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@article{154759,
        author = {Riddhi Dange and Jinisha Kande and Kunal Bhosale and Ankush Hutke},
        title = {Predicting Agricultural Produce using Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {12},
        pages = {329-334},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=154759},
        abstract = {Agriculture is the largest contributor of the GDP in our country. But still the farmers do not get the exact worth price of the crops. It is mostly due to improper irrigation or imprecise crops  selection, sometimes the crop yield is less than expected. In order to add efficiency to the whole agriculture process a “Predicting Agricultural Produce using Machine Learning Techniques” is proposed in the following project. The system aims at predicting the agricultural produce by obtaining area on Google Maps API and further calculate area under cultivation. GPS technology is now used widely by various people and organizations for determining the position of objects. GPS can be used to measure the area of land under cultivation.},
        keywords = {Agriculture, Google Maps API, GPS, Machine  Learning},
        month = {},
        }