基于漫反射光谱的叶面药液浓度检测方法

Method for detecting pesticide concentration on leaf using diffuse reflectance spectroscopy

应用漫反射反射光谱对叶面药液质量浓度进行了检测研究。选择350～1900nm波段,以标准偏差归一化、三点滑动平均滤波、一阶导数组合预处理,应用逐步回归分析、主成分、主成分+人工神经网络、偏最小二乘、偏最小二乘+人工神经网络回归分析建立了5种数学模型。试验结果表明这5种算法的预测均方根误差分别为0.067、0.061、0.059、0.039、0.056,偏最小二乘法建模效果优于其他模型。考虑到不同作物种类对叶面药液浓度影响,选用八角金盘、油菜、青菜3种作物叶片为对象,在偏最小二乘下建模,其预测集相关系数分别为0.994、0.974、0.929,预测均分根误差分别为0.039、0.050、0.075。表明不同种类作物对叶面药液浓度检测影响较小,漫反射光谱技术检测叶面药液浓度是可行的。

In this paper, diffuse reflectance spectroscopy was applied to detect and research the pesticide concentration on the surface of leaves. The optimal hands for 350- 1 900 nm were obtained, the normalization of standard deviation, the average filtering of three slides and the first derivative combination were selected as combination pretreatment method, five kinds of mathematical models with the applications of stepwise regression analysis, principal component, principal components combined with the artificial neural network, partial least squares, partial least squares combined with the artificial neural network were established. The results indicate that the root mean square error of cross-validation of the prediction of five algorithms were 0.067, 0.061, 0.059, 0.039, 0.056 respectively. The partial least squares method has high prediction precision. Considering the effects of different crop types to the leaves prediction precision, three kinds of plant leaves for object：fatsia japonica, brassica napus and green vegetables were selected by using the partial least squares method, the correlation coefficient between the prediction values and the truth values in the prediction set were 0.994, 0.974, 0.929 and the root mean square error of cross-validation of the prediction set were 0.039, 0.050, 0.075. The results indicate that different kinds of crop leaves have less influence on the measurement of solution concentration and it is feasible to test the pesticide concentration with diffuse spectroscopy technology.