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@article{156263,
        author = {CH. Preethi and N. Likhitha Reddy and B. Mounika and J. Alisha Reddy},
        title = {Crop Yield Prediction Using Machine Learning Technologies},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {3},
        pages = {224-225},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=156263},
        abstract = {The primary thing that is essential for survival is agriculture. A key viewpoint for finding a practical and real-world solution to the crop production issue is machine learning (ML). It primarily focuses on estimating the crop yield using a variety of machine learning approaches. Here, Naive Bayes and K Nearest Neighbor are utilised as classifier models, which aid in delivering the highest level of accuracy. By taking into account variables like temperature, soil, rainfall, acreage, etc., the predictions provided by machine learning algorithms will assist farmers in choosing which crop to cultivate to induce the greatest yield. This ties the technology and agricultural sectors together.},
        keywords = {Crop_yield_prediction, Machine Learning, Naive Bayes, K Nearest Neighbor.},
        month = {},
        }