Enhancing potato crop yield with AI-powered CNN-based leaf disease detection and tracking

  • Mudassir Iftikhar Department of Computer Science, Sindh Madressatul Islam University Karachi, Sindh Pakistan
  • Irfan Ali Kandhro Department of Computer Science, Sindh Madresstual Islam University Karachi, Sindh Pakistan
  • Asadullah Kehar Institute of Computer Science, Shah Abdul Latif University, Khairpur
  • Neha Kausar Department of Computer Science, Sindh Madressatul Islam University Karachi, Sindh Pakistan

Abstract

While plant diseases continue to have a severe impact on food production, farmers face a formidable challenge in trying to meet the escalating demands of a population that is expanding quickly for agricultural items like potatoes. Despite spending billions on disease management, farmers frequently struggle to effectively control disease without the aid of cutting-edge technology. The paper examines a disease diagnosis method based on deep learning. To be more precise, it uses a Convolutional Neural Network (CNN) method for the disease's detection and classification. This study examines the impact of data augmentation while conducting an extensive performance evaluation of the hyper-parameter in the setting of detecting plant diseases with a focus on potatoes. The experimental findings demonstrate the effectiveness of the suggested model's 98% accuracy. Considering growing global issues, this research aims to open new pathways for more efficient plant disease management and, eventually, increase agricultural output.

Published
Apr 7, 2024
How to Cite
IFTIKHAR, Mudassir et al. Enhancing potato crop yield with AI-powered CNN-based leaf disease detection and tracking. Mehran University Research Journal of Engineering and Technology, [S.l.], v. 43, n. 2, p. 123-132, apr. 2024. ISSN 2413-7219. Available at: <https://publications.muet.edu.pk/index.php/muetrj/article/view/3034>. Date accessed: 24 may 2024. doi: http://dx.doi.org/10.22581/muet1982.3034.
Section
Articles
This is an open Access Article published by Mehran University of Engineering and Technolgy, Jamshoro under CCBY 4.0 International License