Transfer Learning for Plants’ Disease Classification with Siamese Networks in low data regime

Authors

  • El mehdi RAOUHI LTI Laboratory, ENSA, University Chouaib Doukkali, El Jadida, Morocco
  • Mohamed LACHGAR LTI laboratory, ENSA, Chouaib Doukkali University, El Jadida 1166, Morocco https://orcid.org/0000-0002-6155-3309
  • Hamid HRIMECH University Hassan 1st, ENSA of Berrechid, LAMSAD, B.P 218, Morocco
  • Ali KARTIT LTI laboratory, ENSA, Chouaib Doukkali University, El Jadida 1166, Morocco

Keywords:

Deep Learning, Convolutional Neuronal Networks, Transfer Learning, Siamese Networks

Abstract

Timely disease detection in plants remains a challenging task for farmers. They do not have many options other than consulting fellow farmers. Expertise in plant diseases is necessary for an individual to be able to identify the diseased leaves. For this, Deep Convolutional Neuronal Networks based approaches are readily available to find solutions for various problems related to plant disease detection. Actually advanced deep CNN-based models successfully performed good accuracy. However, due to a smaller number of image samples available in the datasets, there exist problems of over-fitting obstructing the performance of deep learning approaches. In this work, we used a Siamese convolutional neural network (SCNN) model with different Transfer Learning (TL) models to classify plants diseases. In our approach, we extend the insufficient and various volume data by species using data augmentation techniques. Experiments are performed on a publicly available dataset open access series of imaging studies (Plant Village), by using the proposed approach, an excellent test accuracy of 96.77% is achieved for the classification of plants disease using variant training sample size especially those on low data regime. We proceed to compare Transfer Learning with Siamese Network with their state-of-the-art most CNN architectures and discovered that the proposed model using Siamese Network outperformed the state-of-the-art models in terms of performance, efficiency, and accuracy.

Downloads

Download data is not yet available.

References

A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, “A survey of the recent architectures of deep convolutional neural networks,” Artif. Intell. Rev., pp. 1–70, 2020.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–14, 2015.

S. Liu and W. Deng, “Very deep convolutional neural network based image classification using small training sample size,” Proc. - 3rd IAPR Asian Conf. Pattern Recognition, ACPR 2015, pp. 730–734, 2016.

M. H. Saleem and J. Potgieter, “Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers,” 2020.

S. B. Jadhav, “Identification of plant diseases using convolutional neural networks,” Int. J. Inf. Technol., 2020.

S. R. Maniyath et al., “Plant disease detection using machine learning,” Proc. - 2018 Int. Conf. Des. Innov. 3Cs Comput. Commun. Control. ICDI3C 2018, no. April, pp. 41–45, 2018.

J. Boulent, S. Foucher, J. Théau, and P. L. St-Charles, “Convolutional Neural Networks for the Automatic Identification of Plant Diseases,” Front. Plant Sci., vol. 10, no. July, 2019.

M. Türkoğlu and D. Hanbay, “Plant disease and pest detection using deep learning-based features,” pp. 1636–1651, 2019.

A. Fuentes, S. Yoon, S. C. Kim, and D. S. Park, “A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition,” Sensors (Switzerland), vol. 17, no. 9, 2017.

M. H. Saleem, J. Potgieter, and K. M. Arif, “Plant disease detection and classification by deep learning,” Plants, vol. 8, no. 11, pp. 32–34, 2019.

R. Barman, “Transfer Learning for Small Dataset,” Proceedings of National Conference on Machine Learning, 26th March 2019 no. April, 2019.

J. M. Celaya-padilla, J. I. Galván-tejada, H. Gamboa-rosales, and C. A. Olvera-olvera, “applied sciences Comparison of Convolutional Neural Network Architectures for Classification of Tomato Plant Diseases.”, Appl. Sci. 2020

S. E. E. Profile and S. E. E. Profile, “A Preliminary Study on Deep Transfer Learning Applied to Image Classification for Small Datasets A preliminary study on deep transfer learning applied to image classification for small datasets,” no. January, 2021.

J. G. A. Barbedo, “Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification,” Comput. Electron. Agric., vol. 153, no. October, pp. 46–53, 2018.

A. Mehmood, M. Maqsood, M. Bashir, and Y. Shuyuan, “A deep siamese convolution neural network for multi-class classification of alzheimer disease”, Brain Sci., vol. 10, no. 2, 2020.

F. A. Foysal, M. S. Islam, and S. Abujar, “A Novel Approach for Tomato Diseases Classification Based on Deep Convolutional Neural Networks”, no. July. Springer Singapore, 2019.

A. Brodzicki, D. Kucharski, and J. Jaworek-korjakowska, “Transfer Learning Methods as a New Approach in Computer Vision Tasks with Small Datasets Transfer Learning Methods as a New Approach in Computer Vision Tasks with Small Datasets,” no. October, 2020.

A. Krizhevsky and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Advances in neural information processing systems. pp. 1–9. 2012.

C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, “Inception-v4, inception-ResNet and the impact of residual connections on learning,” 31st AAAI Conf. Artif. Intell. AAAI 2017, pp. 4278–4284, 2017.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 770–778, 2016.

P. Goncharov, A. Uzhinskiy, G. Ososkov, A. Nechaevskiy, and J. Zudikhina, “Deep Siamese Networks for Plant Disease Detection,” EPJ Web Conf., vol. 226, p. 03010, 2020

P. Pawara, E. Okafor, and L. Schomaker, “Data Augmentation for Plant Classification,” pp. 615–626, 2017.

Downloads

Published

2021-07-20

How to Cite

[1]
E. mehdi RAOUHI, M. . LACHGAR, H. HRIMECH, and A. KARTIT, “Transfer Learning for Plants’ Disease Classification with Siamese Networks in low data regime”, IJCEDS, vol. 1, no. 1, pp. 8–13, Jul. 2021.

Issue

Vol. 1 No. 1 (2021): Volume 1. Issue 1

Section

Articles

License

Copyright (c) 2021 El mehdi RAOUHI, Mohamed LACHGAR, Hamid HRIMECH, Ali KARTIT

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Copyright on any article published in the International Journal of Computer Engineering and Data Science (IJCEDS) is retained by the author(s). All articles are published under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0), which permits any non-commercial use, distribution, and reproduction in any medium, provided that the original work is properly cited.

License Agreement

By submitting and publishing their work in IJCEDS, the authors:

  • Grant IJCEDS the non-exclusive right to publish the article and to identify IJCEDS as the original publisher.

  • Authorize any third party to use, share, and reproduce the article for non-commercial purposes, provided that appropriate credit is given to the original authors and source, and a link to the license is included.