A GB-MPC control algorithm (GWO-BP-MPC) was proposed to solve the problem of precise temperature control of fruit and vegetable coupling drying devices. Firstly, the BP (Back Propagation) neural network was improved using the Grey Wolf Optimizer (GWO) algorithm to increase the relevance and accuracy of the prediction model. By means of an improved neural network, we developed a high-accuracy predictive model for temperature control of drying units. Secondly, the projection conjugate gradient method was proposed for nonlinear optimization of the control system to improve the solving speed and accuracy of the optimal solution. The GB-MPC control algorithm was compared with the PID controller. The experimental results shown that the convergence speed of GB-MPC control was faster, the time took to reach a steady state in a single stage was shortened by 47 seconds compared with PID control. In the control process, the temperature change range of the GB-MPC control algorithm was smaller and there was no overshoot problem, which gave a better control effect than PID.

In order to improve the efficiency of peanut harvesting in China, realize the deep integration of agricultural machinery and agronomy in peanut production and promote the development of peanut industry. According to the research on peanut planting agronomy and plant biological characteristics, this paper focuses on the analysis of the clamping and collecting scheme of the three-ridge six-row peanut combine harvester, and designs the structure of the clamping and collecting device to solve the situation that the peanut clamping and collecting process is prone to congestion under large feeding volume. By analysing the forces on the peanut plant, the structure of the core fruit picking device has been designed, and the factors and parameters affecting the effectiveness of the peanut picking operation have been optimised through experimental research using the peanut leakage and crushing rates as indicators. The whole machine structure of the three-ridge six-row peanut combine harvester was designed to ensure that the peanut combine harvester can complete the processes of harvesting, digging, clamping and conveying, soil removal, collecting, fruit picking, cleaning and fruit collection at one time. Finally, through field tests on the three-ridge six-row peanut combine harvester, it was verified that the peanut leakage rate was 1.92% and the crushing rate was 0.84% at different forward speeds, and all of them were congested, which met the national peanut harvesting standards.

There are approximately 36.7 million hectares of saline alkali land available in China. To enhance the comprehensive utilization value of coastal saline alkali land and boost crop yields in such areas, it is essential to conduct research on optimizing the operational performance of high-performance soil contact components in light of the soil characteristics of coastal saline alkali land. Discrete element simulation can be employed to investigate the operational mechanisms of various key components. Nevertheless, at present, there is a dearth of discrete element models for the key physical parameters and soil structure of coastal saline alkali land soil. In this article, typical coastal saline alkali field soil was sampled, and the physical properties of the saline alkali soil, including salt content, moisture content, particle size distribution, and particle size, as well as intrinsic parameters such as soil compaction, density, Poisson’s ratio, and shear modulus, were measured. The Hertz Mindlin with Bonding contact model was employed. Physical experiments on soil accumulation angles at different depths were carried out using the cylindrical lifting method. Subsequently, by means of the discrete element method and the BBD experimental design method, a response surface model was established, and an optimization analysis was performed on the optimal parameters for the soil–soil collision recovery coefficient, static friction coefficient, and dynamic friction coefficient at each depth. Test benches for measuring the collision recovery coefficient, static friction coefficient, and rolling friction coefficient of saline alkali soil at -65Mn were set up, calculation formulas for each parameter were derived, and the contact parameters between soil at different depths and 65Mn were obtained. The results of the sliding friction angle test on different depths of saline alkali soil at -65Mn were further verified using the discrete element method, with a maximum error of 3.11%, which falls within the allowable range. This suggests that the calibration results of the discrete element simulation parameters for the interaction between soil and contact components are reliable, providing data and model support for future research on enhancing the operational performance of high-performance contact components.