Study on Grain Moisture Detection System Based on the Theory of Dielectric Properties

Abstract

After studying the relationship between the moisture content of crops and their relative permittivity, the principle of capacitive sensor and the research results of measuring micro-capacitance, this paper summarizes the theoretical basis of dielectric properties of grain, the dielectric properties of wheat, corn and rice, the relationship between the dielectric properties of these crops and their water content. With these theory analyses, the paper gives a full look on the grain moisture detection system by the means of dielectric properties, which consists of six parts, a cylindrical capacitor, signal conditioning circuits, single-chip control module with an A/D converter, temperature detection module, keyboard module and display module. The system is simple, and can adapt to extreme environment and accomplish the rapid detection in manufacturing. A description is given of the principle, its hardware circuit and software programming flow chart.

The Design and Experimentation of a Corn Moisture Detection Device Based on Double Capacitors

“…When the dielectric constant of the medium changes [ 21 ], the capacitance will change. The dielectric constant of water is almost 80, far higher than corn [ 22 ]. As a result, the relative dielectric constant is different at different levels of moisture.…”

An Improved Optimization Algorithm Based on Density Grid for Green Storage Monitoring System

“…Tan L B conducted a comprehensive study of a grain moisture detection system consisting of six components: one cylindrical capacitor, a signal conditioning circuit, a microcontroller control module with a frequency converter, a temperature detection module, a keyboard module, and a display module. The system is simple and can adapt to extreme environments to achieve rapid inspection in manufacturing processes [12]. A prediction model was developed and the results showed the prediction of r = 0.84, RMSE = 1.75%, and that the hyperspectral image technique could be used for a direct non-destructive detection of moisture mass fraction uniformity.…”

“…Table 1 shows the data from 20 monitoring points in the maize storage monitoring data set. The results of the clustering analysis are shown below: 10,12,13,16,17,18] G = [14] After clustering the data, the results obtained were matched and analyzed according to the national corn quality indicators (Table 2) entered by the user, thus enabling real-time monitoring of the quality status of the stored corn. In the data above and in the following table, A, B, and D were first class corn, and C, E, F, and G were unqualified quality corn.…”