Study on the identification of resistance of rice blast based on near infrared spectroscopy.

Rice Blast is the most devastating rice disease which poses a serious threat to the safe production of rice. The most effective way to prevent rice blast is to cultivate the rice varieties that have resistance to the disease, however, traditional resistance testing requires professional personnel, a tedious process, long determination time and high cost. In order to quickly identify different resistant rice seeds which are difficult to distinguish with the naked eye, a rapid non-destructive identification method based on Near-Infrared Spectroscopy (NIRS) was proposed. Four different types of resistant rice seeds (high resistance, high susceptibility, susceptibility and resistance) came from in HeiLongjiang province of China were selected as the research objects. A total of 240 spectral data (60 from each variety) were scanned by the NIR spectrometer. The BP neural network (BP), Support Vector Machines (SVM), Probabilistic Neural Network (PNN) models were established based on the original spectral data in the full-spectrum (11520-4000 cm). Among all, Raw-BP has the best identification accuracy which reaches 100% with an iteration time of 869 s. After extracting the feature wavelengths by successive projections algorithm (SPA) on the spectral data, Raw-SPA-BP, Raw-SPA-SVM and Raw-SPA-PNN models were established. The accuracy of these three models didn't improve. But the iteration time of the SPA-BP model was shortened to 791 s. Another group of BP, SVM, and PNN models were established after using different spectral pretreatment methods and the SPA feature extraction. After Multivariate Scatter Correction (MSC), the accuracy of the MSC-SPA-BP model was still 100% and the iteration time was shortened to 840 s, which is 1/30 of the time at which the original data model was formed. The accuracy of the MSC-SPA-PNN model increased from 60% to 90% and the accuracy of the MSC-SPA-SVM model increased from 60% to 85%. Based on the comparison analysis of the models mentioned above, a best neural network identification model of the MSC-SPA-BP with 513 inputs, 8 hidden layers and 4 outputs was established. Its classification accuracy reached 100% with an iteration time of 29 s, indicating that the MSC-SPA-BP model can completely achieve identification of four different resistant rice seeds. Therefore, the proposed method of the BP neural network identification model based on NIRS can be fully applied to the non-destructive rapid identification of rice seeds. Meanwhile, it provides a reference for the rapid identification of other crop seeds.