The noise generated by combine harvesters during operation has drawn growing attention, particularly that of the conveying trough shell, whose noise generation mechanism remains unclear. This study investigated the vibration radiation noise characteristics of conveying troughs by analyzing a chain system with 83 links using numerical simulation and experimental validation. A dynamic model of the conveyor chain system was developed, and the time domain reaction force at the bearing support was used as excitation for the trough shell’s finite element model. Modal and harmonic response analyses were performed to obtain the vibration response, which served as an acoustic boundary input for the LMS Virtual Lab. The indirect boundary element method was used to compute the radiated noise, achieving coupled modeling of chain system vibration and trough shell noise. Simulation results revealed that the maximum radiated noise occurred at approximately 112 Hz, closely matching experimental data. Comparative analysis of transmitted noise at 500 Hz and 700 Hz showed acoustic power levels of 98.4 dB and 109.52 dB, respectively. Results indicate that transmitted noise dominates over structural radiation in energy contribution, highlighting it as the primary noise path. This work offers a validated prediction model and supports noise control design for combine harvester conveying troughs.

This study investigates noise detection and damping-based noise mitigation strategies for cavity structures, with a specific focus on addressing noise issues in the conveyor trough of combine harvesters. Despite its practical significance, research on the noise generation mechanisms, transmission paths, and control measures for conveyor troughs remains limited, particularly under varying operational conditions. To bridge this gap, this work integrates experimental measurements with numerical simulations to systematically analyze and optimize the noise reduction performance of the conveyor trough. Noise measurements were conducted using the sound intensity method, revealing sound pressure levels in the range of 93–95 dB. Frequency spectrum analysis identified key noise sources and dominant frequency components. Finite element analysis (FEA) and vibration modal testing were performed to uncover critical noise-inducing factors, including chain meshing impacts and structural resonances. Based on these findings, a damping optimization strategy was proposed by incorporating constrained damping layers to attenuate vibration and reduce noise in targeted frequency bands. The effectiveness of this approach was validated through multiple coherence analysis, which confirmed significant suppression of structural vibration noise in the 0–500 Hz range, while experimental results showed that the optimized conveyor trough structure achieved a maximum reduction of 0.4071 dB in continuous equivalent A-weighted sound pressure under load conditions. This research provides a comprehensive methodology for noise control and structural optimization of conveyor trough systems, offering valuable theoretical and practical insights for enhancing the operational comfort and environmental performance of combine harvesters.

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.

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.