Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 3 , Issue 2 , PP: 38-47, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Ehsan khodadadi 1 * , Sunil Kumar 2 , Marwa M. Eid 3
Doi: https://doi.org/10.54216/JAIM.030204
Cotton leaf diseases pose significant threats to sustainable farming practices, leading to yield losses and economic burdens for cotton growers worldwide. In this paper, we propose a smart solution for efficient and accurate detection of cotton leaf diseases using machine learning techniques. Our approach leverages a convolutional neural network (CNN) architecture specifically designed for visual recognition of leaf diseases. To train and optimize the CNN model, we employ a genetic algorithm that enhances the learning process and improves classification performance. The proposed model is trained and evaluated on a comprehensive dataset containing six classes of cotton leaf diseases, namely Aphids, Army worm, Bacterial Blight, Powdery Mildew, Target spot, and healthy leaves. Experimental results demonstrate the effectiveness of our proposed method, achieving an overall accuracy of 97% on the test set. Comparative analyses with existing studies and methodologies reveal the superior performance of our approach, showcasing its potential for practical implementation in the field of cotton leaf disease detection. The outcomes of this study have significant implications for farmers, agronomists, and agricultural organizations, enabling them to make informed decisions and take timely actions to protect their crops and enhance productivity.
Machine Learning (ML) , Cotton Farming , Leaf Disease , Artificial Intelligence , Sustainability.
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