Development and Experimental Analysis of a Fuzzy Grey Control System on Rapeseed Cleaning Loss

Design and Testing of the Peanut Pod Cleaning Device

Due to the design of peanut harvesters and cleaners, peanut pods are often mixed with soil, gravel, peanut straw, and other impurities. To solve this problem, this study focused on designing a peanut pod cleaning device by integrating a negative pressure centrifugal fan, a hydrometric cleaning sieve, and a reversible long-mesh cleaning sieve. The relative motion of the peanut pod on the sieve was discussed, its stress analyzed, the design parameters of the sieve and fan determined, and the operation of the device was monitored by using sensors, which accurately recorded and adjusted the working parameters. Finally, the key parameters were tested, and the results showed that the design requirements were met at a vibration frequency of 5.5 Hz, a hydrometric cleaning sieve inclination of 15°, a reciprocating long mesh cleaning sieve inclination of 5°, and a fan speed of 1500 rev/min; the mean loss is 2.26%, and the mean impurity is 3.18%. The findings can be used to provide technical support and reference for the development of peanut pod cleaning devices.

Research and application of a new method for adjusting the vehicle body height

The use of active suspension for vehicle height adjustment has problems of high cost, high energy consumption, slow response, and complex structure. This paper proposes a new method for adjusting the vehicle body using the damping asymmetric characteristic of semi-active suspensions, which is based on the idea that the dampers with damping asymmetric characteristics will cause a change in the mean position of the vehicle body vibration. To verify the feasibility of this method, a single-wheel vehicle model containing asymmetric damping is established. The system's responses under the sinusoidal and random roads excitation are obtained by the fourth-order Runge-Kutta method, the influences of key parameters on the vehicle body's shifting height are analyzed, and the mechanism of vehicle body's shift is explained from the perspective of energy conservation. Then a vehicle body height controller based on third-order linear active disturbance rejection control (LADRC) is designed. Simulation results show that the proposed method for controlling the vehicle height with asymmetric damping can quickly adjust the vehicle to the expected height whether under the sinusoidal road or random road. In addition, no additional hardware and energy consumption are required, providing a new idea for vehicle height control.

Double-Threshold Segmentation of Panicle and Clustering Adaptive Density Estimation for Mature Rice Plants Based on 3D Point Cloud

Crop density estimation ahead of the combine harvester provides a valuable reference for operators to keep the feeding amount stable in agriculture production, and, as a consequence, guaranteeing the working stability and improving the operation efficiency. For the current method depending on LiDAR, it is difficult to extract individual plants for mature rice plants with luxuriant branches and leaves, as well as bent and intersected panicles. Therefore, this paper proposes a clustering adaptive density estimation method based on the constructed LiDAR measurement system and double-threshold segmentation. The Otsu algorithm is adopted to construct a double-threshold according to elevation and inflection intensity in different parts of the rice plant, after reducing noise through the statistical outlier removal (SOR) algorithm. For adaptively parameter adjustment of supervoxel clustering and mean-shift clustering during density estimation, the calculation relationship between influencing factors (including seed-point size and kernel-bandwidth size) and number of points are, respectively, deduced by analysis. The experiment result of density estimation proved the two clustering methods effective, with a Root Mean Square Error (RMSE) of 9.968 and 5.877, and a Mean Absolute Percent Error (MAPE) of 5.67% and 3.37%, and the average accuracy was more than 90% and 95%, respectively. This estimation method is of positive significance for crop density measurement and could lay the foundation for intelligent harvest.