Soybeans are a crucial crop, and it is therefore necessary to make accurate predictions of their mechanical properties during harvesting to optimize the design of threshing cylinders, minimize the breakage rate during threshing, and enhance the quality of the final product. However, a precise model for the mechanical response of soybean seeds under stress conditions is currently lacking. To establish an accurate finite-element model (FEM) for soybeans that can predict their mechanical behavior under various loading conditions, an ellipsoidal modeling approach tailored for soybeans is proposed. Soybeans harvested in Xinjiang were collected and processed as experimental materials; the average moisture content was 11.77%, there was an average density of 1.229 g/cm³, and the average geometric specifications (height, thickness, and width) were 8.50 mm, 7.92 mm, and 7.10 mm, respectively. Compression tests were conducted on the soybeans in vertical, horizontal, and lateral orientations at the same loading speed to analyze the load and damage stages of these soybeans harvested in Xinjiang. The experimental results indicate that as the contact area decreases, the crushing load increases, with soybeans in the horizontal orientation being able to withstand the highest ultimate pressure. When placed vertically, the soybeans are not crushed; in horizontal and lateral orientations, however, they exhibit varying degrees of breakage. The Hertz formula was simplified based on the geometric characteristics of soybeans, and the elastic moduli in the X, Y, and Z directions of the soybean seeds were calculated as 42.8821 MPa, 40.4342 MPa, and 48.7659 MPa, respectively, using this simplified Hertz formula. A model of the soybeans was created in SolidWorks Ver.2019 and imported into ANSYS WORKBENCH for simulation verification. The simulation results were consistent with the experimental findings. The research findings enhance the understanding of the mechanical behavior of soybean seeds and provide robust scientific support for the optimization of soybean processing technologies and the improvement of storage and transportation efficiency.

To address the problems of low crushing efficiency and uneven distribution in traditional straw crushing and returning machines for cotton stalk return operations in Xinjiang, a vertical straw crushing and returning machine with large and small dual-blade discs was designed, adapted to Xinjiang’s cotton planting model. The machine employs a differentiated configuration of large and small blade discs corresponding to four and two rows of cotton stalks, respectively, effectively reducing tool workload while significantly improving operational efficiency. A simulation model of the crushing and returning machine was developed using the discrete element method (DEM), and a flexible cotton stalk model was established to systematically investigate the effects of machine forward speed, crushing blade rotational speed, and knife tip-to-ground clearance on operational performance. Single-factor simulation experiments were conducted using crushing qualification rate and broken stalk drop rate as evaluation indicators. Subsequently, a multi-factor orthogonal field experiment was designed with Design-Expert software (13.0.1.0, Stat-Ease Inc, Minneapolis, MN, USA). The optimal working parameters were determined to be machine forward speed of 3.5 m/s, crushing blade shaft speed of 1500 r/min, and blade tip ground clearance of 60 mm. Verification tests demonstrated that under these optimal parameters, the straw crushing qualification rate reached 95.9% with a broken stalk drop rate of 15.5%. The relative errors were less than 5% compared to theoretical optimization values, confirming the reliability of parameter optimization. This study provides valuable references for the design optimization and engineering application of straw return machinery.

As a vital oil and cereal crop in China, soybean requires efficient and low-loss harvesting to ensure food security and sustainable agricultural development. However, pod-shattering losses during soybean harvesting in Xinjiang remain severe due to low pod moisture content and poor mechanical strength, while existing studies lack a systematic analysis of the interaction mechanism between reeling devices and pods. The current research on soybean harvester headers predominantly focuses on conventional rigid designs, with limited exploration of flexible reel mechanisms and their biomechanical interactions with soybean pods. To address this, this study proposes an optimization method for low-loss harvesting technology based on mechanical-crop interaction mechanisms, integrating dynamic simulation, contact mechanics theory, and field experiments. Texture analyzer tests revealed pod-shattering force characteristics under different compression directions, showing that vertical compression exhibited the highest shattering risk with an average force of 14.3271 N. A collision model between the spring tooth and pods was established based on Hertz contact theory, demonstrating that reducing the elastic modulus of the spring tooth and increasing the contact area significantly minimized mechanical damage. Simulation verified that the PVC-nylon spring tooth reduced the maximum equivalent stress on pods by 90.3%. Furthermore, the trajectory analysis of spring-tooth tips indicated that effective pod-reeling requires a reel speed ratio (Δ) exceeding 1.0. Field tests with a square flexible spring tooth showed that the optimized reel reduced header loss to 1.371%, a significant improvement over conventional rigid teeth. This study provides theoretical and technical foundations for developing low-loss soybean harvesting equipment. Future work should explore multi-parameter collaborative optimization to enhance adaptability in complex field conditions.

With the expansion of modern protected agriculture, the amount of post-harvest tomato biomass has increased sharply. Conventional unmanaged disposal practices disrupt carbon flows and cause substantial environmental emissions. Tomato plant residues (TPRs), which are rich in lignocellulose and selected high-value secondary metabolites, have considerable potential as feedstocks for green industrial materials. However, their complex biophysical properties, high physiological moisture content, and recalcitrant cell-wall barriers hinder large-scale processing. This review systematically examines the mechanisms and process architectures for converting TPRs into macromolecular products. First, it analyzes cross-scale anatomical heterogeneity and dynamic rheological properties of TPRs, defining their physicochemical boundaries as industrial precursors. Second, it summarizes the development of physical field-coupled equipment, ranging from anti-tangling harvest-shredding to die-roller densification. Furthermore, it examines the core mechanisms of multi-field-coupled pretreatment technologies, including steam explosion, deep eutectic solvents (DES), and mechanochemistry, in deconstructing vascular skeletons and reducing multiphase mass-transfer resistance. Finally, this review discusses reconstruction pathways for TPR-derived components in advanced polymer materials, including biodegradable nanocellulose films, bio-based composites, aerogels, and lignin-based polyurethane networks. Overall, it links microscopic reaction kinetics with macroscopic equipment engineering, proposes a closed-loop material conversion system from in-field volume reduction to cascaded biorefinery, and provides an engineering framework for future multi-machine intelligent collaboration and continuous production across the industrial chain.

Mechanized harvesting in the industrial tomato sector is currently bottlenecked by excessive mechanical injuries and elevated levels of foreign materials generated during electro-mechanical combine harvesting operations. To combat these limitations, this comprehensive review explores recent breakthroughs in harvester-mounted smart grading systems engineered specifically for complex, open-field conditions. Rather than relying solely on conventional optical inspection, the study examines the transition toward advanced, heterogeneous edge-computing frameworks—incorporating FPGAs and embedded GPUs—deployed within electro-mechanical harvesting platforms. This architectural evolution plays a crucial role in mitigating unpredictable processing delays caused by intense operational vibrations, although achieving absolute real-time stability under extreme field conditions remains an ongoing challenge. To minimize bruising and physical deterioration, our analysis synthesizes findings from multi-scale explicit dynamic finite element simulations, unpacking the underlying microstructural failure modes of the crop. We illustrate how regulating applied forces via soft robotic effectors can help approach a ‘damage-free’ handling threshold, though empirical results vary depending on fruit maturity and dynamic operational speeds. Furthermore, coupling multi-modal sensor fusion with Convolutional Neural Networks (CNNs) shows promising potential for non-destructive internal property evaluation under the vibration, dust, and throughput constraints of electro-mechanical harvesters, pending broader validation across diverse field datasets. Ultimately, by projecting future trends in onboard electro-mechanical harvester separation and advocating for a closer synergy between agronomic practices and machine engineering, this paper delivers a comprehensive blueprint for building next-generation, highly resilient, and gentle sorting machinery.

With the rapid expansion of modern facility agriculture, harvesting robots for tomato (Solanum lycopersicum L.) and other solanaceous fruits have become key equipment for alleviating labor shortages. However, in unstructured greenhouse and open-field scenarios, branch and leaf occlusion, overlapping fruit clusters, variable illumination, and fragile fruit tissues constrain perception reliability and mechanical contact safety. Embodied intelligence, a closed harvesting loop integrating perception, spatial reasoning, motion control, and physical-contact feedback, offers a promising paradigm to address these challenges. Nevertheless, the transition from prototypes to reliable commercial deployment remains constrained by environmental variability, biological heterogeneity, and system-level integration. This review systematically examines the mechanisms and process architectures for converting multimodal perception into low-damage harvesting actions of solanaceous fruit-picking robots. First, the review analyzes deep-learning-based real-time recognition methods, covering target detection, instance segmentation, and occlusion-adaptive strategies. Second, attention turns to spatial 3D localization and pedicel feature acquisition, including depth sensing and high-precision hand–eye calibration. Furthermore, operation patterns and flexible low-damage end-effectors are examined, clarifying fruit drop collision damage and physical contact models of suction and shearing devices. Finally, technical bottlenecks and future directions are discussed, encompassing multi-modal perception–action coupling, agricultural foundation models, end-to-end closed-loop control, and commercialization requirements. Overall, it links algorithmic perception accuracy with system-level harvesting reliability, proposes a crop-specific, damage-aware evaluation framework, and provides a roadmap for robust, low-damage commercial deployment.