Maize seed classification based on image entropy using hyperspectral imaging technology

The seed purity is an important index of the seed quality. Hyperspectral imaging technology was investigated to classify the maize seeds in this study. First, the image entropy of hyperspectral images were extracted as classification features for 17 varieties including 1632 samples, which the spectral region covered 400-1 000 nm and contained 233 wavelengths. Then, sixty-five optimal wavelengths were selected using partial least squares (PLS) projection algorithm. At last, partial least squares discriminant analysis (PLSDA) was used to develop the classification models for the maize seed purity. The results indicated that the training accuracy of 99.19% and the testing accuracy of 98.90% were achieved by the models with the optimal wavelengths (only 27.90% of full wavelengths), which can implement the purity classification of multi-class maize seeds.