A Comparative Analysis of Machine Learning Prediction Techniques for Crop Yield Prediction in India
Main Article Content
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.
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
