Machine Learning Approach for Prediction of Crop Yield

Authors

  • Ashwini V

Keywords:

Agriculture, Recurrent Neural Network (RNN), Decision Tree, Random Forest, Naïve Bayes.

Abstract

India is a global agriculture powerhouse. The average productivity of many crops in India is quite low and the current situation faced by farmers in India leads to increase in suicide rate over years, due to the impact of climate change in country. Crop productivity can be increased using Machine Learning (ML) methods and climate data. For weather forecasting, Machine Learning approaches like Recurrent Neural Network (RNN) are utilised, while Machine Learning categorization techniques like Decision Tree, Random Forest, and Nave Bayes are used to predict appropriate crops. Therefore, its necessary to build a model which takes into consideration of all the parameters for the better selection of crops which increases the crops yield.

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Published

2021-08-11

How to Cite

Ashwini V. (2021). Machine Learning Approach for Prediction of Crop Yield. Journal of Research Proceedings, 1(2), 272–283. Retrieved from http://i-jrp.com/index.php/jrp/article/view/63