An Image Processing System for the Detection of Cotton Crop Diseases

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ID: 312730
2025
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Abstract
The cotton sector is a significant part of Pakistan's industrial economy, substantially contributing to the nation's GDP. However, sustaining cotton yields has become increasingly difficult due to diseases exacerbated by climate change, which threaten both export revenue and local livelihoods. Traditional methods for identifying these diseases are often inaccurate and inefficient, resulting in significant crop losses and delayed responses. To address this critical issue, this study proposes an AI-driven system that leverages digital image processing and machine learning to provide a scalable, real-time solution for cotton leaf disease detection. By enabling early and accurate identification of diseases, the system facilitates proactive crop management and helps farmers make timely decisions. Integrating a mobile application with cloud-based analytics supports policymakers in effectively tracking disease outbreaks and allocating resources. While promising, future improvements should focus on enhancing data diversity and computational efficiency to ensure widespread adoption. This study offers a valuable contribution to improving agricultural sustainability and productivity in regions heavily reliant on cotton cultivation.
Reference Key
imported_1777056324_69ebba440d3ac Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Benish Khalid, Khushbu Khalid Butt, Hamza Shahab Awan, Irshad Ahmed Sumra
Journal Journal of Computing & Biomedical Informatics
Year 2025
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/952
Keywords Keywords not found

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