Identification of rapeseed varieties based on hyperspectral imagery - ZOU Wei; FANG Hui; ZHOU Kang-yun; BAO Yi-dan; HE Yong; 何勇 (浙江大学学报. 农业与生命科学版)
Identification of rapeseed varieties by using hyperspectral imaging technique combined with artificial neural network (ANN) was proposed. Hyperspectral images of several rapeseed varieties in range 400-1 000 nm were acquired, and then the principal component analysis (PCA) was performed to select three optimal band images. The texture parameters were extracted from the optimal band images based on gray level histogram and gray level co-occurrence matrix (GLCM) statistical methods. The ANN model was used for the identification of rapeseed varieties. Detection results of ANN model showed that the discriminating rates of rapeseed varieties in the training and prediction sets were 93.75% and 91.67%, respectively. It is indicated that the hyperspectral imaging technology has a good classification and identification effects on rapeseed varieties.