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@article{193111,
        author = {Vedangi Sharma and Shivanam Vashishtha and Tanisha and Shivraj Pal},
        title = {Crop Yield Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {4211-4215},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193111},
        abstract = {Agricultural productivity serves as a cornerstone of economic stability and food security worldwide. This research presents an intelligent crop yield prediction framework leveraging machine learning algorithms integrated with a Flask-based web interface. The system analyzes multiple environmental parameters including temperature, rainfall, humidity, and soil characteristics to forecast agricultural output.
We implemented and evaluated four distinct machine learning algorithms. The Random Forest algorithm demonstrated superior performance with 94.2% accuracy, followed by the Decision Tree at 89.7%. Experimental validation using historical agricultural datasets from multiple regions confirms the system's reliability and practical applicability in precision farming scenarios.
The web application provides farmers with an intuitive platform for real-time predictions, enabling data-driven agricultural decisions.},
        keywords = {Crop Yield Prediction, Machine Learning, Flask, Random Forest, Precision Agriculture, Web Application, Agricultural Analytics},
        month = {February},
        }