DETECTION OF DEFECTS OF CERASUS HUMILIS FRUITS BASED ON HYPERSPECTRAL IMAGING AND CONVOLUTIONAL NEURAL NETWORKS

Abstract

In order to perform highly effective identification of external defects and increase the additional value of Cerasus Humilis fruits, this study used hyperspectral imaging technology to collect information on intact and defective Cerasus Humilis fruits. Based on the full transition spectrum, partial least squares discriminant analysis (PLS-DA) and back propagation neural networks (BPNN) were used to establish a discriminative model. The competitive adaptive reweighted sampling (CARS) was used to extract feature wavelengths, principal component analysis was used for data compression of single band images, BPNN and convolutional neural networks (CNN) were used for defect Cerasus Humilis fruits recognition of principal component images. The results showed that the overall detection accuracy of PLS-DA and BPNN models based on wavelength spectral information were 83.81% and 85.71%, respectively. BPNN was used to establish the calibration model based on the selected characteristic wavelengths by CARS, the accuracy rate was 90.47%. The classified accuracy of CNN model based on principal component images was 93.33%, which was obviously better than that of BPNN model at 83.81%. The research shows that the CNN model was successfully applied to the detection of Cerasus Humilis fruits defects using hyperspectral imaging. This study provides a theoretical basis for the development of fruit grading and sorting equipment.