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A grape disease identification and severity estimation system

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Abstract

It is an important research of automatically identifying the type and severity level of crop diseases, which affects food production, disease control, and economic loss prediction. Deep learning-based methods have achieved successful application for disease identification. However, the method of only using a single network model to identify crop disease is simple and limited since networks vary in complexity and architecture. Furthermore, to measure the effectiveness of the measures taken, precisely quantifying the level of severity is necessary, instead of simply dividing the level into serious or not. In response to these problems, a novel system of disease identification and severity estimation is proposed in this paper. Firstly, an ensemble learning model is proposed to recognize crop diseases, which consider the comprehensive outputs of ResNet50, Inceptionv3, and DenseNet121. The relative majority voting is used as the ensemble classifier to obtain the final identification result. Second, a pixel-level segmentation model is proposed to precisely estimate disease severity after the disease is identified, which can track small changes in infected areas and obtain fine-grained resolutions. DeepLab architecture, encoder-decoder, and dilated convolution are integrated into the proposed segmentation model. Furthermore, an attention mechanism is applied to DeepLabv3+ to improve the performance of severity estimation, which can give more weight to disease areas to achieve more precise segmentation. A public grape disease database is used to verify the performance of the proposed system. Experimental results show that compared with an individual model and other existing studies, the proposed disease identification model has stronger robustness and higher identification accuracy. Furthermore, the proposed severity estimation model also achieves a lower severity error and a higher classification accuracy than baseline models of DeepLabv3+, PspNet, and SegNet.

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References

  1. Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495

    Article  Google Scholar 

  2. Bock CH, Poole GH, Parker PE, Gottwald TR (2010) Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. CRC Crit Rev Plant Sci 29(2):59–107

    Article  Google Scholar 

  3. Cruz A et al (2019) Detection of grapevine yellows symptoms in Vitis vinifera L. with artificial intelligence. Comput Electron Agric 157(11):63–76

    Article  Google Scholar 

  4. Esgario JGM, Krohling RA, Ventura JA (2020) Deep learning for classification and severity estimation of coffee leaf biotic stress. Comput Electron Agric 169(1):1–9

    Google Scholar 

  5. Fernández-Díaz M, Gallardo-Antolín A (2020) An attention long short-term memory based system for automatic classification of speech intelligibility. Eng Appl Artif Intell 96(8):1–8

    Google Scholar 

  6. Hu G, Wang H, Zhang Y, Wan M (2021) Detection and severity analysis of tea leaf blight based on deep learning. Comput Electr Eng 90(1):1–15

    Google Scholar 

  7. Hughes DP, Salathe M (2015) An open access repository of images on plant health to enable the development of mobile disease diagnostics. ar**v, pp 1–13

  8. Jadhav SB (2019) Convolutional neural networks for leaf image-based plant disease classification. IAES Int J Artif Intell 8(4):328–341

    Google Scholar 

  9. Kim TH, Sajjadi MSM, Hirsch M, Sch B (2018) Encoder-Decoder with atrous separable convolution for semantic image segmentation. In: European Conference on Computer Vision(ECCV), pp 111–127

  10. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. In: In 3rd international conference on learning representations, pp 1–15

  11. Kranz J (1988) Measuring plant disease. In: Experimental techniques in plant disease epidemiology. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 35–50

  12. Kumar A, Kim J, Lyndon D, Fulham M, Feng D (2017) An ensemble of fine-tuned convolutional neural networks for medical image classification. IEEE J Biomed Health Inform 21(1):31–40

    Article  Google Scholar 

  13. Liang Q, **ang S, Hu Y, Coppola G, Zhang D, Sun W (2019) PD 2 SE-Net: computer-assisted plant disease diagnosis and severity estimation network. Comput Electron Agric 157(12):518–529

    Article  Google Scholar 

  14. Liang X, B JL, Luo Y, Wu G (2020) Automatic segmentation and diagnosis of intervertebral discs based on deep neural networks. In: International Conference on Neural Information Processing (ICONIP), pp 168–175

  15. Liu B, Ding Z, Tian L, He D, Li S, Wang H (2020) Grape leaf disease identification using improved deep convolutional neural networks. Front Plant Sci 11(7):1–14

    Google Scholar 

  16. Liu J, Li M, Luo Y, Yang S, Li W, Bi Y (2021) Alzheimer’s disease detection using depthwise separable convolutional neural networks. Comput Methods Programs Biomed 203(5):1–10

    Google Scholar 

  17. Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). Springer International Publishing, Cham, pp 234–241

  18. Shen J, Robertson N (2021) Towards large scale effective ensemble adversarial attacks against deep neural network learning. Inf Sci (Ny) 569(4):469–478

    Article  Google Scholar 

  19. Shi Y, Wei Z, Ling H, Wang Z, Shen J, Li P (2021) Person retrieval in surveillance videos via deep attribute mining and reasoning. IEEE Trans Multimed 23(4):4376–4387

    Article  Google Scholar 

  20. Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D (2016) Deep neural networks based recognition of plant diseases by leaf image classification. Comput Intell Neurosci 2016(1):1–11

    Article  Google Scholar 

  21. Tang Z, Yang J, Li Z, Qi F (2020) Grape disease image classification based on lightweight convolution neural networks and channelwise attention. Comput Electron Agric 178(10):1–9

    Google Scholar 

  22. Waghmare H, Kokare R, Dandawate Y (2016) Detection and classification of diseases of grape plant using opposite colour local binary pattern feature and machine learning for automated decision support system. In: 3rd International Conference on Signal Processing and Integrated Networks (SPIN), pp 513–518

  23. Wang G, Sun Y, Wang J (2017) Automatic image-based plant disease severity estimation using deep learning. Comput Intell Neurosci 2017(1):1–8

    MathSciNet  Google Scholar 

  24. Wang L, Qian X, Zhang Y, Shen J, Cao X (2020) Enhancing sketch-based image retrieval by CNN semantic re-ranking. IEEE Trans Cybern 50(7):3330–3342

    Article  Google Scholar 

  25. Wang C, Du P, Wu H, Li J, Zhao C, Zhu H (2021) A cucumber leaf disease severity classification method based on the fusion of DeepLabV3+ and U-Net. Comput Electron Agric 189(4):1–13

  26. Woo S, Park J, Lee J-Y, Kweon IS (2018) CBAM: convolutional block attention module. In: European Conference on Computer Vision (ECCV), pp 3–19

  27. Wu Y, Wu J, Hu G (2020) MMFS:a grape disease recognition method based on multi-feature fusion and SVM. In: Proceedings of the 2020 4th International Conference on Cloud and Big Data Computing, pp 27–31

  28. Zeng W, Li M (2020) Crop leaf disease recognition based on self-attention convolutional neural network. Comput Electron Agric 172(3):1–7

    MathSciNet  Google Scholar 

  29. Zeng Q, Ma X, Cheng B, Zhou E, Pang W (2020) GANs-based data augmentation for citrus disease severity detection using deep learning. IEEE Access 8(9):172882–172891

    Article  Google Scholar 

  30. Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 6230–6239

  31. Zhu J, Wu A, Wang X, Zhang H (2020) Identification of grape diseases using image analysis and BP neural networks. Multimed Tools Appl 79:21–22

    Article  Google Scholar 

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Acknowledgements

This research was partially supported by the National Natural Science Foundation of China under Grant 61976063, the Guangxi Natural Science Foundation under Grant 2022GXNSFFA035028, research fund of Guangxi Normal University under Grant 2021JC006, the AI+Education research project of Guangxi Humanities Society Science Development Research Center under Grant ZXZJ202205, and the Innovation Project of Guangxi Graduate Education under Grant YCSW2021093.

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Authors and Affiliations

  1. Guangxi Key Lab of Brain-inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, China

    Hai** Shu, Junxiu Liu, Yifan Hua, Jian Chen, Shunsheng Zhang, Min Su & Yuling Luo

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  1. Hai** Shu
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  2. Junxiu Liu
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  3. Yifan Hua
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  4. Jian Chen
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  5. Shunsheng Zhang
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  6. Min Su
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  7. Yuling Luo
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Correspondence to Junxiu Liu.

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Shu, H., Liu, J., Hua, Y. et al. A grape disease identification and severity estimation system. Multimed Tools Appl 82, 23655–23672 (2023). https://doi.org/10.1007/s11042-023-14755-w

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