Han Changjie, Wang Yurong, Shi Zhai, Xu Yang, Qiu Shilong, Mao Hanping
College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
Sensors (Basel). 2024 Feb 22;24(5):1408. doi: 10.3390/s24051408.
Detecting the moisture content of grain accurately and rapidly has important significance for harvesting, transport, storage, processing, and precision agriculture. There are some problems with the slow detection speeds, unstable detection, and low detection accuracy of moisture contents in corn harvesters. In that case, an online moisture detection device was designed, which is based on double capacitors. A new method of capacitance complementation and integration was proposed to eliminate the limitation of single data. The device is composed of a sampling mechanism and a double-capacitor sensor consisting of a flatbed capacitor and a cylindrical capacitor. The optimum structure size of the capacitor plates was determined by simulation optimization. In addition to this, the detection system with software and hardware was developed to estimate the moisture content. Indoor dynamic measurement tests were carried out to analyze the influence of temperature and porosity. Based on the influencing factors and capacitance, a model was established to estimate the moisture content. Finally, the support vector machine (SVM) regressions between the capacitance and moisture content were built up so that the R2 values were more than 0.91. In the stability test, the standard deviation of the stability test was 1.09%, and the maximum relative error of the measurement accuracy test was 1.22%. In the dynamic verification test, the maximum error of the measurement was 4.62%, less than 5%. It provides a measurement method for the accurate, rapid, and stable detection of the moisture content of corn and other grains.
准确快速地检测谷物含水量对收获、运输、储存、加工及精准农业具有重要意义。玉米收获机在检测含水量时存在检测速度慢、检测不稳定及检测精度低等问题。针对这种情况，设计了一种基于双电容的在线水分检测装置。提出了一种电容互补与积分的新方法，以消除单一数据的局限性。该装置由采样机构和由平板电容与圆柱电容组成的双电容传感器构成。通过仿真优化确定了电容极板的最佳结构尺寸。除此之外，还开发了具有软硬件的检测系统来估算含水量。进行了室内动态测量试验，以分析温度和孔隙率的影响。基于影响因素和电容建立了估算含水量的模型。最后，建立了电容与含水量之间的支持向量机（SVM）回归模型，使得R2值大于0.91。在稳定性测试中，稳定性测试的标准偏差为1.09%，测量精度测试的最大相对误差为1.22%。在动态验证测试中，测量的最大误差为4.62%，小于5%。它为准确、快速、稳定地检测玉米及其他谷物的含水量提供了一种测量方法。
