During the harvesting of cut-stem vegetables, the structural parameters of cutters have an important influence on the harvesting effect. Structural parameters of stalks directly affect the cutting effect of the cutter. The thickness of the stalk xylem has a strong influence on the cutting stability and cutting surface effect of the cutter. In this paper, the effect of the broccoli stalk xylem on conventional toothless cutters was investigated using broccoli stalks as the cutting object. It was found that a thicker xylem leads to shear force fluctuations, which in turn affects the smooth operation of the cutting device. Taking locust mouthparts as the research object, a bionic multi-tooth cutting method is proposed in this paper to obtain the contour curve based on the locust upper jaw cutting tooth lobe. By establishing the regression equation, the contour curve of the cutting teeth is fitted accurately. The cutter edge is designed with the locust’s maxillary incisive lobe as the bionic object. ANSYS software was used to simulate the cutting of a double disc cutter and broccoli stalk. The effect of each factor was analyzed by response-surface regression to determine the optimal cutter speed, machine forward speed, cutting inclination, blade overlap, and optimal cutting position. The cutting test is verified via broccoli stalks and a cutting test bench to further determine the cutting device operating parameters. The optimal operating parameters of the cutting device were 0.239 m/s forward speed, 30.974-degree cutting edge angle, 10.066 mm blade overlap, and 467.511 rpm.

The threshing drum, a core component in combine harvesters, experiences significant unbalanced vibrations during high-speed rotation, leading to severe mechanical wear, increased energy consumption, elevated noise levels, potential safety hazards, and higher maintenance costs. A primary challenge is that excessive interference signals often obscure the fundamental frequency characteristics of the vibration, hampering balancing effectiveness. This study introduces a signal conditioning model to suppress such interference and accurately extract the unbalanced quantities from the raw signal. Leveraging this extracted vibration force signal, an automatic optimization method for the balancing counterweights was developed, solving calculation issues inherent in traditional approaches. This formed the basis for an automatic balancing control strategy and an integrated system designed for online monitoring and real-time control. The system continuously adjusts the rotation angles, θ₁ and θ₂, of the balancing weight disks based on live signal characteristics, effectively reducing the drum’s imbalance under both internal and external excitation states. This enables a closed loop of online vibration testing, signal processing, and real-time balance control. Experimental trials demonstrated a significant 63.9% reduction in vibration amplitude, from 55.41 m/s² to 20.00 m/s². This research provides a vital theoretical reference for addressing structural instability in agricultural equipment.

This study investigates the tribological behavior and wear mechanisms of Q235 steel components subjected to abrasive interaction with rice, a critical challenge in agricultural machinery performance and longevity. We employed a comprehensive multi-scale framework, integrating bench-top tribological testing, advanced Discrete Element Method (DEM) coupled with a wear model (DEM-Wear), and detailed surface characterization. Bench tests revealed a composite wear mechanism for the rice–steel tribo-pair, transitioning from mechanical polishing under mild conditions to significant soft abrasive micro-cutting driven by the silica particles inherent in rice during high-load, high-velocity interactions. This elucidated fundamental friction and wear phenomena at the micro-level. A novel, calibrated DEM-Wear model was developed and validated, accurately predicting macroscopic wear “hot spots” on full-scale combine harvester header platforms with excellent geometric similarity to real-world wear profiles. This provides a robust predictive tool for component lifespan and performance optimization. Furthermore, fractal analysis was successfully applied to quantitatively characterize worn surfaces, establishing fractal dimension (Ds) as a sensitive metric for wear severity, increasing from ~2.17 on unworn surfaces to ~2.3156 in severely worn regions, directly correlating with the dominant wear mechanisms. This study offers a valuable computational approach for understanding and mitigating wear in tribosystems involving complex particulate matter, contributing to improved machinery reliability and reduced operational costs.

As a multi-source vibration machine, the complex vibration of the combine harvester has caused many effects on various components. In this paper, the dynamic response of the plate and shell on the combine harvester is studied, and the influence of various factors on the vibration of the plate and shell is determined. The structure of the plate and shell is optimized, and the scheme of structural optimization that replaces the finite element model with the surrogate model is developed. The structure is designed with Latin hypercube design (LHD), the model of response surface is generated based on the Kriging model, and then the parameters are optimized by a multi-objective genetic algorithm (MOGA). It is realized that the first-order natural frequency of the plate is increased by 14.6% under the same mass, avoiding the resonance range of the main excitation of the machine. At the same time, its dynamic response under the multi-point force is also greatly decreased by 62.4%.

Soybean–corn intercropping in the hilly–mountainous regions of Southwest China poses unique challenges to mechanized harvesting because of complex topography and agronomic constraints. Addressing the soybean-harvesting bottleneck in these fields requires advanced sensing and perception rather than purely mechanical redesigns. Prior reviews emphasized flat-terrain machinery or single-crop systems, leaving a gap in sensor-centric solutions for intercropping on steep, irregular plots. This review analyzes how sensors enable the next generation of intelligent harvesters by linking field constraints to perception and control. We frame the core failures of conventional machines—instability, inconsistent cutting, and low efficiency—as perception problems driven by low pod height, severe slope effects, and header–row mismatches. From this perspective, we highlight five fronts: (1) terrain-profiling sensors integrated with adaptive headers; (2) IMUs and inclination sensors for chassis stability and traction on slopes; (3) multi-sensor fusion of LiDAR and machine vision with AI for crop identification, navigation, and obstacle avoidance; (4) vision and spectral sensing for selective harvesting and impurity pre-sorting; and (5) acoustic/vibration sensing for low-damage, high-efficiency threshing and cleaning. We conclude that compact, intelligent machinery powered by sensing, data fusion, and real-time control is essential, while acknowledging technological and socio-economic barriers to deployment. This review outlines a sensor-driven roadmap for sustainable, efficient soybean harvesting in challenging terrains.

The structural reliability of agricultural machinery is critically dependent on bolted joints, with loosening being a significant and prevalent failure mode. Harsh operational environments (intense vibration, impact, corrosion) severely exacerbate loosening risks, compromising machinery performance and safety. Traditional periodic inspections are inadequate for preventing sudden failures induced by loosening, leading to impaired efficiency and safety hazards. This review comprehensively analyzes the unique challenges and opportunities in monitoring bolted joint reliability within agricultural machinery. It covers the following: (1) the status of bolted joint reliability issues (failure modes, impacts, maintenance inadequacies); (2) environmental challenges to joint integrity; (3) evaluation of conventional detection methods; (4) principles and classifications of modern detection technologies (e.g., vibration-based, acoustic, direct measurement, vision-based); and (5) their application status, limitations, and techno-economic hurdles in agriculture. This review identifies significant deficiencies in current technologies for agricultural machinery bolt loosening surveillance, underscoring the pressing need for specialized, dependable, and cost-effective online monitoring systems tailored for agriculture’s demanding conditions. Finally, forward-looking research directions are outlined to enhance the reliability and intelligence of structural monitoring for agricultural machinery.