Monitoring of Paddy Rice Varieties Based on the Combination of the Laser-Induced Fluorescence and Multivariate Analysis

Abstract

Paddy rice is one of three major cereal crops in China, and the number of the paddy rice variety is increasing rapidly. The paddy rice variety is strongly related to crop yield and is also difficult to classify by using the naked eyes. A reliable approach is essential for accurately identifying different paddy rice varieties. Laser-induced fluorescence (LIF) technology has been widely utilized in many fields due to its particular advantages (rapid, non-intrusive, and sensitive). Thus, LIF combined with multivariate analysis that contained principal component analysis (PCA) and support vector machine (SVM) was proposed and was attempted to be utilized to identify different paddy rice varieties in this investigation. These fluorescence spectra displayed a high degree of multi-collinearity, and about 96.58% of the total variance contained in the laser-induced fluorescence spectra which were excited by a 532-nm excited wavelength can be explained by using the first three principle components. A SVM model with the help of threefold cross validation was used for paddy rice variety identification based on new variables calculated utilizing PCA. The numerical and experimental results displayed by using a confusion matrix and the classification accuracy can reach up to 91.36%. Thus, LIF technology combined with multivariate analysis can provide researchers with a faster and more effective tool for identifying different paddy rice varieties.