Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

A cognitive vision method for the detection of plant disease images

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Food security, which has currently attracted much attention, requires minimizing crop damage by timely detection of plant diseases. Therefore, the automatic identification and diagnosis of plant diseases are highly desired in agricultural information. In this paper, we propose a novel approach to identify plant diseases. The method is divided into two parts: starting with the enhancement of the artificial neural network, the extracted pixel values and feature values are input to the enhanced artificial neural network for the image segmentation; then, following the establishment of a CNN based model, the segmented images are input to the proposed CNN model for the image classification. The proposed approach shows an impressive performance in the experimental analyses. It achieved an average accuracy of 93.75% to identify the crop diseases under the complex background conditions, and the validation accuracy was, on average, 10% higher than that of the conventional method. Additionally, almost all the plant disease samples were correctly detected by the proposed approach, and thus the recall rate achieved 100%. The experimental finding presents a substantial performance relative to other state-of-the-art methods and demonstrates the efficiency and extensibility of the proposed approach.

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
$34.99 /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 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
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Faithpraise, F., et al.: Automatic plant pest detection and recognition using k-means clustering algorithm and correspondence filters. Int. J. Adv. Biotechnol. Res. 4(2), 189–199 (2013)

    Google Scholar 

  2. Al-Hiary, H., et al.: Fast and accurate detection and classification of plant diseases. Int. J. Comput. Appl. 17(1), 31–38 (2011)

    Google Scholar 

  3. Ding, W., Taylor, G.: Automatic moth detection from trap images for pest management. Comput. Electron. Agric. 123, 17–28 (2016)

    Article  Google Scholar 

  4. Wells, W.M., III.: Medical image analysis–past, present, and future. Elsevier, Amsterdam (2016)

    Book  Google Scholar 

  5. Devi, T.G., Neelamegam, P.: Image processing based rice plant leaves diseases in Thanjavur, Tamilnadu. Clust. Comput. 22(6), 13415–13428 (2019)

    Article  Google Scholar 

  6. Bera, T., et al.: A survey on rice plant disease identification using image processing and data mining techniques. Emerging technologies in data mining and information security, pp. 365–376. Springer, Singapore (2019)

    Book  Google Scholar 

  7. Zhang, X., et al.: Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access 6, 30370–30377 (2018)

    Article  Google Scholar 

  8. Ebrahimi, M.A., et al.: Vision-based pest detection based on SVM classification method. Comput. Electron. Agric. 137, 52–58 (2017)

    Article  Google Scholar 

  9. García, J., Pope, C., Altimiras, F.: A distributed-means segmentation algorithm applied to lobesia botrana recognition. Complexity 2017, 3 (2017)

    Article  Google Scholar 

  10. Guettari, N., Capelle-Laizé, A.S., Carré, P.: Blind image steganalysis based on evidential k-nearest neighbors. 2016 IEEE International Conference on Image Processing (ICIP). IEEE (2016)

  11. Deepa, S., Umarani, R.: Steganalysis on images using SVM with Selected hybrid features of Gini index feature selection algorithm. Int. J. Adv. Res. Comput. Sci. 8(5), 1503–1509 (2017)

    Google Scholar 

  12. Ramezani, M., Ghaemmaghami, S.: Towards genetic feature selection in image steganalysis. 2010 7th IEEE Consumer Communications and Networking Conference. IEEE (2010)

  13. Sheikhan, M., Pezhmanpour, M., Moin, M.S.: Improved contourlet-based steganalysis using binary particle swarm optimization and radial basis neural networks. Neural Comput. Appli. 21(7), 1717–1728 (2012)

    Article  Google Scholar 

  14. Kodovsky, J., Fridrich, J., Holub, V.: Ensemble classifiers for steganalysis of digital media. IEEE Trans. Inf. Forensics Secur. 7(2), 432–444 (2011)

    Article  Google Scholar 

  15. Zhang, F., et al.: Hierarchical feature learning with dropout k-means for hyperspectral image classification. Neurocomputing 187, 75–82 (2016)

    Article  Google Scholar 

  16. Wang, X.-F., Huang, D.-S.: A novel density-based clustering framework by using level set method. IEEE Trans. Knowl. Data Eng. 21(11), 1515–1531 (2009)

    Article  Google Scholar 

  17. Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 13(8), 841–847 (1991)

    Article  Google Scholar 

  18. Barbedo, J.G.A.: Plant disease identification from individual lesions and spots using deep learning. Biosyst. Eng. 180, 96–107 (2019)

    Article  Google Scholar 

  19. Barbedo, J.G.A.: Factors influencing the use of deep learning for plant disease recognition. Biosyst. Eng. 172, 84–91 (2018)

    Article  Google Scholar 

  20. Hu, Y., Mingqi, Lu., Xiaobo, Lu.: Driving behaviour recognition from still images by using multi-stream fusion CNN. Mach. Vis. Appl. 30(5), 851–865 (2019)

    Article  Google Scholar 

  21. Kamilaris, A., Prenafeta-Boldú, F.X.: Deep learning in agriculture: A survey. Comput. Electron. Agric. 147, 70–90 (2018)

    Article  Google Scholar 

  22. Kussul, N., et al.: Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci. Remote Sens. Lett. 14(5), 778–782 (2017)

    Article  Google Scholar 

  23. Yalcin, H.: Plant phenology recognition using deep learning: Deep-Pheno. 2017 6th International Conference on Agro-Geoinformatics. IEEE (2017)

  24. Huang, H.-W., Li, Q.-T., Zhang, D.-M.: Deep learning based image recognition for crack and leakage defects of metro shield tunnel. Tunn. Undergr. Space Technol. 77, 166–176 (2018)

    Article  Google Scholar 

  25. Garcia-Garcia, A., et al.: A review on deep learning techniques applied to semantic segmentation. arXiv preprint arXiv 1704, 06857 (2017)

    Google Scholar 

  26. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 60, 84–90 (2012)

    Google Scholar 

  27. Szegedy, C., et al.: Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition (2015)

  28. Zhang, M., et al.: The application of one-class classifier based on CNN in image defect detection. Procedia Comput. Sci. 114, 341–348 (2017)

    Article  Google Scholar 

  29. Tang, H., et al.: Median filtering detection of small-size image based on CNN. J. Visual Commun. Image Represent. 51, 162–168 (2018)

    Article  Google Scholar 

  30. Gao, H., et al.: Robust detection of median filtering based on combined features of difference image. Signal Process. Image Commun. 72, 126–133 (2019)

    Article  Google Scholar 

  31. Mafi, M., et al.: A comprehensive survey on impulse and Gaussian denoising filters for digital images. Signal Process. 157, 236–260 (2019)

    Article  Google Scholar 

  32. Holland, J.H.: An introductory analysis with applications to biology, control, and artificial intelligence. Adaptation in natural and artificial systems, 1st edn. The University of Michigan, USA (1975)

    MATH  Google Scholar 

  33. Pourmohammadali, B., et al.: Studying the relationships between nutrients in pistachio leaves and its yield using hybrid GA-ANN model-based feature selection. Comput. Electron. Agric. 172, 105352 (2020)

    Article  Google Scholar 

  34. Ranganathan, A.: The levenberg-marquardt algorithm. Tutoral LM Algorithm 11(1), 101–110 (2004)

    Google Scholar 

  35. Madsen, K., Nielsen, H.B., Tingleff, O.: Methods for non-linear least squares problems. (2004)

  36. Jaro, M., et al.: Implementation of K-means segmentation algorithm on Intel Xeon Phi and GPU: Application in medical imaging. Adv. Eng. Softw. 103, 21–28 (2017)

    Article  Google Scholar 

  37. Phadikar, S., Sil, J., Das, A.K.: Classification of rice leaf diseases based on morphological changes. Int. J. Inf. Electron. Eng. 2(3), 460–463 (2012)

    Google Scholar 

  38. Lv, J., et al.: A segmentation method of bagged green apple image. Sci. Hortic. 246, 411–417 (2019)

    Article  Google Scholar 

  39. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  Google Scholar 

  40. Kunnath, N.X., Lee, S.H.: Meanshift segmentation guided spatially adaptive histogram equalizatio. Springer, Berlin, Heidelberg (2015)

    Google Scholar 

  41. Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell. 17(8), 790–799 (1995)

    Article  Google Scholar 

  42. Prajapati, H.B., Shah, J.P., Dabhi, V.K.: Detection and classification of rice plant diseases. Intell. Decis. Technol. 11(3), 357–373 (2017)

    Article  Google Scholar 

  43. LeCun, Y., et al.: Handwritten digit recognition with a back-propagation network. Advances in neural information processing systems. (1990)

  44. Lin, T.Y., et al.: Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision. (2017)

  45. Keras-GPU: https://anaconda.org/anaconda/keras-gpu. Accessed 17 Jun 2019

  46. GeForce GTX 1060: https://www.nvidia.com/en-us/geforce/products/10series/geforce-gtx- 1060/specifications. Accessed 17 Jun 2019

  47. Hughes, D., Salathé, M.: An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv 1511, 08060 (2015)

    Google Scholar 

  48. Glauner, P.O.: Deep convolutional neural networks for smile recognition. ar Xiv preprint arXiv 1508, 06535 (2015)

    Google Scholar 

  49. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. European conference on computer vision, Springer, Cham (2014)

    Book  Google Scholar 

Download references

Acknowledgements

This work is partly supported by the grants from the National Natural Science Foundation of China (Project no. 61672439) and the Fundamental Research Funds for the Central Universities (#20720181004). The authors wish to thank all the editors and anonymous reviewers for their constructive advice.

Author information

Authors and Affiliations

  1. School of Informatics, Xiamen University, Xiamen, 361005, China

    Junde Chen, Jinxiu Chen, Defu Zhang & Y. A. Nanehkaran

  2. Spira Inc, Los Angeles, CA 90032, USA

    Yuandong Sun

Authors
  1. Junde Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinxiu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Defu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Y. A. Nanehkaran
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuandong Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Defu Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, J., Chen, J., Zhang, D. et al. A cognitive vision method for the detection of plant disease images. Machine Vision and Applications 32, 31 (2021). https://doi.org/10.1007/s00138-020-01150-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00138-020-01150-w

Keywords

Search

Navigation