Potato Leaf Disease Detection Using Machine Learning and Deep Learning Techniques

This paper presents an automated system for detecting and classifying diseases in potato leaves using machine learning (ML) and deep learning (DL) techniques. The study focuses on three major diseases that affect potato plants—Late Blight, Early Blight, and Bacterial Wilt—and explores both traditional ML approaches and modern DL architectures for image classification. We have used a publicly available data set and implemented different models like Support Vector Machines (SVM), Random Forest, K Nearest Neighbors, and Convolutional Neural Networks (CNN). From the experimental result, it is obvious that CNN-based models, and with transfer learning outperform traditional ML models significantly in terms of accuracy, precision and recall