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In this study, Hyperspectral Imaging System (400-1,000nm) was used to discriminate the maturity of peanut cluster. Hyperspectral data of 480 peanut samples in 4 different mature stages were collected by Hyperspectral Imaging (HSI) system, respectively. Principal component analysis (PCA) was conducted on the hyperspectral images. Support vector machine (SVM)was used established discriminant model using the spectra basing on feature wavelength (extract the 4 feature wavelengths were 415, 575, 727, and 930 nm). The results of the discrimination model depending on PCA indicated that the different mature was accurately identified with a calibration accuracy of 94.5% and a prediction accuracy of 89.1%; the results of the discrimination model based on feature wavelength was accurately detected with a calibration accuracy of 93.5% and a prediction accuracy of 89.5%. The overall results indicated that an HSI technique was constructed from spectral and image information both aspects of information, which can be combined with multivariate analysis could be used as an effective tool for non-destructive and rapid variety discrimination and visualization of different mature stages peanut cluster in one plant. The proposed method provides great potential for developing a multi-spectral imaging system for practical application nondestructive testing of peanut cluster maturity. This study contributes theoretical guiding significance and can be applied for the further identification of peanut maturity in the field and industry of the peanut production chain.
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