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
Article Information
Identifiers and Pagination:
Year: 2024Volume: 18
E-location ID: e18743315288623
Publisher ID: e18743315288623
DOI: 10.2174/0118743315288623240223072349
Article History:
Received Date: 26/11/2023Revision Received Date: 23/01/2024
Acceptance Date: 02/02/2024
Electronic publication date: 05/03/2024
Collection year: 2024
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.
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
