Plant Disease Detection System Using Machine Learning Algorithm

Abstract Plant diseases pose a significant threat to global food security, causing substantial agricultural losses. Traditional disease detection methods are labor-intensive and often unreliable. This paper presents an automated Machine Learning (ML)-based Plant Disease Detection System that leverages Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forest (RF) for accurate disease classification using leaf images. We utilize a publicly available dataset (Plant Village) containing thousands of labeled images of healthy and diseased leaves. Our experiments demonstrate that deep learning models (CNNs) achieve 98.7% accuracy, outperforming traditional ML techniques. The proposed system includes a mobile application for real-time disease detection, enabling farmers to take timely preventive measures. This research contributes to precision agriculture by providing an efficient, scalable, and cost-effective disease detection solution. Keywords: Plant disease detection, Machine Learning, Deep Learning, CNN, SVM, Random Forest, Precision Agriculture, Image Processing