Variety classification and identification of maize seeds based on hyperspectral imaging method

In this study, eight different varieties of maize seeds were used as the research objects. Conduct 81 types of combined preprocessing on the original spectra. Through comparison, Savitzky-Golay (SG)-multivariate scattering correction (MSC)-maximum-minimum normalization (MN) was identified as the optimal preprocessing technique. The competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and their combined methods were employed to extract feature wavelengths. Classification models based on back propagation (BP), support vector machine (SVM), random forest (RF), and partial least squares (PLS) were established using full-band data and feature wavelengths. Among all models, the (CARS-SPA)-BP model achieved the highest accuracy rate of 98.44%. This study offers novel insights and methodologies for the rapid and accurate identification of corn seeds as well as other crop seeds.