-RBF-PID controller achieves a rise time of 0.0335 s (step), with maximum speed errors of 0.0039 m center dot s- 1 (ramp) and 0.0038 m center dot s- 1 (sinusoidal). The blade speed controller responds in 0.0163 s (step) and 0.0246 s (load step), with a maximum speed error of 15.33 r center dot min- 1 under continuous disturbance. Field tests across five rows of a single orchard yielded mean mowing, grassbreaking, and cutting stability rates of 92.67 %, 89.41 %, and 88.98 %, respectively, with no motor stalling. The proposed strategy ensures stable, efficient, and precise mowing under dynamic orchard conditions; demonstrates robust applicability in hilly orchards; and provides a reference for coordinated control in agricultural robotics.

Anthracnose, a severe postharvest disease in mangoes caused by Colletotrichum gloeosporioides, leads to substantial economic losses due to its latent infection. Conventional spectral-based detection relies typically on averaged one-dimensional (1D) spectral data, neglecting spatial information related to disease infection. This study introduces an early detection method combining hyperspectral imaging with a three-dimensional convolutional neural network (3D-CNN) to simultaneously extract spatial and spectral features. The developed 3D-CNN model achieved 95.24 % accuracy in distinguishing healthy, asymptomatic, and symptomatic mango samples, outperforming both 1D-CNN (92.52 %) and traditional machine learning models (65.31 %–90.48 %). Gradient-weighted class activation mapping (Grad-CAM) interpretation revealed that the 3D-CNN focuses on distinct spectral wavelengths for different infection stages: 1067–1161 nm for healthy, 1217–1298 nm for asymptomatic, and 1204–1373 nm for symptomatic, corresponding to biochemical changes during infection. In contrast, the 1D-CNN utilized the same wavelengths across all stages (1000–1117 nm). Spatially, the 3D-CNN also exhibited selective focus on different fruit regions consistent with infection status, such as the center of healthy fruit and infected areas of symptomatic samples. This interpretable approach offers a powerful tool for early anthracnose detection and holds significant potential for improving mango disease management.

To mitigate tillage-depth fluctuations induced by surface undulations and actuator nonlinearities in horticultural electric tractors, this study develops a predictive depth control method. A novel MSCNN-BiLSTM-Attention model is constructed to forecast pitch-angle variations. It utilizes multi-scale convolution to extract disturbance features across different temporal scales, while bidirectional temporal modeling combined with a multi-head attention mechanism enhances temporal representations. Model hyperparameters are optimized using an improved beluga whale optimization algorithm. The predicted pitch angle is integrated as feedforward compensation into the control loop. By fusing this prediction with a rotary-tillage kinematic model, future tillage depth is estimated, enabling the design of an RBF-based active disturbance rejection controller (RBF-ADRC) for adaptive compensation of system nonlinearities and time-varying disturbances. The proposed prediction model achieved MAE and RMSE of 0.0526 degrees and 0.0686 degrees, respectively, with a test-set R2 of 0.9931, and errors primarily within +/- 0.03 degrees. Simulations demonstrated that the proposed prediction-assisted control strategy outperforms RBFPID, optimal sliding mode, and model predictive controllers under step, sinusoidal, and slope disturbances, achieving zero overshoot, a steady-state error of 0.001 cm, and reduced dynamic errors. Greenhouse field experiments further validated the method, yielding average tillage depths of 4.03, 8.14, and 11.59 cm against targets of 4, 8, and 12 cm, with maximum fluctuations of approximately 0.6 cm. Overall, this deep learning-based predictive feedforward strategy significantly improves tillage-depth stability and control precision under complex surface conditions, providing a practical solution for intelligent electric tractors in protected horticulture.

Accurate weed models are critical for elucidating mechanical weeding interactions. However, key parameters and targeted models for flexible weed-body fracture are lacking. We propose a novel, discrete-element model (DEM) for simulating flexible weed stress fracture using the Hertz–Mindlin with Bonding V2 contact model. The proposed model facilitates high-fidelity root-architecture reconstruction and accurately reproduces root mechanical responses, including fracture, fragmentation, tension, and shear. A full-chain multi-index constrained parameter calibration system was established via physico-mechanical testing of barnyard grass (Echinochloa crus-galli) roots, with a simulated-measured contact-angle deviation of just 2.23%. The key calibrated parameters included a normal stiffness and shear stiffness of 1.98243 × 1014 N·m−3, normal strength and shear strength of 2.03399 × 1010 MPa, bonding V2 radius of 5.771 × 10−2 mm, and inter-particle contact radius of 6.627 × 10−2 mm, ensuring model accuracy and biological authenticity. Moreover, we developed an adaptive evolutionary genetic hippopotamus optimisation algorithm (AEG-HOA) for the global optimization of DEM parameters, with the ultimate tensile and shear strengths as response metrics. Comparative results revealed that AEG-HOA achieved 1.11% and 1.08% calibration errors, far below those of conventional methods (4.23% and 3.23%, respectively), attaining convergence within nine iterations with robust stability. This work provides a high-precision standardised DEM for mechanical weeding interaction research and efficient plant-simulation-parameter calibration.

Background Sustainable dehydration is needed to reduce postharvest losses and support low-carbon agri-food processing; however, conventional drying remains highly energy-intensive. Scope and Approach This review synthesizes recent advances in ozone application in agri-food drying applied as aqueous or gaseous pretreatments, as ozone-enriched drying air (co-treatment), or as post-drying sanitation, with emphasis on mechanisms that modify tissue microstructure and mass-transfer pathways across major commodity groups. Key Findings and Conclusions Under well-controlled conditions, ozone can enhance drying kinetics (e.g., increased effective moisture diffusivity) and reduce drying time while improving microbial safety, as it ultimately decomposes to oxygen without leaving chemical residues. Reported quality responses are commodity-and protocol-dependent; benefits may include improved color stability, rehydration behavior, and retention of selected bioactives, whereas excessive exposure can promote oxidative deterioration. Industrial translation is limited by challenges in achieving uniform ozone delivery, implementing reliable monitoring and control, ensuring occupational safety, and the scarcity of techno-economic and life-cycle assessment evidence. Future studies should prioritize standardized ozone dosimetry and reporting, pilot-scale validation, and closed-loop control to define commodity-specific operating windows that balance intensification gains against oxidative risks.

Dust accumulation on greenhouse roofs remains a significant challenge, significantly reducing light transmission and crop productivity, and creating a need for efficient and automated cleaning solutions. Current manual or semi-automated methods are labor-intensive, inconsistent, and often unsuitable for large greenhouse structures. This study presents the design and simulation of a solar-powered autonomous cleaning system for plastic greenhouse roofs. The proposed system integrates a photovoltaic (PV) energy supply with an automated cleaning mechanism composed of rotating brushes, flexible wipers, and a water pump to effectively remove dust and contaminants from the greenhouse surface. A lightweight mobility unit is incorporated to enable stable movement and operation on inclined greenhouse roofs. Structural and performance analyses were conducted using three-dimensional modeling and finite element simulations to evaluate stress and deformation in the frame, brush, and wiper. The maximum stress occurred at the upper joints, while total deformation remained below 2.1 mm, confirming adequate structural safety under operational loads. The cleaning efficiency was tested at two-wheel speeds (25 rpm and 50 rpm) and three roof positions (top, middle, bottom), demonstrating stable operation and uniform cleaning performance. The proposed system offers a practical solution for maintaining greenhouse transparency, reducing manual labor, and supporting sustainable agricultural management.

Abstract Traditional greenhouse cleaning methods are labor-intensive, prone to human error, and inefficient, often compromising light transmittance and productivity. To address these challenges, this study proposes an autonomous robot designed to clean greenhouse roofs efficiently and reliably. The robot features an integrated cleaning system with adjustable brushes, wipers, and water sprinklers, ensuring optimal performance and significantly improving light transmittance. Powered by a 500 W PV system, it utilizes electric wheels for smooth, stable movement and incorporates a replaceable brush-wiper mechanism for enhancing durability and maintenance efficiency. The design process involved SolidWorks modeling for mass properties, CFD simulations with the k-ε turbulence model to evaluate wind load conditions, and ANSYS structural analysis to confirm durability under extreme wind speeds of up to 126 km/h (ten times greater than normal conditions). Structural tested at different robot’s rotational speeds 25 rpm and 50 rpm confirmed optimal performance at 25 rpm, balancing cleaning efficiency and long-term durability. Additionally, the robot incorporates advanced control unit with sensors for autonomous operation, real-time light transmission monitoring, and navigation capabilities, distinguishing it from traditional manual or semi-automated methods. The results demonstrated robust performance in extreme conditions, surpassing existing systems limited to standard weather. The robot’s performance is limited by speed (0.35 m/s), battery life, roof complexity, maintenance, adaptability, and cost, indicating areas for improvement. Future developments will integrate AI for autonomous decision-making, GPS for precise navigation, and a smart cleaning system to optimize performance based on real-time data, further reducing maintenance costs and ensuring optimal greenhouse lighting.

Hypervolatile species such as carbon monoxide (CO) and molecular nitrogen (N-2) have been detected in comets, and could be used to constrain comet formation temperature conditions if their presence is due to freeze-out and/or entrapment. Here, we instead explore another plausible origin of cometary hypervolatiles: photodissociation of less volatile species. We characterize CO and N-2 formation following ultraviolet (UV) irradiation and electron bombardment of carbon dioxide (CO2), ammonia (NH3), H2O:CO2, H2O:NH3, and H2O:CO2:NH3 cometary ice analogs. We find that CO and N-2 form in all photoprocessed ices at temperatures between 10 and 100 K, resulting in 0.4%-0.9% CO and 0.03%-0.7% N-2 relative to water, and CO/CO2 and N-2/NH3 mixing ratios of 2.5%-62% and 0.7%-9%, respectively, across the experiments. Because our initial ices are reasonably well matched to interstellar ices and we use a UV exposure similar to a dark cloud, we can compare the resulting ratios directly to cometary abundances. Such a comparison shows that, while only a few of CO observations in comets are readily explained by photodissociation, almost all observed cometary N-2 can be accounted for by photodissociation of NH3 embedded in water ice. The latter result is also consistent with observed similarly elevated isotopic ratios of N-2 and NH3 in 67P. Taken together, our results suggest that N-2/H2O ratios <1% should be used cautiously when inferring a comet's formation location, while the more substantial CO abundances seen in many comets do likely imply entrapment at low ice temperatures.

Rice is one of the major staple crops in China, and its yield is closely tied to national food security and farmers’ economic returns. Lodging in rice not only reduces the efficiency of mechanical harvesting but also severely impacts yield and grain quality. Therefore, accurately identifying lodged areas is of great importance. This study proposes a rice lodging detection method based on UAV-acquired multispectral remote sensing imagery. High-resolution, multi-temporal images were collected over paddy fields in Yuhang District, Zhejiang Province, using DJI Mavic 3 M and M300 UAVs. A dataset was constructed via image cropping and data augmentation. Two deep learning models—U-Net with a VGG-16 backbone and DeepLabv3+ with a MobileNetv2 backbone—were compared for semantic segmentation performance. Experimental results show that the U-Net model achieved superior performance on the validation set, with a mean Intersection over Union (MIoU) of 91.57 %, mean Pixel Accuracy (MPA) of 95.83 %, Precision of 95.27 %, Recall of 95.83 %, and training/validation losses of 0.106 and 0.151, respectively, outperforming the DeepLabv3+ model. Additionally, the impact of different training-validation data split ratios was examined. The U-Net model showed better generalization and stability when trained with a 9:1 split compared to an 8:2 split. Furthermore, based on the semantic segmentation results, the area of lodged rice was estimated and compared against ground-truth measurements. The U-Net model produced minimal relative error, with a maximum deviation of <3 %, demonstrating strong practical applicability. These findings suggest that the U-Net model not only offers high accuracy and stability but also provides a reliable technical foundation for agricultural disaster monitoring and precision management using high-resolution UAV imagery.

Visible/near-infrared spectroscopy provides a non-destructive approach for evaluating soluble solids content (SSC) of pears (Pyrus pyrifolia Nakai). However, variations among pear cultivars, especially in pear color, markedly affect spectral reflectance, thereby limiting the cross-cultivar SSC prediction. To address this, we developed SpecColorNet, a multimodal deep learning framework that integrates spectral and peel color data to accurately predict the SSC of six different pear cultivars simultaneously, and interpreted its decision-making mechanism with gradient-weighted class activation mapping++ (Grad-CAM++). The prediction accuracy of the multicultivar SpecColorNet was improved by 9.42 % compared to the multi-cultivar model based solely on spectral data with corresponding RMSEP values of 0.63, 0.60, 0.92, 0.70, 0.54 and 0.55 degrees Brix for six different pear cultivars. In addition, the prediction accuracy of SpecColorNet was comparable to that of single-cultivar spectral models. The interpretation analysis indicated that the SpecColorNet successfully directed the model's attention to the 555-640 nm spectral region, where the most marked cross-cultivar differences occur, thereby improving its generalization and accuracy. Overall, this study proposed a multimodal approach for robust SSC prediction across diverse pear cultivars, which overcame the challenge of cultivar diversity and peel color variability to enable multi-cultivar models with enhanced robustness over pure spectroscopy-based methods. (c) 2025 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

To improve the torque performance of axial flux hub motors (AFHMs) and address the performance mismatch issues caused by traditional single-field optimization, a multiobjective optimization method based on a novel multiphysics field-coupled model is proposed in this article. The establishment of an accurate model is essential for the optimization. Therefore, a novel electromagnetic-thermal-fluid multiphysics coupling model (EM-T-F MCM) for the oil-immersed AFHM is developed based on the thermal network method (TNM). This model comprehensively considers the effects of temperature on the conductivity of windings and silicon steel, the performance of PMs, and the thermophysical properties of the cooling medium. Compared to traditional coupling models, the average prediction accuracy of the EM-T-F MCM is enhanced by 4.17%, and it has been validated under both dynamic and steady-state conditions. Then, an improved nondominated sorting genetic algorithm II (NSGA-II) is proposed for multiobjective optimization of AFHMs based on the EM-T-F MCM. Finally, a prototype motor is fabricated, and the test platform is built to verify the validity of AFHMs and the proposed method. The results indicate that the optimized AFHM torque is improved by approximately 8% with nearly 50% reduced torque ripples, while the high-efficiency operation region is expanded by approximately 18%.

Detecting postharvest tomato ripeness is essential for quality control. To reveal the evolution of complex conductivity σ∗and complex permittivityε∗ during tomato ripening, this study integrates bioimpedance spectroscopy (BIS) and finite element method (FEM) to predict postharvest tomato maturity. Based on the Maxwell-Wagner equation, σ∗ and ε∗ were derived from the measured impedance and conductance data. BIS measurements were conducted on whole tomatoes at four ripening periods and their components (pericarp, chamber, core, cavity). A finite element model was implemented in COMSOL to simulate electrical field distribution and quantify tissue-specific differences. Continuous monitoring of white ripening period tomatoes was used to validate the model, yielding an average accuracy of 85.16%, peaking at 92.86% in red ripening period and dipping to 80.30% in color change period, elucidate the dynamic changes in electrical properties during tomato ripening and provide a basis for nondestructive maturity assessment.

Strawberries grown on elevated stands usually suffer from fruit occlusion issues, which severely limit the implementation of strawberry recognition and picking point localization, and the embedded devices carried by strawberry picking robots have high requirements for model lightweighting, posing a dual challenge to the efficient execution of automated picking tasks by robots. To address this issue, this study proposes a method for strawberry recognition and picking point localization in multi-occlusion scenes based on a lightweight keypoint detection model. Firstly, a strawberry dataset covering no, slight, moderate, and heavy occlusion scenes is constructed. Then, a lightweight strawberry recognition and keypoint detection network, LS-net, is proposed. LS-net improves the spatial relationship modelling capability between strawberries and stems by integrating the lightweight MobileNetv4 backbone with the Mobile Grouped-Query Attention mechanism; improves the feature pyramid network using depthwise separable convolutions and incorporates an anchor-free decoupled head network to reduce computational complexity while maintaining detection accuracy; and introduces the Matrix Non-Maximum Suppression to optimize the processing of overlapping strawberries, which effectively reduces the false negative detections. Based on the keypoint detection results from LS-net, the picking point coordinates and stem pose are calculated after a series of processes such as region-of-interest extraction, binarization, and depth data alignment. The experimental results show that the accuracy of LS-net is 91.07%, the mean average precision is 93.93 %, and the average pixel Euclidean distance is 4.79. By deploying LS-net to the embedded device, its frames per second reaches 78.2, and the success rates of 3D picking point localization and stem pose estimation are 84.07% and 81.32 %, respectively. LS-net and related methods provide a visual recognition solution adapted to embedded devices for strawberry picking robots. (c) 2025 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Precision seeding represents a key advancement in rapeseed mechanization, offering an effective strategy to minimize seed usage, reduce labor requirements, and improve efficiency. Accurate real-time seed-flow monitoring is essential for effectively maintaining sowing quality. Conventional detection techniques struggle with high-frequency seed discharge, leading to missed detections of overlapping seeds, limited resistance to interference, and diminished accuracy. This study introduces a novel rapeseed seed-flow detection method and sensor system based on microwave resonant cavity perturbation with absorptive damping. By constructing a unidirectional microwave field and incorporating absorbent materials, multipath reflections within the cavity were significantly mitigated, thereby improving detection stability and precision. CST simulations confirmed the directional damping and absorption characteristics of the materials, introducing negligible attenuation (mean 0.41 dB) along the main propagation path while providing strong suppression (mean 16.92 dB) of unwanted field components. The signal-processing circuit features two-stage intermediate-frequency amplification, RMS detection, and multithreshold comparators. When integrated with an overlapping-seed recognition algorithm and an STM32 microcontroller, the system achieved real-time counting of both single and overlapping seeds. Bench tests revealed that at a seeding frequency of approximately 31 Hz, the detection error of the device was below 2.3%, compared to over 4.1% for traditional photoelectric and fiber-optic sensors. Additional tests using multiple rapeseed varieties further verified robust single-seed detection accuracy (>97%). The recognition rates for double and triple overlapping seeds reached 76.3% and 42.0%, respectively. In field trials, detection accuracy remained above 95.5% with stable performance under operational speeds of 2.8-4.8 km/h and seeding frequencies of 12.7-30.7 Hz. The results offer an effective technical solution for precise seed-flow monitoring in rapeseed direct seeding, enhancing precision planter intelligence.

The aim of this study is to enhance the detection accuracy of rice chlorophyll content under complex backgrounds, optimize rice production management models, and improve the efficiency and quality of grain production. To achieve this, a spectral device for rice chlorophyll content detection with integrated background classification capability was developed. The device employed a MobileNetV4-Conv-Small-based model for rice background classification, enabling the categorization of clear, muddy, and green algae-covered backgrounds. The classification results showed that the model performed best under the green algae-covered background, with all performance metrics exceeding 97%. The muddy background followed, with metrics surpassing 94%, while the clear background proved more challenging, though the metrics still exceeded 93%. By combining preprocessing techniques with convolutional neural networks (CNNs), distinct rice chlorophyll content detection models were developed for each background type. For clear backgrounds, the optimal model was FD + CNN, with R2, RMSE, and RPD values of 0.975, 5.191, and 6.318, respectively. For muddy backgrounds, the optimal model was SS + CNN, with R2, RMSE, and RPD values of 0.627, 18.249, and 1.638, respectively. For green algae-covered backgrounds, the SS + CNN model also achieved the best results, with R2, RMSE, and RPD values of 0.719, 16.417, and 1.885, respectively. Field experiments confirmed that the device achieved a rice background classification accuracy of 94.67% and a relative error compliance rate of 84.00% for chlorophyll content detection. These results demonstrate the feasibility of integrating background classification and chlorophyll content detection for rice cultivation.