ML Methods for Crop Yield Prediction and Estimation: An Exploration
M. Alagurajan1, C. Vijayakumaran2
1M. Alagurajan, Department of CSE, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India.
2C. Vijayakumaran, Associate Professor, Department CSE, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India.
Manuscript received on January 23, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 3506-3508 | Volume-9 Issue-3, February 2020. | Retrieval Number: C5775029320/2020©BEIESP | DOI: 10.35940/ijeat.C5775.029320
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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: Machine learning Has performed a essential position within the estimation of crop yield for both farmers and consumers of the products. Machine learning techniques learn from data set related to the environment on which the estimations and estimation are to be made and the outcome of the learning process are used by farmers for corrective measures for yield optimization. This paper we explore various ML techniques utilized in crop yield estimation and provide the detailed analysis of accuracy of the techniques.
Keywords: Machine Learning, Crop Yield.