基于高光谱图像技术与迁移学习的水晶梨早期损伤检测

doi:10.3964/j.issn.1000-0593(2022)11-3626-05

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Abstract Pear in storage, packaging and transportation processes may occur in different degrees of mechanical damage. If not removed in time, the damage may gradually become serious and rot, resulting in serious economic losses. In order to establish a rapid and non-destructive detection method for early bruise detection and bruise time assessment of pears, hyperspectral imaging technology combined with a transfer learning model was used to identify crystal pears with an early bruise. The hyperspectral images of crystal pears with no bruise, crush bruise at 24 h and crush bruise at 48 h were acquired by the hyperspectral imaging system, and 80 hyperspectral images of crystal pears with no bruise, crush bruise at 24 h and crush bruise at 48 h were obtained. Principal component analysis was performed on the hyperspectral images, and principal component images 4, 5 and 6 (PC4, PC5 and PC6) were selected as the feature images for detecting crystal pear bruises. After the data expansion of the stitched images with three principal components, 160 images of crystal pears with no bruise, crush bruise at 24 h and crush bruise at 48 h were obtained. The training sample set and test sample set were divided according to the ratio of 9∶1, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and transfer learning bruise recognition models based on the ResNet50 network were established, respectively. The results showed that the overall recognition accuracy of SVM, k-NN and ResNet50 network-based transfer learning models for the test set was 83.33%, 85.42% and 93.75%, respectively. The transfer learning model based on the ResNet50 network had the best recognition results, and its correct recognition rates for the test set of crystal pears with no bruise, crush bruise at 24 h and crush bruise at 48 h reached 100%, 83% and 95%, respectively. The results of this study indicate that hyperspectral imaging technology combined with the transfer learning method based on the ResNet50 network can achieve early bruise detection of crystal pears and have a great prediction performance for bruise time, and the longer the bruise time, the higher the recognition accuracy.

Key words：Hyperspectral imaging; Transfer leaning; Crystal pear; Bruise detection

Received: 2021-08-30 Accepted: 2022-04-05

ZTFLH:

TP751

Corresponding Authors: LIU Da-yang E-mail: ldy333ldy@163.com

Cite this article:

WANG Guang-lai,WANG En-feng,WANG Cong-cong, et al. Early Bruise Detection of Crystal Pear Based on Hyperspectral Imaging Technology and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3626-3630.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2022)11-3626-05 OR https://www.gpxygpfx.com/EN/Y2022/V42/I11/3626

[1] GUO Wen-chuan, WANG Ming-hai, YUE Rong(郭文川, 王铭海, 岳绒). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2013, 44(2): 142.
[2] Zhang M, Li C, Takeda F, et al. Transactions of the ASABE, 2017, 60(5): 1489.
[3] Pan X Y, Sun L J, Li Y S, et al. Journal of the Science of Food and Agriculture, 2019, 99(4): 1709.
[4] SUN Shi-peng, PENG Jun, LI Rui, et al(孙世鹏, 彭俊, 李瑞, 等). Food Science(食品科学), 2017, 38(2): 301.
[5] CHEN Xin-xin, GUO Chen-tong, ZHANG Chu, et al(陈欣欣, 郭辰彤, 张初, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(1): 150.
[6] Jiang H, Zhang C, He Y, et al. Applied Sciences-Basel, 2016, 6(12): 450.
[7] TIAN You-wen, WU Wei, LU Shi-qian, et al(田有文, 吴伟, 卢时铅, 等). Food Science(食品科学), 2021, 42(19): 260.
[8] Zhang M Y, Jiang Y, Li C Y, et al. Biosystems Engineering, 2020, 192: 159.
[9] Wang Z D, Hu M H, Zhai G T. Sensors, 2018, 18(4): 1126.
[10] Azizah L M, Umayah S F, Riyadi S, et al. Deep Learning Implementation Using Convolutional Nerual Network in Mangosteen Surface Defect Detection. 2017 7th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2017. 242. doi: 10.1109/ICCSCE.2017.8284412.
[11] Bai Yuhao, Xiong Yingjun, Huang Jichao, et al. Postharvest Biology and Technology, 2019, 156: 110943.
[12] WEI Chen-jie, WANG Ji-fen, ZENG Xiao-hu(卫辰洁, 王继芬, 曾啸虎). Journal of Instrumental Analysis(分析测试学报), 2021, 40(7): 1043.
[13] YU Xiao-na, HUANG Liang, CHEN Peng-di(余晓娜, 黄亮, 陈朋弟). Journal of Chongqing University(重庆大学学报), 2022, https://kns.cnki.net/kcms/detail/50.1044.N.20210615.1554.005.html.
[14] He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 770. doi: 10.1109/CVPR.2016.90.
[15] WANG Chun-shan, ZHOU Ji, WU Hua-rui, et al(王春山, 周冀, 吴华瑞, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2020, 36(20): 209.
[16] LIN Si-han, LI Jing, XUE Long, et al(林思寒, 黎静, 薛龙, 等). Acta Agriculturae Universitatis Jiangxiensis(江西农业大学学报), 2018, 40(4): 835.