Disease Detection for Crop Yield using Machine Learning

Agriculture forms the backbone of the Indian economy, with nearly 60% of the land dedicated to farming. One of the critical challenges faced by this sector is crop disease, which significantly reduces yield and quality. This study presents a machine learning-based framework for early detection and diagnosis of crop diseases using image processing and remote sensing techniques. The system combines image partitioning, attribute extraction, and categorization using techniques like K-Means clustering and Support Vector Machines (SVM). It aims to aid farmers with precise, timely information to reduce crop losses and improve yield sustainably.