Skip to main content
SpringerLink
Log in

Leaf disease recognition based on channel information attention network

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Aiming at the problem of the variety of plant leaf diseases and how to extract effective features, an attention network model fused with channel information is proposed to identify a variety of plant leaf diseases. Firstly, a residual structure based basic network is built for feature extraction, and in order to extract effective information, the feature is re-calibrated by integrating multiple channel information through the attention network. Then, the constraint information is added into the cross entropy function to accelerate the convergence of the model. Finally, the model is tested on the data sets of 16 diseases of four different plants. The results show that the recognition accuracy of the basic network model is 83.13%, while the accuracy increased by 4.64% after fusing the channel information network. Compared with other models, the fusion model improves the recognition accuracy by 9.72% and the model complexity is less than twice that of the optimal model in the comparison experiment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Availability of data and materials

The data sets supporting the results of this article are included within the article.

Code availability

Correspondence and requests for code should be addressed to H.D.

References

  1. Al Bashish D, Braik M, Bani-Ahmad S (2011) Detection and classification of leaf diseases using k-means-based segmentation and neural-networks-based classification. Inf Technol J 10(2):267–275. https://doi.org/10.3923/itj.2011.267.275

    Article  Google Scholar 

  2. Arivazhagan S, Newlin Shebiah R, Ananthi S et al (2013) Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric Eng Int: The CIGR Journal 15(1):211–217

    Google Scholar 

  3. Ashwini K, Vincent P M D R, Srinivasan K, Chang C -Y (2021) Deep learning assisted neonatal cry classification via support vector machine models. Front Public Health 670352:9. https://doi.org/10.3389/fpubh.2021.670352

    Google Scholar 

  4. Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: ICLR 2015: international conference on learning representations

  5. Chaudhari S, Polatkan G, Ramanath R et al (2019) An attentive survey of attention models. arXiv:1904.02874

  6. Chorowski J, Bahdanau D, Serdyuk D et al (2015) Attention-based models for speech recognition. In: NIPS’15 proceedings of the 28th international conference on neural information processing systems—vol 1, 28, pp 577–585

  7. Cui Y, Chen Z, Wei S, et al. (2017) Attention-over-attention neural networks for reading comprehension. In: Proceedings of the 55th annual meeting of the association for computational linguistics, vol 1, pp 593–602. https://doi.org/10.18653/V1/P17-1055

  8. Fang C C, Shi F (2020) Tomato disease image recognition based on improved depth residual network. Comput Appl 40(S1):203–208

    Google Scholar 

  9. Ferentinos K P (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318. https://doi.org/10.1016/J.COMPAG.2018.01.009

    Article  Google Scholar 

  10. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778. https://doi.org/10.1109/CVPR.2016.90

  11. He X, Li S H, Liu B (2020) Identification of grape leaf disease based on multi-scale residual neural network. Comput Eng 1000–3428

  12. Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. CoRR. arXiv:2103.02907

  13. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation Networks. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, vol 42(8), pp 2011–2023. https://doi.org/10.1109/CVPR.2018.00745

  14. Ji M, Zhang L, Wu Q (2020) Automatic grape leaf diseases identification via united model based on multiple convolutional neural networks. Inf Process Agric 7(3):418–426. https://doi.org/10.1016/J.INPA.2019.10.003

    Google Scholar 

  15. Joseph I A, Khan M A, Luo H (2019) Orange leaf diseases identification using digital image processing. In: International symposium on intelligence computation and applications, pp 360–378. https://doi.org/10.1109/ICIIECS.2017.8275951

  16. Karthik R, Hariharan M, Anand S, Mathikshara P, Johnson A, Menaka R (2020) Attention embedded residual CNN for disease detection in tomato leaves. Appl Soft Comput 86:105933. https://doi.org/10.1016/j.asoc.2019.105933

    Article  Google Scholar 

  17. Kim S, Hori T, Watanabe S (2017) Joint CTC-attention based end-to-end speech recognition using multi-task learning. In: 2017 IEEE International conference on acoustics, speech and signal processing (ICASSP), pp 4835–4839. https://doi.org/10.1109/ICASSP.2017.7953075

  18. Krizhevsky A, Sutskever I, Hinton G E (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90. https://doi.org/10.1145/3065386

    Article  Google Scholar 

  19. LeCun Y, Boser B, Denker J S, et al. (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541–551. https://doi.org/10.1162/NECO.1989.1.4.541

    Article  Google Scholar 

  20. Lee S H, Goëau H., Bonnet P, Joly A (2020) Attention-based recurrent neural network for plant disease classification. Front Plant Sci 11:1897. https://doi.org/10.3389/fpls.2020.601250

    Article  Google Scholar 

  21. Li J (2015) Research on automatic identification of tobacco diseases based on convolution neural network. Shandong Agricultural University, Taiwan

    Google Scholar 

  22. Li G, Ma Z, Wang H (2011) Image recognition of grape downy mildew and grape powdery mildew based on support vector machine. In: International conference on computer and computing technologies in agriculture. Springer Science and Business Media, LLC, pp 151–162. https://doi.org/10.1007/978-3-642-27275-2_17

  23. Liu Y, Shao Z, Teng Y et al (2021) NAM: normalization-based attention module. CoRR, arXiv:2111.12419

  24. Lv M, Zhou G, He M, Chen A, Zhang W, Hu Y (2020) Maize leaf disease identification based on feature enhancement and DMS-robust Alexnet. IEEE Access 8:57952–57966. https://doi.org/10.1109/ACCESS.2020.2982443

    Article  Google Scholar 

  25. Lu Y, Yi S, Zeng N et al (2017) Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267:378–384. https://doi.org/10.1016/J.NEUCOM.2017.06.023

    Article  Google Scholar 

  26. Ma W, Cui Y, Shao N et al (2019) TripleNet: triple attention network for multi-turn response selection in retrieval-based chatbots. In: Proceedings of the 23rd conference on computational natural language learning (CoNLL). https://doi.org/10.18653/v1/k19-1069

  27. Mnih V, Heess N, Graves A, Kavukcuoglu K (2014) Recurrent models of visual attention. In: Advances in neural information processing systems, vol 27, pp 2204–2212

  28. Mokhtar U, El-Bendary N, Hassenian AE et al (2015) SVM-based detection of tomato leaves diseases. Adv Intell Syst Comput 323:641–652. https://doi.org/10.1007/978-3-319-11310-4_55

    Article  Google Scholar 

  29. Nandhini Abirami R, Durai Raj Vincent P M, Srinivasan K, et al. (2021) Deep CNN and deep GAN in computational visual perception-driven image analysis. Complexity 2021:30. https://doi.org/10.1155/2021/5541134

    Article  Google Scholar 

  30. Pallagani V, Khandelwal V, Chandra B et al (2019) DCRop: a deep-learning based framework for accurate prediction of diseases of crops in smart agriculture. In: 2019 IEEE international symposium on smart electronic systems (iSES) (formerly inis), pp 29–33. https://doi.org/10.1109/iSES47678.2019.00020

  31. Patil J K, Kumar R (2017) Analysis of content based image retrieval for plant leaf diseases using color, shape and texture features. Eng Agric Environ Food 10(2):69–78. https://doi.org/10.1016/j.eaef.2016.11.004

    Article  Google Scholar 

  32. Rangarajan A K, Purushothaman R, Ramesh A (2018) Tomato crop disease classification using pre-trained deep learning algorithm. Procedia Comput Sci 133:1040–1047. https://doi.org/10.1016/J.PROCS.2018.07.070

    Article  Google Scholar 

  33. Saleem M H, Potgieter J, Arif K M (2020) Plant disease classification: a comparative evaluation of convolutional neural networks and deep learning optimizers. Plants 9(10):1319. https://doi.org/10.3390/plants9101319

    Article  Google Scholar 

  34. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: ICLR 2015 international conference on learning representations

  35. Singh V, Misra A K (2017) Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf Process Agric 4(1):41–49. https://doi.org/10.1016/j.inpa.2016.10.005

    Google Scholar 

  36. Srinivasan K, Garg L, Datta D, Alaboudi A A, Jhanjhi N Z et al (2021) Performance comparison of deep cnn models for detecting driver’s distraction. Comput Mater Contin 68(3):4109–4124

    Google Scholar 

  37. Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need. In: Proceedings of the 31st international conference on neural information processing systems, vol 30, pp 5998–6008

  38. Woo S, Park J, Lee J Y, Kweon I S (2018) CBAM: convolutional block attention module. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer vision—ECCV 2018. ECCV 2018. Lecture notes in computer science, vol 11211. Springer, Cham. https://doi.org/10.1007/978-3-030-01234-2_1

  39. Zhao B, Wu X, Feng J et al (2017) Diversified visual attention networks for fine-grained object classification. IEEE Trans Multimed 19(6):1245–1256. https://doi.org/10.1109/TMM.2017.2648498

    Article  Google Scholar 

  40. Zhou B, Khosla A, Lapedriza A et al (2016) Learning deep features for discriminative localization. In: CVPR. IEEE Computer Society. https://doi.org/10.1109/CVPR.2016.319

Download references

Acknowledgments

This study was supported by research grants from the National Natural Science Foundation of China(61873178,61976150), Key Research and Development Projects of Shanxi Province(201803D31038), Key R & D projects in Jinzhong City, China grant number Y192006, the Central Guidance on Local Science and Technology Development Fund of Shanxi. Shanxi Provincial Central Guide local Science and technology development fund project (YDZJSX2021C005,YDZJSX2022A016). The open project of CAD & CG State Key Laboratory of Zhejiang University in 2022 (A2221).

Funding

This study was supported by research grants from the National Natural Science Foundation of China(61873178,61976150), Key Research and Development Projects of Shanxi Province(201803D31038), Key R & D projects in Jinzhong City, China grant number Y192006, the Central Guidance on Local Science and Technology Development Fund of Shanxi. Shanxi Provincial Central Guide local Science and technology development fund project (YDZJSX2021C005,YDZJSX2022A016). The open project of CAD & CG State Key Laboratory of Zhejiang University in 2022 (A2221).

Author information

Author notes

    Authors and Affiliations

    1. Department of Information and Computer, Taiyuan University of Technology, No.209, University Street, Jinzhon, 030600, China

      Hongxia Deng, Dongsheng Luo, Jinxiu Hou, Guanyu Qian & Haifang Li

    2. Department of Software, Taiyuan University of Technology, No.209, University Street, Jinzhon, 030600, China

      Zijing Zhou

    Authors
    1. Hongxia Deng
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Dongsheng Luo
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Zijing Zhou
      View author publications

      You can also search for this author in PubMed Google Scholar

    4. Jinxiu Hou
      View author publications

      You can also search for this author in PubMed Google Scholar

    5. Guanyu Qian
      View author publications

      You can also search for this author in PubMed Google Scholar

    6. Haifang Li
      View author publications

      You can also search for this author in PubMed Google Scholar

    Contributions

    All authors wrote and reviewed the manuscript text. H.D.conceived the experiment, implemented and tested the proposed model. Z.Z. and J.H. designed the disease recognition experiments, wrote all programs. J.H.and D.L. prepared all data and figures, performed the disease recognition, and drafted the manuscript. G.Q.analysed the results. H.L. contributed to analyses and discussion.

    Corresponding author

    Correspondence to Hongxia Deng.

    Ethics declarations

    Conflict of interest

    The authors declare that they have no conflict of interest.

    Ethics approval and consent to participate

    Not applicable.

    Consent for publication

    Not applicable.

    Additional information

    Publisher’s note

    Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

    Dongsheng Luo, Zijing Zhou, Jinxiu Hou, Guanyu Qian and Haifang Li contributed equally to this work.

    Rights and permissions

    Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

    Reprints and permissions

    About this article

    Check for updates. Verify currency and authenticity via CrossMark

    Cite this article

    Deng, H., Luo, D., Zhou, Z. et al. Leaf disease recognition based on channel information attention network. Multimed Tools Appl 83, 6601–6619 (2024). https://doi.org/10.1007/s11042-023-15512-9

    Download citation

    • Received:

    • Revised:

    • Accepted:

    • Published:

    • Issue Date:

    • DOI: https://doi.org/10.1007/s11042-023-15512-9

    Keywords

    Search

    Navigation