|
|
|
|
|
|
Segmentation of Cucumber Target Leaf Spot Based on U-Net and Visible Spectral Images |
WANG Xiang-yu1, LI Hai-sheng1, LÜ Li-jun1, HAN Dan-feng1, WANG Zi-qiang2* |
1. Department of Electronic Information and Physics, Changzhi University, Changzhi 046011, China
2. Industrial Technology Center, Chengde Petroleum College, Chengde 067000, China |
|
|
Abstract Target leaf spot is one of the main fungous diseases of cucumber. Under suitable conditions, especially under the conditions of the large temperature difference between day and night or saturated humidity, the disease develops rapidly, leads to the reduction of cucumber yield and brings economic losses. The cucumber target leaf spot segmentation can provide an effective basis for the identification and diagnosis of cucumber disease, which has great significance. In this study, a cucumber spectral image was taken as the research object, and U-net deep learning network was utilized to construct the semantic segmentation model for cucumber target leaf spot segmentation. Firstly, the regions with more prominent lesions in the visible spectrum images were selected for training and testing. We captured 135 regions out of 40 images as samples, and each region was 200×200 pixel. The Image labeler tool of Matlab was used to label the samples to mark the affected area and the healthy area. Then, the U-net network was constructed, which contains 46 layers and 48 connections. The cucumber target leaf spots’ feature extraction is completed by convolution layer, ReLU layer and max-pooling. The upsampling is completed by deep connection layer, up convolution layer and up-ReLU. The copy and crop operations and feature fusion are completed by skip connection. The U-net was used for training to get the semantic segmentation model. From 135 samples, 96 were randomly selected as training samples and the remaining 39 as test samples. Set the iterations 240, L2 regularization coefficient 0.000 1, initial learning rate 0.05, momentum parameter 0.9, gradient threshold 0.05, and then utilize the samples for training and testing. After 10 repeated training and testing, the results showed that the average execution time of the semantic segmentation model based on U-net and visible spectrum images was 46.4 s. The average memory occupation was 6 665.8 MB, and it shows that the model has a high execution efficiency. The pixel accuracy of the model was 96.23% ~ 97.98%, mean pixel accuracy was 97.28%~97.87%, mean intersection over union was 86.10%~91.59%, frequency weighted intersection over union was 93.33%~96.19%. It shows that the model has good stability and strong generalization ability. This research used less training samples to obtain a segmentation model with high accuracy, which provides a reference for small sample machine learning and provides a method basis for other vegetable disease spot segmentation, disease identification and diagnosis.
|
Received: 2020-09-06
Accepted: 2020-12-27
|
|
Corresponding Authors:
WANG Zi-qiang
E-mail: cdpc_wzq@cdpc.edu.cn
|
|
[1] Yu G, Yu Y, Fan H, et al. Biochemistry Biokhimiia, 2019, 84(8): 963.
[2] Duan Yabing, Xin Wenjing, Lu Fei, et al. Pesticide Biochemistry and Physiology, 2019, 153: 95.
[3] LI Xiao-hong(李晓红). China Vegetables (中国蔬菜), 2016(3): 66.
[4] LAN Guo-bing, TAN Yao-hua, HE Zi-fu, et al(蓝国兵,谭耀华,何自福,等). Plant Protection(植物保护), 2012, 38(5): 197.
[5] REN Shou-gang, JIA Fu-wei, GU Xing-jian, et al(任守纲,贾馥玮,顾兴健,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2020, 36(12): 186.
[6] BAI Xue-bing, YU Jian-shu, FU Ze-tian, et al(白雪冰,余建树,傅泽田,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(11): 3592.
[7] Zahra Ebrahimi, Mohammad Loni, Masoud Daneshtalab, et al. Expert Systems with Applications: X, 2020, 7: 1.
[8] Nicholas Polson, Vadim Sokolov. Wiley Interdisciplinary Reviews: Computational Statistics, 2020, 12(5): 1.
[9] HU Yue, LUO Dong-yang, HUA Kui, et al(胡 越,罗东阳,花 奎,等). CAAI Transactions on Intelligent Systems(智能系统学报), 2019, 14(1): 1.
[10] ZHAO Xin-yang, CAI Chao-peng, WANG Si, et al(赵欣洋,蔡超鹏,王 思,等). Light Industry Machinery(轻工机械), 2019, 37(3): 60.
[11] Long J, Shelhamer E, Darrell T. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2014, 39(4): 640.
[12] XU Wen-bo, REN Ya-feng, HAN Bing(徐文博,任亚峰,韩 冰). Journal of Mechanical Transmission(机械传动), 2020, 44(8): 78.
[13] LI Xiao-juan, XU Zeng-bing, XIONG Wen, et al(李小娟,徐增丙,熊 文,等). Journal of Vibration and Shock(振动与冲击), 2020, 39(15): 25.
[14] GUO Lin, QIN Shi-yin(郭 琳,秦世引). Journal of Beijing University of Aeronautics and Astronautics(北京航空航天大学学报), 2019, 45(1): 159.
[15] Paul H Yi, Jinchi Wei, Tae Kyung Kim, et al. The Knee, 2019, 27(2): 535.
[16] YANG Sen, FENG Quan, ZHANG Jian-hua, et al(杨 森,冯 全,张建华,等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2020, 51(7): 22.
[17] XUE Yong, WANG Li-yang, ZHANG Yu, et al(薛 勇,王立扬,张 瑜,等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2020, 51(7): 30.
[18] Nikos Petrellis. Symmetry-basel, 2018, 10(7): 270.
[19] Ruihui Mu, Xiaoqin Zeng. KSII Transactions on Internet and Information Systems, 2019, 13(4): 1738.
[20] Yi Zhike, Chang Tao, Li Shuai, et al. IEEE Access, 2019, 7: 69184.
[21] Zhang Sanxing, Ma Zhenhuan, Zhang Gang, et al. Symmetry-basel, 2019, 12(3): 427. |
[1] |
LI Yu1, ZHANG Ke-can1, PENG Li-juan2*, ZHU Zheng-liang1, HE Liang1*. Simultaneous Detection of Glucose and Xylose in Tobacco by Using Partial Least Squares Assisted UV-Vis Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 103-110. |
[2] |
WANG Yu-chen1, 2, KONG Ling-qin1, 2, 3*, ZHAO Yue-jin1, 2, 3, DONG Li-quan1, 2, 3*, LIU Ming1, 2, 3, HUI Mei1, 2. Hyperspectral Reconstruction From RGB Images for Tissue Oxygen
Saturation Assessment[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3193-3201. |
[3] |
YE Wen-chao1, LUO Shui-yang1, LI Jin-hao1, LI Zhao-rong1, FAN Zhi-wen1, XU Hai-tao1, ZHAO Jing1, LAN Yu-bin1, 2, DENG Hai-dong1*, LONG Yong-bing1, 2, 3*. Research on Classification Method of Hybrid Rice Seeds Based on the Fusion of Near-Infrared Spectra and Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2935-2941. |
[4] |
WANG Yi-ru1, GAO Yang2, 3, WU Yong-gang4*, WANG Bo5*. Study of the Electronic Structure, Spectrum, and Excitation Properties of Sudan Red Ⅲ Molecule Based on the Density Functional Theory[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2426-2436. |
[5] |
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. |
[6] |
MAO Yi-lin1, LI He1, WANG Yu1, FAN Kai1, SUN Li-tao2, WANG Hui3, SONG Da-peng3, SHEN Jia-zhi2*, DING Zhao-tang1, 2*. Quantitative Judgment of Freezing Injury of Tea Leaves Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2266-2271. |
[7] |
LIU Mei-jun, TIAN Ning*, YU Ji*. Spectral Study on Mouse Oocyte Quality[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1376-1380. |
[8] |
CI Cheng-gang*, ZANG Jie-chao, LI Ming-fei*. DFT Study on Spectra of Mn-Carbonyl Molecular Complexes[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1434-1441. |
[9] |
LI Kai-yu1, ZHANG Hui2, MA Jun-cheng3, ZHANG Ling-xian1*. Segmentation Method for Crop Leaf Spot Based on Semantic Segmentation and Visible Spectral Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1248-1253. |
[10] |
CHEN Qing1, TANG Bin1, 2*, LONG Zou-rong1, 2, MIAO Jun-feng1, HUANG Zi-heng1, DAI Ruo-chen1, SHI Sheng-hui1, ZHAO Ming-fu1, ZHONG Nian-bing1. Water Quality Classification Using Convolution Neural Network Based on UV-Vis Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 731-736. |
[11] |
LIU Si-qi1, FENG Guo-hong1*, TANG Jie2, REN Jia-qi1. Research on Identification of Wood Species by Mid-Infrared Spectroscopy Based on CA-SDP-DenseNet[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 814-822. |
[12] |
WANG Ren-jie1, 2, FENG Peng1*, YANG Xing3, AN Le3, HUANG Pan1, LUO Yan1, HE Peng1, TANG Bin1, 2*. A Denoising Algorithm for Ultraviolet-Visible Spectrum Based on
CEEMDAN and Dual-Tree Complex Wavelet Transform[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 976-983. |
[13] |
LI Yun-xia1, MA Jun-cheng2, LIU Hong-jie3, ZHANG Ling-xian1*. Tillering Number Estimation of Winter Wheat Based on Visible
Spectrogram and Lightweight Convolutional Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 273-279. |
[14] |
LENG Si-yu1, 2, QIAO Jia-hui1, WANG Lian-qing3, WANG Jun1, 2*, ZOU Liang1. Rapid Qualitative Analysis of Wool Content Based on Improved
U-Net++ and Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 303-309. |
[15] |
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. |
|
|
|
|