|
|
|
|
|
|
Early Bruise Detection of Crystal Pear Based on Hyperspectral Imaging Technology and Transfer Learning |
WANG Guang-lai, WANG En-feng, WANG Cong-cong, LIU Da-yang* |
College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150000, China
|
|
|
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.
|
Received: 2021-08-30
Accepted: 2022-04-05
|
|
Corresponding Authors:
LIU Da-yang
E-mail: ldy333ldy@163.com
|
|
[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.
|
[1] |
WANG Cai-ling1,ZHANG Jing1,WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
[2] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
[3] |
MA Qian1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, CHENG Hui-zhu1, 2, ZHAO Yan-chun1, 2. Research on Classification of Heavy Metal Pb in Honeysuckle Based on XRF and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2729-2733. |
[4] |
CAI Jian-rong1, 2, HUANG Chu-jun1, MA Li-xin1, ZHAI Li-xiang1, GUO Zhi-ming1, 3*. Hand-Held Visible/Near Infrared Nondestructive Detection System for Soluble Solid Content in Mandarin by 1D-CNN Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2792-2798. |
[5] |
ZHANG Fu1, 2, WANG Xin-yue1, CUI Xia-hua1, YU Huang1, CAO Wei-hua1, ZHANG Ya-kun1, XIONG Ying3, FU San-ling4*. Identification of Maize Varieties by Hyperspectral Combined With Extreme Learning Machine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2928-2934. |
[6] |
TANG Ting, PAN Xin*, LUO Xiao-ling, GAO Xiao-jing. Fusion of ConvLSTM and Multi-Attention Mechanism Network for
Hyperspectral Image Classification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2608-2616. |
[7] |
LIANG Wan-jie1, FENG Hui2, JIANG Dong3, ZHANG Wen-yu1, 4, CAO Jing1, CAO Hong-xin1*. Early Recognition of Sclerotinia Stem Rot on Oilseed Rape by Hyperspectral Imaging Combined With Deep Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2220-2225. |
[8] |
ZHANG Qian1, YANG Ying1*, LIU Gang1, 2, 3, WU Xiao1, NING Yuan-lin1. Detection of Dairy Cow Mastitis From Thermal Images by Data Enhancement and Improved ResNet34[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 280-288. |
[9] |
FU Peng-you1, 2, WEN Yue2, ZHANG Yu-ke3, LI Ling-qiao1*, YANG Hui-hua1, 2*. Deep Learning Modelling and Model Transfer for Near-Infrared Spectroscopy Quantitative Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 310-319. |
[10] |
WU Yun-fei, LUAN Xiao-li*, LIU Fei. Transfer Learning Modeling of 2,6-Dimethylphenol Purity Based on PLS Subspace Alignment[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3608-3614. |
[11] |
XIANG Song-yang1, 3, XU Zhang-hua1, 2, 4, 5, 6*, ZHANG Yi-wei1, 2, ZHANG Qi1, 3, ZHOU Xin1, 2, YU Hui1, 3, LI Bin1, 2, LI Yi-fan1, 2. Construction and Application of ReliefF-RFE Feature Selection Algorithm for Hyperspectral Image Classification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3283-3290. |
[12] |
CHEN Jin-hao, JIANG Da-peng, ZHANG Yi-zhuo*, WANG Ke-qi*. Research on Data Migration Modeling Method for Bending Strength of
Solid Wood Based on SWCSS-GFK-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1471-1477. |
[13] |
DAI Ruo-chen1, TANG Huan2*, TANG Bin1*, ZHAO Ming-fu1, DAI Li-yong1, ZHAO Ya3, LONG Zou-rong1, ZHONG Nian-bing1. Study on Detection Method of Foxing on Paper Artifacts Based on
Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1567-1571. |
[14] |
ZHOU Bing, LI Bing-xuan*, HE Xuan, LIU He-xiong,WANG Fa-zhen. Classification of Camouflages Using Hyperspectral Images Combined With Fusing Adaptive Sparse Representation and Correlation Coefficient[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3851-3856. |
[15] |
ZHANG Liu1, YE Nan1, MA Ling-ling2, WANG Qi2, LÜ Xue-ying1, ZHANG Jia-bao1*. Hyperspectral Band Selection Based on Improved Particle Swarm Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3194-3199. |
|
|
|
|