Identification of Red Bean Variety with Probabilistic GA—PNN Based on Hyperspectral Imaging

Abstract:

A method to identify different varieties of red bean based on hyperspectral imaging technology was proposed. The hyperspectral imaging system with spectrum range of 390~1050nm was used to capture the hyperspectral images of 162 red bean samples, which were collected from three different areas (Anhui, Shandong and Jiangsu Provinces). ENVI software was adopted to determine the region of interest (ROI) in the hyperspectral image and extract the hyperspectral data by averaging the reflectance from all the pixels in the ROI images, and the original spectra were preprocessed by Savitzky—Golay (SG) smoothing. As there was a large number of noise and redundant information in the raw hyperspectral images and hyperspectral data, some data processing methods should be used to remove the noise, accelerate the processing efficiency and improve the performance of the models. The method of feature extraction was SPA, the number of characteristic wavelengths was determined as 9 by using the leave-one-out cross-validation. The methods of feature selection were PCA and ICA. According to the standard of the cumulative contribution rate of variance was more than 85%, seven characteristic wavelengths were selected. Through test and verification, 17 was the best number of characteristic wavelengths of ICA. Finally, the selected optimal characteristic wavelengths and principal components were used as the inputs of the model. However, the results did not meet the expected accuracy, the threshold of PNN neural network and hidden layer nodes were optimized by GA. The recognition rate of the model was higher than 85%, and the recognition rate of the highest SPA—GA—PNN model reached 97.5%. The results demonstrated that it was feasible to use hyperspectral imaging technology for the identification of red bean variety. PNN neural network model can identify red bean variety fast, effectively and nondestructively and provide theoretical basis and technical means for the realization of red bean variety identification based on hyperspectral image technology.