RECOGNITION OF DROUGHT STRESS IN MILLET ON HYPERSPECTRAL IMAGING

Millets are one of China's primary traditional food crops, and drought can adversely impact their yield and quality. To quickly detect the degree of drought stress in cereal grains, this study establishes a nondestructive classification model based on hyperspectral imaging technology. The raw spectral data underwent preprocessing using six pretreatment methods and various combinations of these methods. Subsequently, three distinct algorithms were employed for feature wavelength selection. To assess the severity of drought stress on millet, classification models were developed by integrating texture and color features, utilizing Support Vector Machine (SVM), Partial Least Squares Discriminant Analysis (PLS-DA), and Multilayer Perceptron (MLP) algorithms. The results indicate that the D1st-SVM model, based on CARS wavelength selection, exhibits the highest modeling performance when feature wavelengths are fused with significant texture and color variables, achieving an accuracy rate of 93%. These findings suggest that drought identification in millet can be performed quickly and nondestructively by integrating image features through hyperspectral imaging technology.