• BHUVANESHWAR C
• PRAVEEN P
• RASHMI V
• VISHNU VARDHANA S
• HIMANSU SEKHAR ROUT

Agriculture is essential in developing countries but faces challenges like crop diseases, low yields, and poor resource management. These issues often arise from limited access to timely information and expert advice. An AI-based system addresses these problems by helping farmers make informed, data-driven decisions. It detects crop diseases from leaf images using deep learning models like CNN and MobileNetV2. It also recommends suitable crops based on environmental factors such as soil nutrients, pH, temperature, and rainfall. Additionally, it provides personalized fertilizer suggestions based on soil and crop needs. Built with Python and Flask, the system uses real agricultural datasets and features a simple web interface. Docker is used for easy deployment and scalability. This integrated solution promotes sustainable farming and reduces guesswork and costs. It aims to improve productivity and support rural farmers with accessible, AI-powered tools.

" AI-DRIVEN CROP DISEASE PREDICTION AND MANAGEMENT SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.b418-b423, May-2025, Available :http://www.jetir.org/papers/JETIR2505148.pdf

" AI-DRIVEN CROP DISEASE PREDICTION AND MANAGEMENT SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppb418-b423, May-2025, Available at : http://www.jetir.org/papers/JETIR2505148.pdf