CNN-Based Potato Leaf Disease Classification System

Abstract

Potato, a cornerstone crop for global food security, faces substantial yield reductions due to various leaf diseases. Detecting these diseases early and accurately is imperative for implementing effective control measures. This study presents a novel deep learning framework leveraging Convolutional Neural Networks (CNNs) for robust potato leaf disease classification. CNNs, renowned for their prowess in image recognition tasks, are well-suited for this purpose due to their ability to learn intricate patterns and features from images. Our approach utilizes a meticulously curated dataset comprising healthy and diseased potato leaf images to train the model, enabling it to accurately discern average from abnormal leaf morphology. Through exhaustive training and evaluation, our CNN model achieves impressive accuracy in distinguishing between healthy and diseased leaves, even under varying conditions. This dependable tool equips farmers with early disease detection capabilities, safeguarding crop health and optimizing yield potential. By leveraging state-of-the-art technology, this research significantly enhances agricultural sustainability and ensures global food security, addressing a critical challenge in contemporary farming practices.