Agricultural robotics has emerged as a research area within robotics, with a particular focus on designing end effectors that are adapted to the physical characteristics of the target fruits. Acquiring a comprehensive understanding of the physical and mechanical properties specific to tomato fruits not only minimizes mechanical damage during grasping processes but also serves as a foundation for the optimal design of gripping components. In this study, the Syngenta Sibede variety of tomatoes was used as the experimental material. The reversible viscoelastic behavior and deformation characteristics of tomato fruits were approximated using a four-element Burgers model through creep testing. The fitting coefficients for the model exceeded 0.99. The creep parameters for the four ripening stages of tomatoes were obtained, and the correlation between the ripening stage, deformation value, and creep parameters was analyzed. Correlation analysis was performed to examine the relationships between each parameter and creep deformation, revealing significant and highly significant correlations. Inter-parameter correlations were also found to be highly significant. Puncture tests were conducted on tomatoes. The exocarp rupture force of the green-ripening stage was 9.224 ± 0.901 N, which was 53.87%, 70.63%, and 104.01% higher than that of the semi-ripening stage, early firm-ripening stage, and mid-late firm-ripening stage, respectively. This study suggests that when harvesting tomatoes at the semi-ripening stage and beyond, attention should be paid to trimming the stem. Compression experiments were conducted on tomatoes, and it was discovered that under the same ripening stage, the axial compressive rupture force of tomatoes was greater than the radial rupture force. Tomatoes exhibited anisotropic behavior. The grasping direction is axial, which can be used as the new design direction of the end-effector.

To solve the problem of the lack of physical properties of pedicels and the changing pattern for designing the end-effector of tomato harvesting robot and different harvesting modes, research was conducted on the physical properties of tomato pedicels and their change patterns. Using a Universal TA texture analyzer, tensile, three-point bending, and shearing tests were performed on tomato pedicels in the early firm-ripening stage. The tomato variety used was Syngenta Spectrum, cultivated seasonally with two crops per year. Spring crop tomatoes were used in this study. The experimental results provide a theoretical basis for designing tomato harvesting robots across three harvesting modes. Tensile tests measured the pull-off force and tensile strength of the abscission zone with varying diameters. These results are crucial for designing robots using a tensile harvesting mode. The location of the tomato pedicel significantly affects the shearing force. A one-way test was conducted on the shearing part. The results showed that the shearing force and energy required for the proximal pedicel are significantly greater than for the distal pedicel. To reduce the shearing force and energy needed by the end-effector’s shearing mechanism on distal pedicels, a response surface test was conducted. Three factors were examined: shearing speed, angle, and distal pedicel diameter. Design–Expert software optimized these factors to minimize shearing energy and force, leading to the best shearing parameters for different distal pedicel diameters. From the three-point bending tests, the average maximum bending breaking force, bending modulus, and bending strength of the tomato abscission zone were determined. These findings offer a theoretical basis for designing tomato harvesting robots with a bending-type harvesting mode.

An optimized tumbling process can significantly improve the oxygen dissolving capacity of composting and fertilizer quality: by increasing the fluffiness of the lower layer of the pile, localized anaerobic fermentation can be avoided, thereby enhancing compost quality. This paper presents a method for improving the oxygen dissolving capacity of rotary drum food waste composting through a combination of simulation optimization and experimental validation. First, the discrete element method was used to optimize the key parameters of the tumbling process. The response surface method was then employed to analyze the composting test results and determine the optimal conditions. To ensure the reliability of the equipment under this method, failure risk analysis was conducted using the finite element method. The simulation optimization results showed that with a rotary drum reactor speed of 3.5 r/min, a horizontal angle of inclination of 2.5°, a mixing blade angle of inclination of 43°, and a blade pitch of 580 mm, the fluffiness of the lower layer of the pile increased by 8.515%, achieving the best tumbling and indirectly enhancing oxygen dissolving capacity. The maximum deformation of the load-bearing components was only 0.0548 mm, and the minimum safety factor was 4.408 (≥1 is considered safe). A 14-day composting experiment was conducted to validate the optimized parameters. The results showed that the maximum temperature of the compost pile reached 68.34 °C (lasting 7 days), with the pH, moisture content, C/N ratio, humus substances, humic acid, and fulvic acid contents of the fertilizer all meeting or exceeding the levels recommended by Chinese national standards. These findings indicate that the optimized tumbling device effectively improved the stability and dissolved oxygen efficiency of food waste composting, providing valuable practical insights and a research foundation for enhancing oxygen efficiency in the composting of other organic wastes.

Intelligent detection of tomato seedling transplant quality represents a core technology for advancing agricultural automation. However, in practical applications, existing algorithms still face numerous technical challenges, particularly with prominent issues of false detections and missed detections during recognition. To address these challenges, we developed the ESG-YOLO object detection model and successfully deployed it on edge devices, enabling real-time assessment of tomato seedling transplanting quality. Our methodology integrates three key innovations: First, an EMA (Efficient Multi-scale Attention) module is embedded within the YOLOv8 neck network to suppress interference from redundant information and enhance morphological focus on seedlings. Second, the feature fusion network is reconstructed using a GSConv-based Slim-neck architecture, achieving a lightweight neck structure compatible with edge deployment. Finally, optimization employs the GIoU (Generalized Intersection over Union) loss function to precisely localize seedling position and morphology, thereby reducing false detection and missed detection. The experimental results demonstrate that our ESG-YOLO model achieves a mean average precision mAP of 97.4%, surpassing lightweight models including YOLOv3-tiny, YOLOv5n, YOLOv7-tiny, and YOLOv8n in precision, with improvements of 9.3, 7.2, 5.7, and 2.2%, respectively. Notably, for detecting key yield-impacting categories such as “exposed seedlings” and “missed hills”, the average precision (AP) values reach 98.8 and 94.0%, respectively. To validate the model’s effectiveness on edge devices, the ESG-YOLO model was deployed on an NVIDIA Jetson TX2 NX platform, achieving a frame rate of 18.0 FPS for efficient detection of tomato seedling transplanting quality. This model provides technical support for transplanting performance assessment, enabling quality control and enhanced vegetable yield, thus actively contributing to smart agriculture initiatives.

Environmental control based on growth stage is critical for enhancing the yield and quality of industrially cultivated Pleurotus pulmonarius. Challenges such as scene complexity and overlapping mushroom clusters can impact the accuracy of growth stage detection and target segmentation. This study introduces a lightweight method called the real-time detection model for the growth stages of P. pulmonarius (GSP-RTMDet). A spatial pyramid pooling fast network with simple parameter-free attention (SPPF-SAM) was proposed, which enhances the backbone’s capability to extract key feature information. Additionally, it features an interactive attention mechanism between spatial and channel dimensions to build a cross-stage partial spatial group-wise enhance network (CSP-SGE), improving the feature fusion capability of the neck. The class-aware adaptive feature enhancement (CARAFE) upsampling module is utilized to enhance instance segmentation performance. This study innovatively fusions the improved methods, enhancing the feature representation and the accuracy of masks. By lightweight model design, it achieves real-time growth stage detection of P. pulmonarius and accurate instance segmentation, forming the foundation of an environmental control strategy. Model evaluations reveal that GSP-RTMDet-S achieves an optimal balance between accuracy and speed, with a bounding box mean average precision (bbox mAP) and a segmentation mAP (segm mAP) of 96.40% and 93.70% on the test set, marking improvements of 2.20% and 1.70% over the baseline. Moreover, it boosts inference speed to 39.58 images per second. This method enhances detection and segmentation outcomes in real-world environments of P. pulmonarius houses, offering a more accurate and efficient growth stage perception solution for environmental control.

The operation of electric rice reaper binders in hilly and mountainous areas currently faces the challenges of poor conveying efficiency and high harvest losses caused by the large dispersion of rice stem posture angles. In this study, we propose a multiparameter collaborative optimization method for improving header structure in an effort to address these challenges. First, key parameters influencing lifting performance and their operational ranges were determined based on a theoretical analysis of the stem-lifting mechanism’s kinematic characteristics. A dynamic model simulating the header’s lifting process was developed by using the ADAMS multibody dynamics platform. Subsequently, we designed a quadratic regression orthogonal rotation combination experiment with three factors, i.e., the stem-lifting speed ratio coefficient, the cutter installation position, and the header tilt angle, using the stem-lifting angle as the evaluation metric. The variance in the experimental data was analyzed with Design-Expert 13.0, and response surface methodology (RSM) was applied to elucidate the parameter interaction effects. The optimal parameter combination was identified as a speed ratio coefficient of 2.14, a cutter installation position of 258.79 mm, and a header tilt angle of 62.63°, yielding a theoretical stem-lifting angle of 2.36°. Field validation tests demonstrated an actual stem-lifting angle of 2.44° (relative error: 3.39%) and a header loss rate of 0.59%, representing a 49.6% reduction compared with the pre-optimized design. These results confirm that the optimized header satisfies operational requirements for hilly terrain rice harvesting, providing both theoretical guidance and technical advancements for the design of low-loss harvesting machinery.