Aiming at the problem that the traditional machine vision has low discrimination accuracy when realizing the fresh level recognition of spinach, A new method for fresh grade recognition of spinach based on hyperspectral and deep learning was conducted in this study. Round leaf spinach stored in room temperature 10oC on a daily basis was regarded as research objects. The spinach was divided into three grades of fresh, relatively fresh and corruption according to the score calculated by considering 6 factors: fresh spinach days of storage, appearance, water content, chlorophyll a, chlorophyll b, and carotenoids. After 6 ROI areas was obtained from the hyperspectral image of spinach leaves shot with high spectrum imaging instrument, the mean reflectance of ROI region was calculated. Based on the grouping elite strategy genetic algorithm, an adaptive grouping strategy was used to screen out a set of wavelengths A, A={389.55 nm, 401.629 nm, 742.325 nm, 949.939 nm, 1 025.662 nm(in Chinese with English abstract)}. Then the artificial grouping strategy was also used for wavelength screening. The number of statistical groups was the wavelength selected by n = 1, 2, 3...n, and the four frequencies with the highest frequency were placed in the set B, B={389.55 nm, 536.365 nm, 742.325 nm, 1 025.662 nm (in Chinese with English abstract)}. The six wavelengths in the A∪B set were combined as the final selected wavelengths, and these wavelengths were better able to identify the fresh grade of spinach. Define training set R and test set T, R and T each containing 240 spinach samples. Using the SVM classifier, based on the spine reflectance corresponding to the six wavelengths, a fresh grade classification test based on the spectral characteristics to define spinach was separately performed. After 10 trials, the mean value of recognition accuracy was obtained, and the three wavelengths with the highest recognition rate were found, which were 389.55, 742.325 and 1 025.662 nm, respectively. The corresponding recognition rates were 62.08%, 60.00% and 60.42%, respectively. This indicated that the recognition rate of spinach fresh grade was low based on spectral characteristics. In addition to the spectral properties, spinach's hyperspectral image also contains rich image information corresponding to all wavelengths, so further spine fresh grade recognition based on image features can be performed. The spinach images corresponding to the three wavelengths extracted from the hyperspectral image set constituted an image sample library. Based on the deep learning technology, the spine fresh grade recognition model was established. The recognition experiments were carried out on four types of images (NormImg389, NormImg742, NormImg1 025 and NormImg_merge) in the image sample library. The average recognition accuracy of the three experiments was 79.69%, 68.75%, 69.27% and 80.99%. The NormImg389 and NormImg_merge test sets had higher recognition rates, which were close to 80%. The image recognition rate of spinach in NormImg_merge was up to 80.99%, which indicated that when the spinach fresh level recognition was performed, the images corresponding to the three wavelengths were merged. Identifying can get the best classification results. This study achieved the non-destructive testing of the fresh grade of round leaf spinach, and the research results provided quality assurance for industrial processing and marketing, which has practical and theoretical significance. © 2019, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
