An Intelligent Deep Learning Framework for Prediction of Diseases in Cotton Plants Using Leaf Images

Abstract

: Cotton is one of the most economically im-portant fiber crops worldwide, but its productivity is significantly affected by var-ious foliar diseases that reduce both yield and quality. Early and accurate detection of these diseases remains a major chal-lenge in traditional agriculture due to reli-ance on manual inspection, which is time-consuming and prone to human error. In this study, an intelligent deep learning framework is proposed for the automated prediction of diseases in cotton plants using leaf images. The framework leverages ad-vanced image processing techniques and Convolutional Neural Networks (CNNs), along with transfer learning models, to classify healthy and diseased leaves with high accuracy. The system incorporates image preprocessing, data augmentation, and feature extraction to enhance model performance and generalization. Experi-mental results demonstrate that the pro-posed approach achieves superior accura-cy, precision, and robustness compared to conventional methods. The developed mod-el has significant potential for real-time deployment in precision agriculture sys-tems, enabling farmers to take timely dis-ease management actions and improve crop productivity.