Crop Yield Prediction using XG Boost Algorithm
Rohit Ravi1, B. Baranidharan2
1Rohit Ravi*, M.Tech, Department of Computer Science & Engineering, SRM Institute of Science & Technology, Chennai, India.
2Dr. B. Baranidharan, Associate Professor, Department of Computer Science & Engineering, SRM Institute of Science & Technology, Chennai, India.
Manuscript received on January 01, 2020. | Revised Manuscript received on January 20, 2020. | Manuscript published on January 30, 2020. | PP: 3516-3520 | Volume-8 Issue-5, January 2020. | Retrieval Number: D9547118419/2020©BEIESP | DOI: 10.35940/ijrte.D9547.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The main objective of this research is to predict crop yields based on cultivation area, Rainfall and maximum and minimum temperature data. It will help our Indian farmers to predict crop yielding according to the environment conditions. Nowadays, Machine learning based crop yield prediction is very popular than the traditional models because of its accuracy. In this paper, linear regression, Support Vector Regression, Decision Tree and Random forest is compared with XG Boost algorithm. The above mentioned algorithms are compared based on R2, Minimum Square Error and Minimum Absolute Error. The dataset is prepared from the data.gov.in site for the year from 2000 to 2014. The data for 4 south Indian states Andhra Pradesh, Karnataka, Tamil Nadu and Kerala data alone is taken since all these states has same climatic conditions. The proposed model in this paper based on XG Boost is showing much better results than other models. In XG Boost R2 is 0.9391 which is the best when compared with other models.
Keywords: Crop Yield, Precision Agriculture, Random Forest, Support Vector Machine, XG Boost Algorithm Artificial Intelligence, Machine Learning.
Scope of the Article: Machine Learning.