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@article{178125,
        author = {Kondakindi Eshvitha and VAGGU UDAYKIRAN and Syed Rayaan Masood and VANGALA SARATH KUMAR and Mohammed Farooq and Dr. M . Ramesh},
        title = {Crop Yield Analysis & Farm Management System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2854-2858},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178125},
        abstract = {Crop Yield Analysis & Farm Management System is an AI-driven platform designed to help farmers make better decisions. The system predicts crop yields using machine learning algorithms based on soil quality, weather patterns, and crop type. It also provides real-time weather forecasts, helping farmers plan irrigation and farming schedules effectively. Farmers can access AI-powered disease detection by uploading crop images, allowing early identification of issues. Additionally, the platform tracks market price trends, guiding farmers to choose the most profitable crops. A decision support chatbot offers easy-to-understand recommendations, ensuring farmers receive guidance in their preferred language. The system ensures accessibility through a simple, interactive interface that presents results clearly with charts and visual cues. By combining AI and real-time data, this platform improves productivity, reduces costs, and supports sustainable farming practices.},
        keywords = {Crop yield prediction, farm management, AI in agriculture, soil health analysis, market trends, sustainable farming.},
        month = {May},
        }