基于介电特性与SPA-SVR算法的水稻含水率检测方法

为提高基于介电法水稻含水率的检测精度,以北粳3号水稻为研究对象,利用阻抗分析仪及自制同轴圆柱型电容器测量了不同含水率水稻在1 kHz～1 MHz频率下的相对介电常数ε′及介质损耗因数ε"。采用x-y共生距离法划分了72个样本校正集和48个样本预测集。利用无信息变量消除法及连续投影法选取介电参数(ε′、ε"及ε′和ε"两者结合)的特征变量,分别利用所提取的特征变量以及单频、全频下的介电参数来建立预测水稻含水率的多元线性回归及支持向量机回归模型,分析模型的预测性能,并对最佳模型的含水率预测结果进行温度补偿。结果表明:基于ε′与ε"两者结合并利用连续投影法提取特征变量建立的支持向量机回归模型预测效果最佳,其预测集决定系数为0.980,预测均方根误差为0.403%。最佳预测模型对不同品种水稻的含水率预测值与烘干法测得的含水率实测值的绝对误差集中分布在±0.5%内,该研究可为粮食含水率的检测提供参考。

Rice moisture content detection method based on dielectric properties and SPA-SVR algorithm

Water content affects rice quality, which also has an important impact on rice storage, transportation, acquisition and processing. The annual loss of production caused by grain deterioration was up to 10 million tons, and the economic loss was up to 20 billion. Therefore, detecting rice moisture content accurately is beneficial to improve rice quality and reduce yield loss. A new method based on dielectric properties was proposed to detect the moisture content of rice in this study. Firstly, the dielectric properties (relative dielectric constant and dielectric loss factor) of 120 copies of rice of Japonica No.3 with different moisture contents were measured with impedance analyzer and self-made coaxial cylindrical capacitor at 201 discrete frequencies over the frequency range of 1 kHz-1 MHz, and the moisture contents of rice were measured by dry weight method. Secondly, sample set partitioning based on joint x-y distances (SPXY) was used to subset partitioning. Uninformative variables elimination (UVE) and successive projection algorithm (SPA) were applied to extract the characteristic variables of dielectric parameters ((the relative dielectric constant, dielectric loss factor and relative dielectric constant combined with dielectric loss factor). And the effect of SPA was compared with that of UVE to determine the optimal method for characteristic variable selection simultaneously. Finally, the support vector regression (SVR) machine and multiple linear regression (MLR) were adopted to establish the relationship models with two kinds of characteristic variables, single variables and full variables for predicting rice moisture content. And the performances of all the models were evaluated by the determination coefficient and root mean square error for calibration set and prediction set. The least square method was used for linear regression of predicted moisture content and measured moisture content at different temperatures, and the temperature compensation was carried out for the prediction results. The performances of the best model to predict different varieties of rice moisture content were explored to determine the applicability of the model. The research results showed that the relative dielectric constant decreased with the increase of the measurement frequency between 1kHz and 1MHz. When the frequency was greater than 300 kHz, the dielectric loss factor decreased with the increase of frequency and increased with the increase of water content. The measurement frequency and moisture content had an obvious effect on the dielectric properties of rice. Based on SPXY, 72 samples were partitioned to a calibration set and 48 samples to a prediction set. SPA was more effective than UVE in selecting useful information from the whole spectra of dielectric constant and dielectric loss factor. The model established by using the combination of relative dielectric constant and dielectric loss factor at multiple frequencies had better performance in predicting moisture content, which compared with the single dielectric parameter at a single frequency. Compared with MLR, SVR had better performance in predicting moisture content. The results showed that the support vector machine regression model based on the combination of relative dielectric constant and dielectric loss factor and SPA gave the highest correlation coefficient of predication set (0.980) and the lowest root mean square error of predication set (0.403%). When the best model was used to predict the water content of different varieties of rice, the prediction results were more accurate. Compared with the measured water content by the drying method, the prediction error was concentrated within ±0.5%. The study provided a reference for improving the accuracy of the grain moisture detection device.