Automated Cotton Leaf Disease Detection Using Image Processing and Machine Learning in MATLAB

Abstract

This paper presents an automated cotton leaf disease detection system using image processing and machine learning techniques implemented in MATLAB. Cotton crops are vulnerable to various leaf diseases that significantly impact yield, and traditional detection methods are manual and time-consuming. The proposed system processes leaf images through preprocessing, segmentation, feature extraction, and classification to identify diseased and healthy leaves. Color, texture, and shape features are extracted and used to train a supervised machine learning model for accurate disease recognition. Experimental results indicate that the system provides a fast, reliable, and efficient solution for early cotton leaf disease diagnosis, supporting improved agricultural management practices.