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@article{168497,
        author = {SHAIK AMRIN and B MURALI},
        title = {PREDICTING AGRICULTURE YIELDS USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {1030-1035},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168497},
        abstract = {Agriculture contributes a significant amount to the economy of India due to the dependence on human beings for their survival. The main obstacle to food security is population expansion leading to rising prediction, technology can assist farmers in producing more. This project main goal is to predict crop yields utilizing the features of amount of rainfall, crop, area, production, pesticides and fertilizers that have posed a serious threat to the long-term viability of agriculture. Crop yield prediction is a decision support tool that uses machine learning algorithms, that can be used to make decisions about which crops to produce. To estimate the agriculture yield, machine learning techniques: Decision Tree, Random Forest, Support Vector Machine, K Nearest Neighbors Regressor and XG-Boost have been used and for performance evaluation accuracy, root mean square error, mean square error and mean absolute error are compared.},
        keywords = {Crop Yield Prediction, Machine Learning, Agriculture, Food Security, Sustainable Agriculture},
        month = {October},
        }