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.

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.

To promote the germination of rice panicles during the regeneration season, it is necessary to ensure a stubble height of 300–450 mm when mechanically harvesting the first-season rice. However, due to variations in the depth of the paddy soil and fluctuations in the height of the header during harvesting, maintaining the desired stubble height becomes challenging, resulting in a significant impact on the yield during the regeneration season. This study presents the design of an adaptive profiling header capable of adjusting the height and level of the header adaptively. Based on the theoretical analysis of the profiling mechanism, a quadratic regression orthogonal rotation combination experiment is designed. Considering the actual field conditions, the range of each factor is determined, and simulation experiments are conducted based on the MBD-DEM coupling to establish a mathematical regression model between each factor and indicator. In the case of the profiling wheel linkage length of 562 mm, profiling wheel width of 20 mm, and profiling wheel mass of 3.6 kg, the supporting force of the header on the profiling wheel would be greater than zero, the supporting force of soil on the profiling wheel and the depth of soil subsidence represent the smallest values, and the highest sensitivity and accuracy of the profiling wheel are achieved. Bench tests demonstrated that the header exerts a force on the profiling wheel, confirming the normal functioning of the profiling. The average magnitudes of forces exerted by the soil on the profiling wheel are obtained to be 31.98 N, 31.63 N, and 30.86 N, whereas the corresponding average soil subsidence depths are obtained as 3.4 mm, 5.6 mm, and 8.3 mm, aligning closely with the simulation values. The results indicate that the profiling mechanism achieves high accuracy in ground profiling and that the structural design is reasonable. By employing fuzzy PID control to adjust the height of the header, the average error in adjustment is obtained as 6.75 mm, while the average error in the horizontal adjustment is derived as 0.64°. The header adjustment is fast, offering high positioning accuracy, thereby meeting the harvesting requirements of the first season of ratooning rice.

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%.

In order to reduce ground drop loss during mechanical pepper picking and improve the net recovery rate, a drum snap finger picking device was designed. The picking device is mainly composed of a picking drum and auxiliary picking components; the picking finger arrangement was designed biomimetically and its structure and operating parameters were optimized by the DEM (discrete element method). According to the physical and mechanical characteristics of the pepper and the simplified three-dimensional model of the picking device, a virtual simulation model of the pepper-picking device was established using the EDEM software. Through simulation analysis and using the orthogonal test method, the main factors which affect the ground drop loss rate of pepper and their optimal parameter combination values were determined. The simulation results were verified by a pepper-picking field experiment. Orthogonal tests show that, when the picking drum speed ($V^{\prime }$) is 210 rpm, the pepper-feeding speed ($V^{″}$) is 1100 $\mathrm{m}\mathrm{m}·{\mathrm{s}}^{-1}$, the bending angle of each picking spring tooth (C) is 162°, and each group of circumferential fingers has rows, the picking device has a good picking effect. At this time, the ground drop loss rates in both the simulation and field test were 7.50% and 7.85%, respectively, and the drop error was only 4.46%, which was within the allowable range. The design form and parameter optimization simulation method in this paper provide an important reference for the design and optimization of pepper-harvesting machinery.