Skip to main content
SpringerLink
Log in

Pseudoinverse learning autoencoder with DCGAN for plant diseases classification

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

Abstract

Pest infestation of crops and plants impacts agricultural development. Generally, farmers or specialist observe the plants with the naked eye to recognise and diagnose ailments. However, this technique can be time-consuming, costly and inexact. In contrast, auto-detection using image processing methods gives fast and precise results. This paper introduces a new plant disease identification model predicated on leaf image classification that employs a deep convolutional generative adversarial network (DCGAN) along with a classifier identified by multilayer perceptron (MLP) neural networks trained with a pseudoinverse learning autoencoder (PILAE) algorithm. The DCGAN performes two tasks: (1) synthesis of the minor class images to overcome the issue of imbalance in the dataset and (2) extracting deep features of all images within the dataset. The PILAE training procedure is not required to identify the learning control variables or indicate the number of hidden layers. Consequently, the PILAE classifier can fulfil exceptional execution with regard to training efficiency and reliability. Empirical results from PlantVillage dataset possess demonstrated how the presented method yields positive results with other models and reasonably minimal complexly.

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

Similar content being viewed by others

References

  1. Akhtar A, Khanum A, Khan SA, Shaukat A (2013) “Automated plant disease analysis (apda): Performance comparison of machine learning techniques”. In: 2013 11th International Conference on Frontiers of Information Technology, pp. 60–65 IEEE

  2. Al-Hiary H, Bani-Ahmad S, Reyalat M, Braik M, ALRahamneh Z (2011) Fast and accurate detection and classification of plant diseases. International Journal of Computer Applications 17(1):31–38

    Article  Google Scholar 

  3. Ale L, Sheta A, Li L, Wang Y, Zhang N (2019) “Deep learning based plant disease detection for smart agriculture”. In: 2019 IEEE Globecom Workshops GC Wkshps, pp. 1–6 IEEE

  4. Arsenovic M, Karanovic M, Sladojevic S, Anderla A, Stefanovic D (2019) Solving current limitations of deep learning based approaches for plant disease detection. Symmetry 11(7):939

    Article  Google Scholar 

  5. Athanikar G, Badar P (2016) Potato leaf diseases detection and classification system. International Journal of Computer Science and Mobile Computing 5(2):76–88

    Google Scholar 

  6. Athiwaratkun B, Kang K (2015) “Feature representation in convolutional neural networks”. arXiv:1507.02313

  7. Barré P., Stöver B. C., Müller K. F., Steinhage V (2017) Leafnet: a computer vision system for automatic plant species identification. Ecological Informatics 40:50–56

    Article  Google Scholar 

  8. Barua S, Ma X, Erfani SM, Houle ME, Bailey J (2019) Quality evaluation of gans using cross local intrinsic dimensionality. arXiv:1905.00643

  9. Blancard D, Laterrot H, Marchoux G, Candresse T (2012) 2-Diagnosis of parasitic and nonparasitic diseases. Tomato Diseases 35–411

  10. Brahimi M, Arsenovic M, Laraba S, Sladojevic S, Boukhalfa K, Moussaoui A (2018) Deep Learning for Plant diseases: Detection and Saliency Map Visualisation, pp. 93–117 Springer International Publishing

  11. Brahimi M, Boukhalfa K, Moussaoui A (2017) Deep learning for tomato diseases: classification and symptoms visualization. Appl Artif Intell 31 (4):299–315

    Article  Google Scholar 

  12. Cai W, Wei Z (2020) Piigan: Generative adversarial networks for pluralistic image inpainting. IEEE Access 8:48451–48463

    Article  Google Scholar 

  13. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research 16:321–357

    Article  Google Scholar 

  14. Choudhury SD, Yu J-G, Samal A (2018) Leaf recognition using contour unwrapping and apex alignment with tuned random subspace method. Biosyst Eng 170:72–84

    Article  Google Scholar 

  15. Dandawate Y, Kokare R (2015) “An automated approach for classification of plant diseases towards development of futuristic decision support system in indian perspective. In: 2015 International conference on advances in computing, communications and informatics (ICACCI), pp. 794–799 IEEE

  16. DeChant C, Wiesner-Hanks T, Chen S, Stewart EL, Yosinski J, Gore MA, Nelson RJ, Lipson H (2017) Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology 107 (11):1426–1432

    Article  Google Scholar 

  17. Dyrmann M, Karstoft H, Midtiby HS (2016) Plant species classification using deep convolutional neural network. Biosyst Eng 151:72–80

    Article  Google Scholar 

  18. Fan DP, Cheng MM, Liu JJ, Gao SH, Hou Q, Borji A (2018) “Salient objects in clutter:, Bringing salient object detection to the foreground”. In: Proceedings of the European conference on computer vision (ECCV), pp 186–202

  19. Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318

    Article  Google Scholar 

  20. Fu K, Zhao Q, Gu IY-H, Yang J (2019) Deepside: a general deep framework for salient object detection. Neurocomputing 356:69–82

    Article  Google Scholar 

  21. Fujita E, Kawasaki Y, Uga H, Kagiwada S, Iyatomi H (2016) Basic investigation on a robust and practical plant diagnostic system. In: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 989–992 IEEE

  22. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680

  23. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680

  24. Guo P, Lyu MR (2004) A pseudoinverse learning algorithm for feedforward neural networks with stacked generalization applications to software reliability growth data. Neurocomputing 56:101–121

    Article  Google Scholar 

  25. Guo P, Lyu MR, Chen CLP (2003) Regularization parameter estimation for feedforward neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B 33(1):35–44

    Article  Google Scholar 

  26. Guo P, Zhou X, Wang K (2018) “Pilae:, A non-gradient descent learning scheme for deep feedforward neural networks”. arXiv:1811.01545

  27. Hanssen IM, Lapidot M (2012) “Major tomato viruses in the mediterranean basin,”. In: Advances in virus research, vol. 84, pp. 31–66 Elsevier

  28. Hughes D, Salathé M. et al (2015) “An open access repository of images on plant health to enable the development of mobile disease diagnostics”. arXiv:1511.08060

  29. Hwang U, Choi S, Yoon S (2017) Disease prediction from electronic health records using generative adversarial networks. arXiv:1711.04126

  30. Kawasaki Y, Uga H, Kagiwada S, Iyatomi H (2015) “Basic study of automated diagnosis of viral plant diseases using convolutional neural networks”. In: International Symposium on Visual Computing pp. 638–645 Springer

  31. Kheirkhah FM, Asghari H (2018) Plant leaf classification using gist texture features. IET Comput Vis 13(4):369–375

    Article  Google Scholar 

  32. Kornblith S, Shlens J, Le QV (2019) Do better imagenet models transfer better?. In: proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2661–2671

  33. Krizhevsky A, Sutskever I, Hinton GE (2012) “Imagenet classification with deep convolutional neural networks”. In: Advances in neural information processing systems, pp 1097–1105

  34. Kumar N, Belhumeur PN, Biswas A, Jacobs DW, Kress WJ, Lopez I, Soares JVB (2012) “Leafsnap: A computer vision system for automatic plant species identification”. In: The 12th European Conference on Computer Vision ECCV

  35. LeCun Y, Bengio Y, Hinton G (2015) “Deep learning”. nature 521(7553):436

    Article  Google Scholar 

  36. Liang S, Zhang W (2019) “Accurate image recognition of plant diseases based on multiple classifiers integration”. In: Chinese Intelligent Systems Conference, pp. 103–113 Springer

  37. Lu Y, Yi S, Zeng N, Liu Y, Zhang Y (2017) Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267:378–384

    Article  Google Scholar 

  38. Mahmoud MAB, Guo P (2019) A novel method for traffic sign recognition based on dcgan and mlp with pilae algorithm. IEEE Access 7:74602–74611

    Article  Google Scholar 

  39. Mao W, Liu Y, Ding L, Li Y (2019) Imbalanced fault diagnosis of rolling bearing based on generative adversarial network: a comparative study. IEEE Access 7:9515–9530

    Article  Google Scholar 

  40. Mohanty SP, Hughes DP, Salathé M. (2016) Using deep learning for image-based plant disease detection. Frontiers in plant science 7:1419

    Article  Google Scholar 

  41. Mokhtar U, El Bendary N, Hassenian AE, Emary E, Mahmoud MA, Hefny H, Tolba MF (2015) “Svm-based detection of tomato leaves diseases”. In: Intelligent Systems’ 2014, pp. 641–652 Springer

  42. Nachtigall LG, Araujo RM, Nachtigall GR (2016) “Classification of apple tree disorders using convolutional neural networks”. In: 2016 IEEE 28th International Conference on Tools with Artificial Intelligence ICTAI, pp. 472–476 IEEE

  43. Nazki H, Yoon S, Fuentes A, Park DS (2020) “Unsupervised image translation using adversarial networks for improved plant disease recognition”. Computers and Electronics in Agriculture 168:105117

    Article  Google Scholar 

  44. Ping G, Duan F, Pei W, Yao Y, Xin X (2017) Pulsar candidate identification with artificial intelligence techniques. arXiv:1711.10339

  45. Radford A, Metz L, Chintala S (2015) “Unsupervised representation learning with deep convolutional generative adversarial networks”. arXiv:1511.06434

  46. Ren S, He K, Girshick R, Sun J (2015). In: Advances in neural information processing systems, pp 91–99

  47. Ren JS, Wang W, Wang J, Liao S (2012) “An unsupervised feature learning approach to improve automatic incident detection”. In: 2012 15th International IEEE Conference on Intelligent Transportation Systems pp. 172–177 IEEE

  48. Sa I, Ge Z, Dayoub F, Upcroft B, Perez T, McCool C (2016) Deepfruits: a fruit detection system using deep neural networks. Sensors 16(8):1222

    Article  Google Scholar 

  49. Samanta D, Chaudhury PP, Ghosh A (2012) Scab diseases detection of potato using image processing. International Journal of Computer Trends and Technology 1:3

    Google Scholar 

  50. Shah MP, Singha S, Awate SP (2017) “Leaf classification using marginalized shape context and shape+ texture dual-path deep convolutional neural network”. In: 2017 IEEE International Conference on Image Processing ICIP, pp. 860–864 IEEE

  51. Shi R, Zhai D, Liu X, J Jiang, et al. (2020) “Rectified meta-learning from noisy labels for robust image-based plant disease diagnosis”. arXiv:2003.07603

  52. Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D (2016) Deep neural networks based recognition of plant diseases by leaf image classification. Computational intelligence and neuroscience. 2016

  53. Söderkvist O. (2001) “Computer vision classification of leaves from swedish trees” Master’s Thesis, Teknik Och Teknologier Stockholm Sweden

  54. Suh S, Lee H, Jo J, Lukowicz P, Lee YO (2019) Generative oversampling method for imbalanced data on bearing fault detection and diagnosis. Applied Sciences, vol. 4:9

    Google Scholar 

  55. SungHo B “Deep convolutional generative adversarial network (dcgan) implementation on matconvnet.” https://github.com/sunghbae/dcgan-matconvnet, [Online; accessed 19-6-2019].

  56. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826

  57. Tian Y, Yang G, Wang Z, Li E, Liang Z (2019) Detection of apple lesions in orchards based on deep learning methods of cyclegan and yolov3-dense, vol 2019

  58. Too EC, Li Y, Njuki S, Liu Y (2018) A comparative study of fine-tuning deep learning models for plant disease identification. Comput Electron Agric 161:272–279

    Article  Google Scholar 

  59. Too EC, Yujian L, Kwao P, Njuki S, Mosomi ME, Kibet J (2019) Deep pruned nets for efficient image-based plants disease classification. Journal of Intelligent & Fuzzy systems, no Preprint 1–17

  60. Valerio Giuffrida M, Scharr H, Tsaftaris SA (2017) “Arigan: Synthetic arabidopsis plants using generative adversarial network”. In: Proceedings of the IEEE International Conference on Computer Vision Workshops pp 2064–2071

  61. Vedaldi A, Lenc K (2015) “Matconvnet: Convolutional neural networks for matlab”. In: proceedings of the 23rd ACM international conference on Multimedia, pp. 689–692 ACM

  62. Wang X, Du W, Guo F, Hu S (2020) Leaf recognition based on elliptical half gabor and maximum gap local line direction pattern. IEEE Access 8:39175–39183

    Article  Google Scholar 

  63. Wang J, Guo P, Xin X (2018) “Review of pseudoinverse learning algorithm for multilayer neural networks and applications”. In: International Symposium on Neural Networks, pp. 99–106 Springer

  64. Wang K, Guo P, Xin X, Ye Z (2017) “Autoencoder low rank approximation and pseudoinverse learning algorithm”. In: Systems, Man, and Cybernetics SMC, 2017 IEEE International Conference on, pp. 948–953 IEEE

  65. Wang BX, Japkowicz N (2004) “Imbalanced data set learning with synthetic samples”. In: Proc. IRIS Machine Learning Workshop 19 sn

  66. Wang H, Li G, Ma Z, Li X (2012) “Application of neural networks to image recognition of plant diseases”. In: 2012 International Conference on Systems and Informatics ICSAI 2012, pp. 2159–2164 IEEE

  67. Wang D, Vinson R, Holmes M, Seibel G, Bechar A, Nof S, Luo Y, Tao Y (2018) “Early tomato spotted wilt virus detection using hyperspectral imaging technique and outlier removal auxiliary classifier generative adversarial nets (or-ac-gan)”. In: 2018 ASABE Annual International Meeting, p. 1 American Society of Agricultural and Biological Engineers

  68. Wang B, Wang D (2019) “Plant leaves classification: A few-shot learning method based on siamese network”. IEEE Access 7:151754–151763

    Article  Google Scholar 

  69. Wang Z, Zou C, Cai W (2020) Small sample classification of hyperspectral remote sensing images based on sequential joint deeping learning model. IEEE Access 8:71353–71363

    Article  Google Scholar 

  70. Yang C, Wei H (2019) Plant species recognition using triangle-distance representation. IEEE Access 7:178108–178120

    Article  Google Scholar 

  71. You H, Tian S, Yu L, Lv Y (2019) “Pixel-level remote sensing image recognition based on bidirectional word vectors”. In: IEEE Transactions on Geoscience and Remote Sensing

  72. Zhang J, Chen L, Zhuo L, Liang X, Li J (2018) An efficient hyperspectral image retrieval method: Deep spectral-spatial feature extraction with dcgan and dimensionality reduction using t-sne-based nm hashing. Remote Sens 10(2):271

    Article  Google Scholar 

  73. Zhang Z, Liu X, Cui Y (2016) Multi-phase offline signature verification system using deep convolutional generative adversarial networks. In: 2016 9th international Symposium on Computational Intelligence and Design (ISCID), vol. 2, pp. 103–107 IEEE

  74. Zhao J-X, Liu J-J, Fan D-P, Cao Y, Yang J, Cheng M-M (2019) “Egnet:, Edge guidance network for salient object detection”. In: Proceedings of the IEEE International Conference on Computer Vision, pp 8779–8788

  75. Zhu Y, Aou M, Krijn M, Vanschoren J, Campus HT (2018) “Data augmentation using conditional generative adversarial networks for leaf counting in arabidopsis plants” BMVC p 324

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

    Mohammed A. B. Mahmoud

  2. School of Systems Science, Beijing Normal University, Beijing, China

    Ping Guo

  3. Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI, USA

    Ke Wang

Authors
  1. Mohammed A. B. Mahmoud
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ping Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ke Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammed A. B. Mahmoud.

Additional information

Publisher’s note

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

This work is fully supported by the grants from the National Natural Science Foundation of China (NSFC) (61375045), and the Joint Research Fund in Astronomy (U1531242) under cooperative agreement between the NSFC and Chinese Academy of Sciences (CAS).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mahmoud, M.A.B., Guo, P. & Wang, K. Pseudoinverse learning autoencoder with DCGAN for plant diseases classification. Multimed Tools Appl 79, 26245–26263 (2020). https://doi.org/10.1007/s11042-020-09239-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09239-0

Keywords

Search

Navigation