Accurate yield estimation before the wheat harvest is very important for precision management, maintaining grain market stability, and ensuring national food security. In this study, to further improve the accuracy of winter wheat yield estimation, machine learning models, including GPR, SVR, and DT, were employed to construct yield estimation models based on the single and multiple growth periods, incorporating the color and multispectral vegetation indexes. The results showed the following: (1) Overall, the performance and accuracy of the yield estimation models based on machine learning were ranked as follows: GPR, SVR, DT. (2) The combination of color indexes and multispectral vegetation indexes effectively improved the yield estimation accuracy of winter wheat compared with the multispectral vegetation indexes and color indexes alone. The accuracy of the yield estimation models based on the multiple growth periods was also higher than that of the single growth period models. The model with multiple growth periods and multiple characteristics had the highest accuracy, with an R² of 0.83, an RMSE of 297.70 kg/hm², and an rRMSE of 4.69%. (3) For the single growth period, the accuracy of the yield estimation models based on the color indexes was lower than that of the yield estimation models based on the multispectral vegetation indexes. For the multiple growth periods, the accuracy of the models constructed by the two types of indexes was very close, with R² of 0.80 and 0.80, RMSE of 330.37 kg/hm² and 328.95 kg/hm², and rRMSE of 5.21% and 5.19%, respectively. This indicates that the low-cost RGB camera has good potential for crop yield estimation. Multi-temporal and multi-sensor remote sensing data fusion can further improve the accuracy of winter wheat yield estimation and provide methods and references for winter wheat yield estimation.

This study delves into the mapping method for the navigation system of a chicken coop disinfection robot. It systematically analyzes the problems of insufficient effective particle count, high particle repetition rate in environmental map information, and penetration phenomenon in traditional SLAM laser point cloud mapping technology in chicken coop environments containing multiple layers of chicken cages. To address these issues, this paper proposes an optimized mapping method based on an improved ICP algorithm, significantly improving the laser point clouds’ registration performance. At the same time, by limiting the sampling of environmental map information particles within a specific range and optimizing the screening based on the predicted distribution of particle poses and the matching degree of the map, the diversity of particles and the accuracy of map information have been effectively improved. The field experiment results show that the maximum error of this method on the chicken coop environment map does not exceed 3.5 cm. The environmental characteristics of the chicken coop are maximally preserved, which verifies the effectiveness and robustness of this method and provides a scientific basis for the mapping method of the livestock and poultry breeding robot navigation system.

In order to analyze the flow field inside the crushing chamber of machine for lentinus edodes stipe crushing and improve performance of the machine based on the results of the flow field analysis, this paper applied the computational fluid dynamics software FLUENT to numerically simulate the flow field inside the crushing chamber of the machine for lentinus edodes stipe crushing. Thus, the flow field characteristics and flow state inside the crushing chamber were intuitively displayed, and the visualization of the flow field in the crushing chamber was realized. Then, the wind speed value obtained by simulation was compared with the value measured by experiment, and the average relative error between them was less than 10%. Finally, according to the simulation results of flow field, the flow field generated by one air inlet and two air inlets was analyzed, and then the blade structure, outlet area parameters, and rotation speed parameters were compared, simulated and optimized. Results analysis: the pressure distribution and velocity distribution of the flow field inside the crushing chamber were obtained, and the optimal structure and parameters were two inlets, the rotation speed of 2000 r/min and the outlet area of 160×80 mm2, the crushing chamber with the new blade parameters and structure had greater turbulent kinetic energy and velocity gradient, which was more favorable to the discharge, and provided a basis for further optimization and improvement of the crushing machine.