A recognition method of corn varieties based on spectral technology and deep learning model

In order to overcome the tedious and time-consuming chemical analysis process and the low accuracy of traditional machine learning model in recognizing varieties, in this study, a recognition model of corn varieties was proposed based on convolutional neural network (LeNet-5) combining the near-infrared(NIR) spectrum processing technology and the deep learning model. First, 450 groups of NIR spectral data of 6 different varieties of corn were acquired by Fourier transform near-infrared spectrometer, and randomly divided into training set and test set according to the ratio of 2:1. Then, the spectral data were preprocessed by the de trending algorithm (DT), and 114 characteristic wavenumbers were extracted from the original NIR spectral data using the competitive adaptive reweighting sampling algorithm (CARS). Finally, based on the optimal selection of spectral characteristic wave numbers, a recognition model of corn varieties was constructed based on LeNet-5 convolution neural network. The results showed that the accuracy of the recognition model was 99.20 %, and the average time was 0.3500 s. Compared with the back propagation neural network (BP), K-nearest neighbor (KNN), support vector machine (SVM), and partial least squares (PLS), the average value of the recognition accuracy was improved by 25.78 %, which provided a new idea and method for accurate and rapid recognition of corn varieties.