Identification of Winter Melon Seed Varieties Based on Hyperspectral Technology

In order to achieve rapid non-destructive testing of winter melon seed varieties, spectral image information of winter melon seeds of three varieties of Guangdongheipi, Yilchuanling and Hanyufenpi was collected by hyperspectral imaging technology．Standard normalization（SNV）, multiple scattering correction（MSC）, median filtering（MF）and other methods were used for pretreatment．Feature wavelengths were extracted using successive projection algorithm（SPA）, regression coefficient（RC）, and competitive adaptive algorithm（CARS）．Particle swarm optimization BP neural network discriminant model was established, and coefficient of determination and root mean square error of prediction set were R²=0.930 and RMSEP=0.047, respectively, and accuracy rate was as high as 96.30%．Results showed that hyperspectral technology combined with particle swarm optimization BP neural network could achieve rapid and non-destructive identification of winter melon seed varieties．