Crop Disease Detection Using Machine Learning

Agriculture plays a vital role in sustaining human civilization, and plant diseases significantly impact crop yield, quality, and overall production.Traditional methods of identifying crop diseases are often labor-intensive and time-consuming, limiting timely intervention.With recent advancements in Machine Learning (ML), more efficient, automated, and accurate approaches for disease detection have emerged.This paper presents a comprehensive review of various ML techniques applied to crop disease detection, focusing on image processing, deep learning models, and predictive analytics.It highlights commonly used algorithms, datasets, and evaluation metrics in disease classification and early detection.The findings aim to support the development of smarter agricultural practices and enhance global food security.