International Journal of Science, Engineering and TechnologyAuthors: R. Dhanush, S. Cynthia Juliet
Abstract: Plant diseases pose a significant threat to agricultural productivity, resulting in substantial economic losses and food insecurity. The early detection and precise diagnosis of these diseases are critical to mitigating their impact. This research paper explores the integration of Artificial Intelligence (AI) technologies in the prediction and detection of plant diseases. By utilizing advanced machine learning algorithms, image processing techniques, and large datasets, AI can accurately identify disease symptoms from plant images. The system is designed to help farmers and agricultural experts take preventive actions by identifying infections at an early stage, thereby reducing the need for excessive pesticide use and improving crop health. In this study, a convolutional neural network (CNN) model is implemented to classify images of plants and predict diseases. The system's performance is evaluated based on its accuracy, efficiency, and real-world applicability. This AI- driven solution offers a scalable approach to sustainable agriculture by enabling precise disease management, enhancing yield, and supporting food security. The findings suggest that AI-based plant disease prediction can revolutionize agricultural practices by providing real-time insights and actionable recommendations to farmers.
