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@article{206016,
        author = {Soham Anand Harkare and Harshal Bagade and J. P. Nawade and Harsh Barbhai},
        title = {An Integrated Machine Learning Framework for Intelligent Agricultural Decision Support: Crop Recommendation, Price Prediction, and Yield Forecasting},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {8860-8865},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206016},
        abstract = {Agriculture is fundamental to global food security and economic development, with India being a major agricultural producer. However, farmers often struggle with critical decisions regarding crop selection, market pricing, and yield optimization due to limited access to data-driven insights. This paper presents a comprehensive machine learning-based agricultural assistance system that integrates three predictive models: crop recommendation, price forecasting, and yield prediction. The system achieves a 99.7% accuracy for crop recommendation using Random Forest classification on 2,200 balanced samples across 22 crops. Price prediction employs ensemble methods with an R2 score of 0.897, while yield forecasting utilizes XGBoost regression achieving R2 = 0.9127. The proposed system has been deployed as a production-ready web application with user authentication, real-time predictions, database integration, and Google Sheets logging for data persistence. Field validation demonstrates practical applicability across diverse agricultural regions. The system significantly reduces decision-making time while improving recommendation accuracy, thereby supporting sustainable agriculture and farmer profitability.},
        keywords = {Machine Learning, Crop Recommendation, Price Prediction, Yield Forecasting, Agricultural Technology, Random Forest, XGBoost, Web Application, Data-Driven Farming},
        month = {June},
        }