RESEARCH ARTICLE


Intelligent Plant Leaf Disease Detection Using Generative Adversarial Networks: a Case-study of Cassava Leaves



Gururaj Harinahalli Lokesh1, *, Soundarya Bidare Chandregowda6, Janhavi Vishwanath2, Vinayakumar Ravi3, *, Pradeep Ravi4, Alanoud Al Mazroa5
1 Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India
2 Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India
3 Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia
4 Department of Information Science and Engineering, GSSS Institute of Engineering and Technology for Women, Mysuru, Karnataka, India
5 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), P.O. Box 84428, Riyadh 11671, Saudi Arabia
6 Department of Artificial Intelligence and Machine Learning, Alva’s Institute of Engineering and Technology, Mangalore, India


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Creative Commons License
© 2024 The Author(s). Published by Bentham Open.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to these authors at the Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia and Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India, E-mails: gururaj.hl@manipal.edu and vinayakumarr77@gmail.com


Abstract

Background

Cassava leaf disease detection is a major problem since it is very difficult to identify the disease in naked-eye observation and even experts such as agricultural scientists can fail in this task. The approach we use in this paper has the potential to overcome this problem.

Materials and Methods

In this, we propose an approach based on artificial intelligence for leaf disease detection using deep learning with generative adversarial networks (GAN). Our experimental study used a dataset including 12880 cassava leaf pictures generated using CycleGAN showing five major disease classes. In order to avoid overfitting, a GAN architecture is proposed for data augmentation using two networks, i.e., a Generator and a Discriminator. The generator is trained to generate similar data samples as the original data

Results

The proposed approach achieved an accuracy of 99.51% for the classification of healthy or unhealthy leaf images, which outperformed existing methods.

Discussion

The discriminator is trained to distinguish between the unique and generated sample records, as actual or fake. To classify cassava images into five categories of diseases, a combination of machine learning models has been trained on original and generated images. The proposed approach showed better accuracy compared to the existing methods.

Conclusion

The proposed deep learning-based method can be used as a tool for early disease diagnosis in cassava leaf disease detection and classification

Keywords: Plant leaf disease, Cassava, Deep learning, CycleGAN, CNN, Deep learning.