Abstract：High-quality rice contains more nutritional value and higher economic value. In order to earn more benefits, some unscrupulous merchants have adulterated high-quality rice or even replaced it with low-quality rice, which has harmed consumer interests and rice trade, and has hurt producers Production motivation. This paper hopes to develop a method for non-destructive identification of high-quality rice based on the features of images and spectra of hyperspectral imaging and deep learning networks. First, hyperspectral images in the 400～1 000 nm range of seven representative rice varieties in China were collected, and the spectra, texture, and shape features of each type of rice were extracted. The spectral features were pre-processed using the multiple scattering correction algorithms to eliminate spectral scattering. Successive projections algorithm (SPA), competitive adaptive weighting algorithm (CARS) and their cascade method (CARS-SPA) were used to select important wavelengths of spectral features. Important variables of shape and texture features were selected using SPA. Finally, convolutional neural network (CNN) was applied to fuse the above-mentioned various features to build rice varieties recognition model, while K-Nearest Neighbors (KNN) and Random Forest (RF) were used for comparison and analysis. The experimental results showed that the classification accuracy of the model constructed using the full spectroscopy reached more than 80%. Among them, KNN had the worst modeling effect and RF had a better effect. In particular, the performance of the CNN model was the best, with training set classification accuracy (ACCT) of 92.96% and prediction set classification accuracy (ACCP) of 89.71%. Compared with the full spectroscopy, the spectroscopy of the important wavelengths had worse classification accuracy. In order to further improve the accuracy of rice varieties identification, texture and shape were combined with spectral features, and the optimal result came from the model constructed of important variables of shape and spectroscopy. Among them, ACCT and ACCP of KNN were 69% and 67%, respectively. The RF model accuracy corresponded to ACCT=99.98% and ACCP=89.10%. The CNN model performed best with ACCT and ACCP of 97.19% and 94.55%. In addition, the classification effect of spectroscopy and texture fusion was worse than using only spectroscopy, indicating that texture features weakened the classification result. For classification models, the performance of CNN was significantly better than the two machine learning methods, which could provide better classification results. All in all, the important variables of shape and spectroscopy combined with CNN models could accurately identify high-quality rice varieties. The proposed method can also be applied to the identification of the variety, attribution and grade of other agricultural products.
[1] Chen J, Huang Y, Tang Y. Agriculture, Ecosystems & Environment, 2011, 142(3-4): 195.
[2] Amagliani L, O’Regan J, Kelly A L, et al. Journal of Food Composition and Analysis, 2017, 59: 18.
[3] Chenglong W, Xiaoyu L, Zhenzhong W, et al. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(1).
[4] Sun X D, Zhang H L, Liu Y D. International Journal of Agricultural and Biological Engineering, 2009, 2(1): 65.
[5] ElMasry G, Kamruzzaman M, Sun D W, et al. Critical Reviews in Food Science and Nutrition, 2012, 52(11): 999.
[6] YANG Xiao-ling, YOU Zhao-hong, CHENG Fang(杨小玲, 由昭红, 成 芳). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016，36(12): 4028.
[7] WU Jing-zhu, LIU Qian, CHEN Yan(吴静珠，刘 倩，陈 岩). Infrared and Laser Engineering(红外与激光工程)，2016, 45(S1): 134.
[8] Wang L, Liu D, Pu H, et al. Food Analytical Methods, 2015, 8(2): 515.
[9] Liu C, Cao Y, Luo Y, et al. Deep Food: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment. International Conference on Smart Homes and Health Telematics. Springer, Cham, 2016，9677：37.
[10] XIE Zhong-hong，XU Huan-liang，HUANG Qiu-gui，et al(谢忠红, 徐焕良, 黄秋桂, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2019, 35(13)：277.
[11] GUI Jiang-sheng，WU Zi-xian，LI Kai(桂江生, 吴子娴, 李 凯). Journal of Zhejiang University·Agric. & Life Sci.(浙江大学学报·农业与生命科学版), 2019, 45(2): 256.
[12] Fan X, Ming W, Zeng H, et al. Analyst, 2019, 144(5): 1789.
[13] Martinsen P, Schaare P. Postharvest Biology and Technology, 1998, 14(3): 271.
[14] Yu X, Lu H, Wu D. Postharvest Biology and Technology，2018，141：39.
