Pear leaf disease segmentation method based on improved DeepLabv3+

Reference

• Azad, R., Asadi-Aghbolaghi, M., Fathy, M., & Escalera, S. (2020). Attention deeplabv3+: Multi-level context attention mechanism for skin lesion segmentation. European conference on computer vision (pp. 1–16). Springer International Publishingvolctrans extension anchor for patchId=2727.
   Google Scholar
• Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481–2495. https://doi.org/10.1109/TPAMI.2016.2644615
   PubMed Web of Science ®Google Scholar
• Baheti, B., Innani, S., Gajre, S., & Talbar, S. (2020). Semantic scene segmentation in unstructured environment with modified DeepLabV3+. Pattern Recognition Letters, 138, 223–229. https://doi.org/10.1016/j.patrec.2020.07.029
   Web of Science ®Google Scholar
• Bhagat, S., Kokare, M., Haswani, V., Hambarde, P., & Kamble, R. (2022). Eff-UNet++: A novel architecture for plant leaf segmentation and counting. Ecological Informatics, 68, 101583. https://doi.org/10.1016/j.ecoinf.2022.101583
   Web of Science ®Google Scholar
• Botev, Z. I., Kroese, D. P., Rubinstein, R. Y., & L’ecuyer, P. (2013). The cross-entropy method for optimization in handbook of statistics. Elsevier, 31, 35–59.
   Google Scholar
• Cai, M., Yi, X., Wang, G., Mo, L., Wu, P., Mwanza, C., & Kapula, K. E. (2022). Image segmentation method for sweetgum leaf spots based on an improved DeeplabV3+ network. Forests, 13(12), 2095. https://doi.org/10.3390/f13122095
   Web of Science ®Google Scholar
• Chen, L. C., Papandreou, G., Schroff, F., et al. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587.
   Google Scholar
• Cheng, Z., Cheng, Y., Li, M., Dong, X., Gong, S., & Min, X. (2023). Detection of cherry tree crown based on improved LA-dpv3+ algorithm. Forests, 14(12), 2404. https://doi.org/10.3390/f14122404
   Web of Science ®Google Scholar
• Das, S., Fime, A. A., Siddique, N., & Hashem, M. M. A. (2021). Estimation of road boundary for intelligent vehicles based on DeepLabV3+ architecture. IEEE Access, 9, 121060–121075. https://doi.org/10.1109/ACCESS.2021.3107353
   Web of Science ®Google Scholar
• Divyanth, L. G., Ahmad, A., & Saraswat, D. (2023). A two-stage deep-learning based segmentation model for crop disease quantification based on corn field imagery. Smart Agricultural Technology, 3, 100108. https://doi.org/10.1016/j.atech.2022.100108
   Google Scholar
• Dong, K., Zhou, C., Ruan, Y., et al. (2020). MobileNetV2 model for image classification [Paper presentation]. 2020 2nd International Conference on Information Technology and Computer Application (ITCA) (pp. 476–480). IEEE. https://doi.org/10.1109/ITCA52113.2020.00106
   Google Scholar
• Donghui, S., Xiuliang, J. U., Dengchao, F., et al. (2016). Face recognition from aggregated images based on FAST detector and SURF descriptor. Foreign Electronic Measurement Technology, 35(1), 94–98.
   Google Scholar
• Du, W., Rao, N., Dong, C., Wang, Y., Hu, D., Zhu, L., Zeng, B., & Gan, T. (2021). Automatic classification of esophageal disease in gastroscopic images using an efficient channel attention deep dense convolutional neural network. Biomedical Optics Express, 12(6), 3066–3081. https://doi.org/10.1364/BOE.420935
   PubMed Web of Science ®Google Scholar
• Fang, Y., Huang, H., Yang, W., Xu, X., Jiang, W., & Lai, X. (2022). Nonlocal convolutional block attention module VNet for gliomas automatic segmentation. International Journal of Imaging Systems and Technology, 32(2), 528–543. https://doi.org/10.1002/ima.22639
   Web of Science ®Google Scholar
• Huang, L. S., ShaO, S., Lu, X. J., et al. (2021). Segmentation and registration of lettuce multispectral image based on convolutional Neural Network. Transactions of the Chinese Society for Agricultural Machinery, 52(9), 186–194.
   Google Scholar
• Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7132–7141).
   Google Scholar
• Hu, Z., Zhao, J., Luo, Y., & Ou, J. (2022). Semantic SLAM based on improved DeepLabv3+ in dynamic scenarios. IEEE Access, 10, 21160–21168. https://doi.org/10.1109/ACCESS.2022.3154086
   Web of Science ®Google Scholar
• Jiaqi, R. U., Bin, W., Xiang, W., et al. (2023). Segmentation and classification of grape black rot lesion degree based on improved UNet++ model [J/OL]. Journal of Zhejiang Agricultural, 35(11), 2720–2730volctrans extension anchor for patchId=3423.
   Google Scholar
• Ji-Feng, L. (2022). Chest CT image segmentation method based on improved Snake model. Automation & Instrumentation, 32(11), 2059–2066.
   Google Scholar
• Jin, X., Xie, Y., Wei, X.-S., Zhao, B.-R., Chen, Z.-M., & Tan, X. (2022). Delving deep into spatial pooling for squeeze-and-excitation networks. Pattern Recognition, 121, 108159. https://doi.org/10.1016/j.patcog.2021.108159
   Web of Science ®Google Scholar
• Keskar, N. S., & Socher, R. (2017). Improving generalization performance by switching from adam to sgd. arXiv preprint arXiv:1712.07628.
   Google Scholar
• Lincai, H., Zhiwen, W. A. N. G., Xiaobiao, F., et al. (2022). Edge detection of gesture image: Canny algorithm based on multi-direction and optimal threshold. Journal of Guangxi University of Science and Technology, 33(1), 71–77.
   Google Scholar
• Mao, W. J., Ruan, J. Q., & Liu, S. (2023). Strawberry disease semantic segmentation based on improved UNet with attention mechanism. Computer Systems & Applications, 32(06), 251–259.
   Google Scholar
• Mattihalli, C., Gared, F., & Getnet, L. (2021). Automatic plant leaf disease detection and auto-medicine using IoT technology. In Krause P., & Xhafa F. IoT-based intelligent modelling for environmental and ecological engineering (pp. 257–273). Springer.
   Google Scholar
• Ono, H., Murakami, S., Kamiya, T., & Aoki, T., 12–15 October (2021). Automatic segmentation of finger bone regions from CR images using improved DeepLabv3+ [Paper presentation]. Proceedings of the 2021 21st International Conference on Control, Automation and Automation and Systems (ICCAS) (pp. 1788–1791), Jeju, Korea.
   Google Scholar
• Peng, H., Xue, C., Shao, Y., Chen, K., Xiong, J., Xie, Z., & Zhang, L. (2020). Semantic segmentation of litchi branches using DeepLabV3+ model. IEEE Access, 8, 164546–164555. https://doi.org/10.1109/ACCESS.2020.3021739
   Web of Science ®Google Scholar
• Sandler, M., Howard, A., Zhu, M., et al. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4510–4520).
   Google Scholar
• Semma, A., Hannad, Y., Siddiqi, I., Djeddi, C., & El Youssfi El Kettani, M. (2021). Writer identification using deep learning with FAST keypoints and Harris corner detector. Expert Systems with Applications, 184, 115473. https://doi.org/10.1016/j.eswa.2021.115473
   Web of Science ®Google Scholar
• Tiwari, D., Ashish, M., Gangwar, N., et al. 4th International Potato leaf Diseases Detection using Deep Learning. (2020).
   Google Scholar
• Tiwari, D., Ashish, M., Gangwar, N., et al. (2020). Potato leaf diseases detection using deep learning [Paper presentation]. 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 461–466). IEEE. https://doi.org/10.1109/ICICCS48265.2020.9121067
   Google Scholar
• Wang, J. P. (2020). Main pear diseases and their control methods. Modern Horticulture, 43(07), 194–195.
   Google Scholar
• Wang, H., Ding, J., He, S., Feng, C., Zhang, C., Fan, G., Wu, Y., & Zhang, Y. (2023). MFBP-UNet: A network for pear leaf disease segmentation in natural agricultural environments. Plants, 12(18), 3209. https://doi.org/10.3390/plants12183209
   Google Scholar
• Wang, Q., Wu, B., Zhu, P., et al. (2020). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11534–11542).
   Google Scholar
• Wenbo, L., Tao, Y., & Qi, L. (2021). Tomato leaf disease detection method based on improved SOLO v2. Transactions of the Chinese Society for Agricultural Machinery, 52(8), 213–220.
   Google Scholar
• Woo, S., Park, J., Lee, J. Y., et al. (2018). Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV) (pp. 3–19).
   Google Scholar
• Xue, X., Luo, Q., Bu, M., Li, Z., Lyu, S., & Song, S. (2023). Citrus tree canopy segmentation of orchard spraying robot based on RGB-D image and the improved DeepLabv3+. Agronomy, 13(8), 2059. https://doi.org/10.3390/agronomy13082059
   Google Scholar
• Yang, K., Zhong, W., & Li, F. (2020). Leaf segmentation and classification with a complicated background using deep learning. Agronomy, 10(11), 1721. https://doi.org/10.3390/agronomy10111721
   Google Scholar
• Yanjun, H., Ting, Z., & Wu, X. J. (2021). Occurrence and prevention of several common pear diseases. Modern Rural Science and Technology, 52(9), 186–194.
   Google Scholar
• Yu, L., Zeng, Z., Liu, A., Xie, X., Wang, H., Xu, F., & Hong, W. (2022). A lightweight complex-valued DeepLabv3+ for semantic segmentation of PolSAR image. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 930–943. https://doi.org/10.1109/JSTARS.2021.3140101
   Web of Science ®Google Scholar
• Yuan, H., Zhu, J., Wang, Q., Cheng, M., & Cai, Z. (2022). An improved DeepLab v3+ deep learning network applied to the segmentation of grape leaf black rot spots. Frontiers in Plant Science, 13, 795410. https://doi.org/10.3389/fpls.2022.795410
   PubMed Web of Science ®Google Scholar
• Yushu, D., Xiao-Chun, Z., Chengming, S., Jun, Y., Tao, L., & Liu, S. P. (2021). Identification of wheat scab based on image processing and Deeplabv3+ model. Chinese Journal of Agricultural Mechanization, 42(09), 209–215.
   Google Scholar
• Zhang, Y., Li, K., Li, K., et al. (2018). Image super-resolution using very deep residual channel attention networks. Proceedings of the European Conference on Computer Vision (ECCV) (pp. 286–301).
   Google Scholar
• Zhang, Y., Wu, M., Li, J., Yang, S., Zheng, L., Liu, X., & Wang, M. (2023). Automatic non-destructive multiple lettuce traits prediction based on DeepLabV3. Journal of Food Measurement and Characterization, 17(1), 636–652. https://doi.org/10.1007/s11694-022-01660-3
   Web of Science ®Google Scholar
• Zhao, W., Zhang, H., Yan, Y., Fu, Y., & Wang, H. (2018). A semantic segmentation algorithm using FCN with combination of BSLIC. Applied Sciences, 8(4), 500. https://doi.org/10.3390/app8040500
   Google Scholar
• Zhong, W., Xinlei, L., Kunlong, Y., et al. (2020). Segmentation and recognition of multi-target blades under complex background based on Mask-RCNN. Acta Agriculturae Zhejiangensis, 32(11), 2059–2066.
   Google Scholar
• Zhongyuan, W., Ruimin, H., & Kai, Z. (2004). An implementation of region partition or connected component label split-and-merge method. Small Microcomputer System, 25(9), 1648–1651.
   Google Scholar