AI Driven Crop Disease Prediction and Management System - R. Karthikeyan,; Bainaboina Nandhini,; Avula Mounika,; Siddhavatam Venkatesh,; Mopuri Sandeep, (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025)
Agriculture is extremely important to human civilization, providing food and contributing to the economy. Plants are often susceptible to diseases and insects that have considerable challenges during production. Early detection of harvest diseases is important to minimize damage and reduce costs. While traditional methods do not provide real-time identification, foldable neuronal networks (CNNs) provide a solution by allowing for accurate detection and classification of leaf disease. This study focuses on identifying diseases in plants such as apples, grapes, corn, potatoes and tomatoes. The proposed deep CNN model is compared to a transfer learning approach, such as VGG16. AI-based systems analyze plant images to recognize diseases at the early stages and recommend management strategies, loss of harvests and improved yields. Such systems have applications in agriculture and biological research.
Keywords: Agriculture Technology; Convolutional Neural Networks (CNNs); Crop Disease Detection; Early Disease Prediction