AI-Driven Plant Disease Detection: Revolutionizing Agricultural Diagnostics Through Computer Vision and Deep Learning

The research paper, titled "AI-Driven Plant Disease Detection: Revolutionizing Agricultural Diagnostics Through Computer Vision and Deep Learning," focuses on leveraging artificial intelligence to transform plant health monitoring. It describes the design and implementation of a mobile application that uses AI-based models, particularly convolutional neural networks (CNNs), to detect plant diseases through image recognition. The study details data collection, preprocessing, model training, and testing phases. It emphasizes real-world application by incorporating edge computing to enable offline functionality, ensuring usability in regions with limited internet connectivity. Through structured evaluation and case studies, the app demonstrates high accuracy in diagnosing diseases like nitrogen deficiency and bacterial infections. This research addresses the urgent need for cost-effective and accessible agricultural tools to combat crop loss due to diseases. It provides a robust, scalable solution and highlights potential improvements, such as integration with IoT devices and expanding the dataset to include rare crop diseases.