压力式谷物产量监测系统优化与试验验证

为了提高谷物收获作业过程中谷物产量在线监测的精度,研制了基于谷物流压力原理的车载谷物产量在线监测系统,该系统包括谷物流量监测装置、定位装置、割台高度控制开关、核心处理器以及人机交互装置。以谷物产量与谷物流压力间的谷物产量监测数学模型为指导,搭建了谷物产量监测试验台,采用Box-Behnken试验设计方法优化谷物流量监测装置结构参数,研究了传感器数量、传感器安装位置和监测装置水平倾角对谷物产量监测系统测产误差的影响,确定了最优参数组合为传感器数量5、传感器安装位置0.24 cm、监测装置水平倾角5°,并对最优参数组合进行了验证试验,结果表明,谷物产量监测系统测产误差为3.27%,满足谷物产量监测的精度要求。对谷物产量监测系统田间实际效果进行了试验验证,试验结果表明,田间测产误差为5.28%,生成的产量分布图为后续田间作业管理提供了决策依据。

Optimization and experimental verification of grain yield monitoring system based on pressure sensors

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.