Lightweight Plant Disease Classification Combining GrabCut Algorithm, New Coordinate Attention, and Channel Pruning | Neural Processing Letters Skip to main content
Springer Nature Link
Log in

Lightweight Plant Disease Classification Combining GrabCut Algorithm, New Coordinate Attention, and Channel Pruning

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

In this paper, a lightweight convolutional neural network is proposed for the classification of plant diseases, containing 63 classes of states for 11 plant species. The different context of experimental data and data in the real environment, insufficient accuracy of the model classification, and oversized model are three main problems of deep learning techniques applied to agricultural production. In this paper, we mainly focus on these three problems. First, the GrabCut algorithm is adopted to unify the background of the experimental data and the real data to black, allowing the trained model to have the same good effect when applied in practice. Then, we propose a new coordinate attention block to improve the classification accuracy of convolutional neural networks and empirically demonstrate the effectiveness of our approach with several state-of-the-art CNN models. Finally, channel pruning is applied to the trained model, which reduces the model size and computational effort by 85.19\(\%\) and 92.15\(\%\) respectively with little change in the model accuracy, making it better suited for agricultural platforms with lower memory and computational capacity.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Savary S, Willocquet L, Pethybridge SJ, Esker P, McRoberts N, Nelson A (2019) The global burden of pathogens and pests on major food crops. Nat Ecol Evol 3(3):430–439

    Article  Google Scholar 

  2. Shrivastava VK, Pradhan MK (2020) Rice plant disease classification using color features: a machine learning paradigm. J Plant Pathol 103:17

    Article  Google Scholar 

  3. Xie C, Yong H (2016) Spectrum and image texture features analysis for early blight disease detection on eggplant leaves. Sensors 16(5):676

    Article  Google Scholar 

  4. Yang X, Zhang R, Zhai Z, Pang Y, Jin Z (2019) Machine learning for cultivar classification of apricots (Prunus armeniaca l.) based on shape features: sciencedirect. Sci Hortic 256:108524–108524

    Article  Google Scholar 

  5. Fang W, Ding YA, Zhang FA, Sheng V (2019) Dog: a new background removal for object recognition from images. Neurocomputing 361:85–91

    Article  Google Scholar 

  6. Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419. https://doi.org/10.3389/fpls.2016.01419

    Article  Google Scholar 

  7. Hang J, Zhang D, Chen P, Zhang J, Wang B (2019) Classification of plant leaf diseases based on improved convolutional neural network. Sensors. https://doi.org/10.3390/s19194161

    Article  Google Scholar 

  8. Yan Q, Yang B, Wang W, Wang B, Chen P, Zhang J (2020) Apple leaf diseases recognition based on an improved convolutional neural network. Sensors. https://doi.org/10.3390/s20123535

    Article  Google Scholar 

  9. Khatkar BS, Chaudhary N, Dangi P (2016) Production and consumption of grains: India

  10. Rother C (2004) Grabcut : interactive foreground extraction using iterated graph cuts. Proc Siggraph 23:3

    Google Scholar 

  11. Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp 13713–13722

  12. Zhuang L, Li J, Shen Z, Gao H, Zhang C (2017) Learning efficient convolutional networks through network slimming. In: 2017 IEEE international conference on computer vision (ICCV)

  13. Sardogan M, Tuncer A, Ozen Y (2018) Plant leaf disease detection and classification based on cnn with lvq algorithm. In: 2018 3rd international conference on computer science and engineering (UBMK)

  14. Mahmoud F, Haines D, Al-Ozairi E, Dashti A (2016) Effect of black tea consumption on intracellular cytokines, regulatory t cells and metabolic biomarkers in type 2 diabetes patients. Phytother Res 30(3):454–462

    Article  Google Scholar 

  15. Saric S, Notay M, Sivamani RK (2017) Green tea and other tea polyphenols: effects on sebum production and acne vulgaris. Antioxidants 6(1):2

    Article  Google Scholar 

  16. Tangney CC, Rasmussen HE (2013) Polyphenols, inflammation, and cardiovascular disease. Curr Atheroscler Rep 15(5):324

    Article  Google Scholar 

  17. Alshatwi AA, Al Obaaid MA, Al Sedairy SA, Ramesh E, Lei KY (2011) Black and green tea improves lipid profile and lipid peroxidation parameters in wistar rats fed a high-cholesterol diet. J Physiol Biochem 67(1):95–104

    Article  Google Scholar 

  18. Shang X, Song M, Yu C (2019) Hyperspectral image classification with background. In: IGARSS 2019-2019 IEEE international geoscience and remote sensing symposium, 2714–2717 .IEEE

  19. Boykov Y, Kolmogorov V (2003) Computing geodesics and minimal surface via graph cuts. IEEE

  20. Chen L.C, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation

  21. Douillard A, Chen Y, Dapogny A, Cord M (2020) Plop: learning without forgetting for continual semantic segmentation

  22. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

  23. Zagoruyko S, Komodakis N (2016) Wide residual networks. arXiv preprint arXiv:1605.07146

  24. Xie S, Girshick R, Dollár P, Tu Z, He K.(2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 1492–1500

  25. Cao Y, Xu J, Lin S, Wei F, Hu H (2019) Gcnet: Non-local networks meet squeeze-excitation networks and beyond. In: Proceedings of the IEEE/CVF international conference on computer vision workshops, 0–0

  26. Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 3146–3154

  27. Hu H, Gu J, Zhang Z, Dai J, Wei Y (2018) Relation networks for object detection. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, 3588–3597

  28. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, 7132–7141

  29. Woo S, Park J, Lee JY, Kweon IS (2018) Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV). pp 3–19

  30. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 770–778

  31. Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning, 6105–6114. PMLR

  32. Ma N, Zhang X, Zheng H-T, Sun J (2018) Shufflenet v2: practical guidelines for efficient cnn architecture design. In: Proceedings of the European conference on computer vision (ECCV). pp 116–131

  33. Howard A, Sandler M, Chu G, Chen L.-C, Chen B, Tan M, Wang W, Zhu Y, Pang R, Vasudevan V, et al (2019) Searching for mobilenetv3. In: Proceedings of the IEEE/CVF international conference on computer vision. pp 1314–1324

  34. Han K, Wang Y, Tian Q, Guo J, Xu C, Xu C (2020) Ghostnet: more features from cheap operations. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp 1580–1589

  35. Han S, Pool J, Tran J, Dally WJ (2015) Learning both weights and connections for efficient neural networks

  36. Wen W, Wu C, Wang Y, Chen Y, Li H (20196) Learning structured sparsity in deep neural networks

  37. Kumar A, Shaikh AM, Li Y, Bilal H, Yin B (2021) Pruning filters with l1-norm and capped l1-norm for cnn compression. Appl Intell 51:1152–1160

    Article  Google Scholar 

  38. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Bach F, Blei, D. (eds.), Proceedings of the 32nd international conference on machine learning. Proceedings of machine learning research, 37: 448–456. PMLR, Lille, France. http://proceedings.mlr.press/v37/ioffe15.html

Download references

Acknowledgements

This work is supported by China Agriculture Research System of MOF and MARA, the Project of Scientific and Technological Innovation Planning of Hunan Province (2020NK2008), Hunan Province Modern Agriculture Technology System for Tea Industry, the National Natural Science Foundation of China (42130716). We are grateful to the High Performance Computing Center of Central South University for partial support of this work.

Author information

Authors and Affiliations

  1. School of Computer Science, Central South University, Lushannan, Changsha, 410083, Hunan, China

    Fang Qi, Yongle Wang & Zhe Tang

Authors
  1. Fang Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yongle Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhe Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhe Tang.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qi, F., Wang, Y. & Tang, Z. Lightweight Plant Disease Classification Combining GrabCut Algorithm, New Coordinate Attention, and Channel Pruning. Neural Process Lett 54, 5317–5331 (2022). https://doi.org/10.1007/s11063-022-10863-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-022-10863-0

Keywords

Search

Navigation