Forecasting Crop Yield Through Classification Approaches of Machine Learning
R. Akshara1, Deepak Sanaka2, Anusha Vaspari3, J. Uday Kiran4
1R. Akshara, Assistant Professor, Department of Computer Science and Engineering, Vignan Institute of Technology and Science College in Hyderabad, India.
2J. Uday Kiran, Department of B. Tech (Computer Science Engineering), Vignan Institute of Technology and Science, College in Hyderabad, India.
3Deepak Sanaka, Department of B. Tech (Computer Science Engineering), Vignan Institute of Technology and Science, College in Hyderabad, India.
4Anusha Vaspari, Department of B. Tech (Computer Science Engineering), Vignan Institute of Technology and Science, College in Hyderabad, India.
Manuscript received on 01 April 2019 | Revised Manuscript received on 06 May 2019 | Manuscript published on 30 May 2019 | PP: 838-843 | Volume-8 Issue-1, May 2019 | Retrieval Number: A9233058119/19©BEIESP
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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: Crop yields are critically dependent on weather. Data Mining and Machine Learning help a great deal in forecasting the yield data beforehand which would be beneficial to the farmers who could then plan the irrigation procedures according to the predicted yield. We make use of already available data to get the forecast figures. We propose to use supervised learning techniques specifically classification models on the available data to get the forecast figures. This paper focuses on wheat yield across the country with analysis done for mid 2017- mid 2018 data.
Index Terms: Data Mining, Machine Learning, Yield Forecasting.
Scope of the Article: Classification