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@article{191531,
        author = {Vijayalakshmi A},
        title = {Pondicherry Crop Yield Prediction Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {7648-7651},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191531},
        abstract = {Agriculture plays a vital role in the economy of India, and accurate crop yield prediction is essential for improving agricultural productivity and decision-making. In regions like Pondicherry, farmers face several challenges due to unpredictable weather conditions, variation in rainfall, temperature changes, and pesticide usage. Traditional methods of crop yield estimation are mostly based on experience and historical practices, which may not provide accurate results under changing climatic conditions.
This project proposes a machine learning–based crop yield prediction system specifically designed for Pondicherry villages. The system analyzes environmental factors such as temperature, rainfall, and pesticide usage to predict crop yield efficiently. A trained machine learning model is integrated with a Flask-based web application that allows users to enter crop and environmental details and obtain yield predictions instantly. The system also stores prediction history and provides data visualization using graphical representations. This approach helps farmers and agricultural planners make informed decisions, reduce risk, and improve overall agricultural productivity.},
        keywords = {Crop Yield Prediction, Machine Learning, Agriculture, Flask, Data Visualization},
        month = {January},
        }