Potato Disease Detection And Curing Using Machine Learning A Systemic Review
Deepak Yadav Gaurav Kumar Singh Dr. Avinash Kumar Sharma
AbstractPotato crops, crucial for global food security, are highly vulnerable to diseases like late blight and early blight, leading to significant yield losses and economic damage. Traditional disease detection methods are inefficient, prompting the rise of machine learning (ML) techniques in agriculture. This paper reviews recent advancements in using ML, particularly image recognition models such as convolutional neural networks (CNNs), for early disease detection and classification. It also explores ML-driven solutions for disease management, including predictive analytics and optimised pesticide use. The review highlights challenges like data scarcity and model generalization, and discusses future research directions to enhance sustainable potato farming through ML