The discrete element method and simulation analysis of the interaction between granular materials and implements provide a convenient and effective method for the optimal design of farming machinery. However, the parameter differences between different materials make discrete element simulation impossible to carry out directly. It is necessary to obtain the specific material parameters and contact parameters through parameter calibration of the simulation object, so as to make the simulation results more reliable. Parameter calibration mainly includes intrinsic parameter measurement, contact model selection, contact parameter selection, and parameter calibration. The test methods of the calibration test include the Plackett–Burman test and other methods of screening parameters with significant influence, and then selecting the optimal parameters through the climbing test, response surface analysis method, etc., and finally carrying out the regression analysis. This paper will describe the existing parameter measurement methods and parameter calibration methods and provide a reference for the scholars who study parameter calibration to carry out parameter calibration.

Leaf mildew is a common disease of tomato leaves. Its detection is an important means to reduce yield loss from the disease and improve tomato quality. In this study, a new method was developed for the multi-source detection of tomato leaf mildew by THz hyperspectral imaging through combining internal and external leaf features. First, multi-source information obtained from tomato leaves of different disease grades was extracted by near-infrared hyperspectral imaging and THz time-domain spectroscopy, while the influence of low-frequency noise was removed by the Savitzky Golay (SG) smoothing algorithm. A genetic algorithm (GA) was used to optimize the selection of the characteristic near-infrared hyperspectral band. Principal component analysis (PCA) was employed to optimize the THz characteristic absorption spectra and power spectrum dimensions. Recognition models were developed for different grades of tomato leaf mildew infestation by incorporating near-infrared hyperspectral imaging, THz absorbance, and power spectra using the backpropagation neural network (BPNN), and the models had recognition rates of 95%, 96.67%, and 95%, respectively. Based on the near-infrared hyperspectral features, THz time-domain spectrum features, and classification model, the probability density of the posterior distribution of tomato leaf health parameter variables was recalculated by a Bayesian network model. Finally, a fusion diagnosis and health evaluation model of tomato leaf mildew with hyperspectral fusion THz was established, and the recognition rate of tomato leaf mildew samples reached 97.12%, which improved the recognition accuracy by 0.45% when compared with the single detection method, thereby achieving the accurate detection of facility diseases.

Autonomous navigation technology plays a crucial role in advancing smart agriculture by enhancing operational efficiency, optimizing resource utilization, and reducing labor dependency. With the rapid integration of information technology, modern agricultural machinery increasingly incorporates advanced techniques such as high-precision positioning, environmental perception, path planning, and path-tracking control. This paper presents a comprehensive review of recent advancements in these core technologies, systematically analyzing their methodologies, advantages, and application scenarios. Despite notable progress, considerable challenges persist, primarily due to the unstructured nature of farmland, varying terrain conditions, and the demand for robust and adaptive control strategies. This review also discusses current limitations and outlines prospective research directions, aiming to provide valuable insights for the future development and practical deployment of autonomous navigation systems in agricultural machinery. Future research is expected to focus on enhancing multi-modal perception under occlusion and variable lighting conditions, developing terrain-aware path planning algorithms that adapt to irregular field boundaries and elevation changes and designing robust control strategies that integrate model-based and learning-based approaches to manage disturbances and non-linearity. Furthermore, tighter integration among perception, planning, and control modules will be crucial for improving system-level intelligence and coordination in real-world agricultural environments.

Crop row detection technology, as one of the key technologies for agricultural robots to achieve autonomous navigation and precise operations, is related to the precision and stability of agricultural machinery operations. Its research and development will also significantly determine the development process of intelligent agriculture. The paper first summarizes the mainstream technical methods, performance evaluation systems, and adaptability analysis of typical agricultural scenes for crop row detection. The paper also summarizes and explains the technical principles and characteristics of traditional methods based on visual sensors, point cloud preprocessing based on LiDAR, line structure extraction and 3D feature calculation methods, and multi-sensor fusion methods. Secondly, a review was conducted on performance evaluation criteria such as accuracy, efficiency, robustness, and practicality, analyzing and comparing the applicability of different methods in typical scenarios such as open fields, facility agriculture, orchards, and special terrains. Based on the multidimensional analysis above, it is concluded that a single technology has specific environmental adaptability limitations. Multi-sensor fusion can help improve robustness in complex scenarios, and the fusion advantage will gradually increase with the increase in the number of sensors. Suggestions on the development of agricultural robot navigation technology are made based on the current status of technological applications in the past five years and the needs for future development. This review systematically summarizes crop row detection technology, providing a clear technical framework and scenario adaptation reference for research in this field, and striving to promote the development of precision and efficiency in agricultural production.

The technology of plastic film mulching is widely applied in Xinjiang, but it also brings about serious issues of residual film pollution. Currently, the 1MSF-2.0 residual film recovery machine can effectively address the problem. However, it faces challenges such as high overall machine weight and noticeable frame vibrations, which affect the stability of the entire machine operation. The frame, as the installation foundation, needs to bear loads and impact. Therefore, the reliability of the frame is crucial for the stability of the entire machine. Improving the frame’s vibration is of great importance. In response to the significant vibration issues during the operation of the 1MSF-2.0 residual film recovery machine, this paper utilized Workbench 2020 R2 to establish a finite element model of the machine frame and conducted static analysis to obtain strength information, thereby initially understanding the optimization space of the frame. Building upon this, Mechanical was employed to solve the first 14 natural frequencies and mode shapes of the frame, and the accuracy of the theoretical analysis was verified through modal testing. After analyzing the frequency characteristics of external excitation forces, it was found that the fourth-order natural frequency of the frame fell within the frequency range of the excitation force of the shaft of the straw grinder, causing resonance in the frame and necessitating structural optimization. The optimal results indicated that the optimized frame increased in mass by 4.41%, reduced the maximum stress value by 2.56 MPa, and increased the fourth-order natural frequency to 22.7 Hz, avoiding the frequency range of the excitation force of the shaft of the straw grinder, thus improving the resonance issue. This paper provides a reference for optimizing the design of the frame of the residual film recovery machine.

Addressing the issues of low harvesting efficiency and high losses in current pod pepper harvesters, this study presents the design of a bionic comb finger pod pepper picking device and an inclined double-spiral bell pepper harvester to improve the harvest rate and reduce loss rate. Through the utilization of discrete element simulation software EDEM, a discrete element model for pod peppers is established. Additionally, a simulation platform for ground drop loss during pod pepper picking is developed, enabling exploration of the movement trajectory and velocity changes of the pod pepper elements. The study also conducts an analysis on the impact of the speed of the picking rollers X₁, the feeding speed of pod peppers X₂, and the spacing between the two picking rollers X₃ on ground drop losses. Based on the results of the single-factor test, the Box–Behnken response surface test was used to optimize the working parameters of the picking device, which resulted in the optimal combination of the working parameters of the picking device: the speed of the picking rollers was 680.41 rpm, the feeding speed of the pod peppers was 0.5 m/s, and the spacing between the two picking rollers was 12 mm, which resulted in the loss rate of pod peppers on the floor of the ground being 3.526%.