An Image Processing System for the Detection of Cotton Crop Diseases

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.