Plant Disease Detection & Classification System

Abstract -- Agriculture plays a pivotal role in global food security, and the early detection of crop diseases is essential for maintaining optimal crop yield and quality. In this research, we explore the application of machine learning (ML) techniques for crop disease detection, aiming to provide a reliable and efficient solution to mitigate the impact of diseases on agricultural productivity. Leveraging a comprehensive dataset comprising diverse crops and disease instances, we employ state-of-the- art ML algorithms, including convolutional neural networks (CNNs) and support vector machines (SVMs), to develop a robust disease detection model. The dataset is carefully curated, and preprocessing steps are applied to enhance the model's generalization capabilities. Our experimental results demonstrate promising levels of accuracy, precision, recall, and F1 score, showcasing the potential of ML in accurately identifying and classifying crop diseases.