A Novel Framework for Intelligent Disease Detection using Advanced Machine Learning-Based Image Processing Technology

Agriculture plays a pivotal role in sustaining global food and textile production, with over 70% of the Indian population directly or indirectly reliant on this sector for their livelihood. In the Indian agricultural landscape, cotton stands out as a vital commercial crop contributing significantly to farmers' income. Despite its economic importance, cotton cultivation faces a persistent challenge—susceptibility to various diseases caused by insects, viruses, and other factors. Detecting these diseases at an early stage is crucial for preventing widespread crop damage. Traditional methods of visual inspection often fall short, necessitating the implementation of an intelligent system for accurate cotton-plant disease detection.This paper proposes a comprehensive framework designed to identify diseases in cotton plants, offering a solution to empower farmers with timely and precise information. By leveraging advanced image processing technology and machine learning algorithms, the proposed system enhances disease identification beyond the capabilities of the naked eye. The goal is to enable farmers to proactively identify and address specific diseases affecting their cotton crops, thereby optimizing resource allocation and ultimately increasing overall agricultural productivity. This framework represents a significant step towards sustainable and efficient cotton farming practices in India.