压力式谷物产量监测系统优化与试验验证.

The information of grain yield distribution is one of the main information in digital agriculture, and its effective acquisition is of great significance for the process of grain harvesting. At present, domestic and foreign researchers had designed different types of grain yield monitoring systems with a variety of means. But due to the impact of measurement accuracy, model matching and other factors, these systems had not been effectively applied to the actual production in China. This study designed an online monitoring system based on the principle of grain flow pressure for monitoring grain yield. In this paper, the mathematical model of grain yield based on grain flow pressure was established, and the whole structure of the pressure-type grain yield monitoring system was determined. The monitoring system was mainly composed of grain flow monitoring device, positioning device, cutting table height control switch, core processor and human-computer interaction device, etc. Because the system proposed had the functions of sensor signal acquisition and processing, data display and storage, it realized real-time measurement, display and storage of grain yield in the process of grain harvesting. Based on the monitoring mathematical model between grain yield and grain flow pressure, a testbed was set up to simulate the actual operation of the end conveying auger of the grain collecting lifter of the grain combine harvests. The testbed was mainly composed of grain flow monitoring device, auger, feeding box, insert plate, three phase alternating current motor, reducer, stage and other parts. The Box-Behnken experimental design method was used to optimize the structural parameters of the grain flow monitoring device with the testbed. In this paper, the influences of the number of the sensors, the installation position of the sensors and the horizontal inclination angle of the monitoring device on the error of the grain yield monitoring system were studied. The optimal parameter combination was determined as follows: the number of the sensors was 5, the sensor installation position was 0.24 cm, and the horizontal inclination angle of the monitoring device was 5°. A verification test was carried out under the optimal working parameters. The experimental results showed that the measurement error of the grain yield monitoring system was 3.27%, which met the precision requirement of grain yield monitoring. In addition, the grain yield monitoring system was applied to field harvesting to verify its actual monitoring effect of yield. The field experimental results showed that the error of the field yield measurement was 5.28%. The grain yield monitoring data of the field experiment were filtered and interpolated, and the yield distribution map was finally generated. The yield distribution map could provide decision basis for subsequent variable sowing and fertilizer management. It can be concluded that the grain yield monitoring system had the characteristics of good versatility, convenient installation and high monitoring accuracy. This study could meet the urgent needs of grain yield monitoring in actual production, and had important practical significance for realizing intelligent and digital agriculture. [ABSTRACT FROM AUTHOR]