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| Cotton Verticillium Wilt Severity Detection Based on Hyperspectral
Imaging and SSFNet |
| WU Nian-yi1, CANG Hao1, GAO Xiu-wen1, LI Yong-quan1, TAN Fei1, DI Ruo-yu1, RUAN Shi-wei1, GAO Pan1*, LÜ Xin2* |
1. College of Information Science and Technology, Shihezi University, Shihezi 832003，China
2. College of Agriculture, Shihezi University, Shihezi 832003，China
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Abstract Verticillium wilt poses a severe threat to cotton yield and quality. Rapid and accurate detection of Verticillium wilt is essential for controlling cotton Verticillium wilt (CVW). Existing CVW detection methods mainly focus on the image or spectral level, overlooking the importance of feature fusion, which limits model performance. We propose a CVW grade detection method, spatial-spectral Fusion Network (SSFNet), to address this. First, we enhance the LAB color space, which is sensitive to pixel changes in infected plants, to enrich the feature representation of RGB images and use an improved ResNet network to build an image feature extraction module. Next, we construct a spectral feature extraction module based on the improved ResNet network and compare the performance of two common feature extraction methods: Least Absolute Shrinkage and Selection Operator (LASSO) and Principal Component Analysis (PCA). Finally, we build the feature fusion model SSFNet based on image and spectral level exploration. Experimental results show that SSFNet performs best compared to single data type features, with an F1 score of 95.96%, demonstrating the potential of image-spectral feature fusion methods combined with deep learning for CVW grade detection.
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Received: 2024-06-18
Accepted: 2024-09-12
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Corresponding Authors:
GAO Pan, LÜ Xin
E-mail: gp_inf@shzu.edu.cn; lxshz@126.com
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[1] Bardak A, Çelik S, Erdoğan O, et al. Plants, 2021, 10(2): 306.
[2] Tao X, Zhang H, Gao M, et al. AMB Express, 2020, 10: 193.
[3] Shrivastava V K, Pradhan M K, Thakur M P. Proceedings of 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), 2021, IEEE, doi: 10.1109/ICAIS50930.2021.9395813.
[4] Ma R, Zhang N, Zhang X, et al. Computers and Electronics in Agriculture, 2024, 217: 108628.
[5] Zhang N, Zhang X, Shang P, et al. Remote Sensing, 2023, 15(13): 3373.
[6] Caldeira R F, Santiago W E, Teruel B. Sensors, 2021, 21(9): 3169.
[7] Chen B, Wang Q, Wang J, et al. 2023 International Seminar on Computer Science and Engineering Technology (SCSET), 2023, 520(doi: 10.1109/SCSET58950.2023.00120).
[8] Elaraby A, Hamdy W, Alruwaili M. Computers, Materials & Continua, 2022, 71(2): 4019.
[9] Zhang K, Wu Q, Liu A, et al. Advances in Multimedia, 2018, https://doi.org/10.1155/2018/6710865.
[10] Liang W J, Zhang H, Zhang G F, et al. Scientific Reports, 2019, 9(1): 2869.
[11] Nawaz M, Nazir T, Javed A, et al. Scientific Reports, 2022, 12(1): 18568.
[12] Cheng H, Li H. Frontiers in Plant Science, 2023, 14: 1274231.
[13] Feng L, Wu B, He Y, et al. Frontiers in Plant Science, 2021, 12: 693521.
[14] Jin X, Jie L, Wang S, et al. Remote Sensing, 2018, 10(3): 395.
[15] Yadav P K, Burks T, Frederick Q, et al. Frontiers in Plant Science, 2022, 13: 1043712.
[16] Li H, Ju W, Song Y, et al. Computers and Electronics in Agriculture, 2024, 217: 108561.
[17] Huang C, Zhang Z, Zhang X, et al. Plant Phenomics, 2023, 5: 0013.
[18] Wang Q, Wu B, Zhu P, et al. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, 11534.
[19] Wang X, Girshick R, Gupta A, et al. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, 7794.
[20] Gorry P A. Analytical Chemistry, 1990, 62(6): 570.
[21] Bugeau A, Giraud R, Raad L. Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, 2023, 847.
[22] Bao W, Cheng T, Zhou XG, et al. Computers and Electronics in Agriculture, 2022, 203: 107485.
[23] Alirezazadeh P, Schirrmann M, Stolzenburg F. Gesunde Pflanzen, 2023, 75(1): 49.
[24] Hu J, Shen L, Sun G. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, 7132.
[25] Huang G, Liu Z, Van der Maaten L, et al. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, 4700.
[26] Sinha D, El-Sharkawy M. 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2019, 0280(doi: 10.1109/UEMCON47517.2019.8993089).
[27] Tan M, Le Q V. Proceedings of the 36th International Conference on Machine Learning, 2019, 6105.
[28] He K, Zhang X, Ren S, et al. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, 770.
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