AI-Driven crop disease prediction and Management system using Deep-Learning

AI-DRIVEN CROP DISEASE PREDICTION AND MANAGEMENT SYSTEM

Parinitha M1, Nethra K2, Yukthi V3, Dr. Ramesh Sengodan4

1 2 3 Students in Computer Science and Engineering at presidency university, Bengaluru

4 Professor in Computer science and Engineering at presidency university, Bengaluru

Abstract - Monitoring crop health is vital for the realization of high productivity and sustainable farming. Crops continue to lower productivity and cost farmers high economic losses despite the achievements made in the field. This project offers a system powered by AI that identifies crop disease from photographs of infected leaves. The model is built on convolutional neural networks (CNNs) and has a prediction accuracy of 94.8%. A friendly web interface allows farmers to submit leaf pictures and get instant disease diagnoses. Beyond detection, the system also gives suitable treatment and prevention advice. This eliminates dependence on human inspection and minimizes unnecessary pesticide use. The AI system streamlines decision-making and enhances crop health and yield in general. It is scalable and can handle a variety of crops and farming areas. The platform also enables precision farming by combining technology with actual farm needs. The model has been validated over multiple crops and shown good performance. Overall, the system provides an intelligent, stable, and environmentally friendly solution for contemporary agriculture.

Keywords: Crop disease detection, Artificial Intelligence, Deep learning, Convolutional Neural Networks, Precision agriculture, Sustainable farming