A Comparative Analysis of Machine Learning Prediction Techniques for Crop Yield Prediction in India
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Abstract
Nowadays, crop yield prediction is one of the most recent, interesting and challenging tasks
due to its dependence on various variable parameters like environmental, weather, soil and
climate factors. Machine learning has become one of the important tools for predicting crop
yield. This paper presents a machine learning framework for crop yield prediction using crop
and weather data. It also compares the performance of potential machine learning methods
like regression, decision trees, random forest, support vector machine and gradient boosting
to forecast the yield of 80 crops in India for the year 2001 to 2016 using historical data.
Furthermore, it has been observed from the results that the root mean square (RMSE) of the
random forest method is 9433.7 for the dataset.
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