Rice Yield Forecasting Using Support Vector Machine
Sunil Kumar1, Vivek Kumar2, R. K. Sharma3
1Sunil Kumar*, Assistant Professor, Department of Management Studies, Kumaun University Campus, Bhimtal, India.
2Vivek Kumar, Professor, Department of Computer Science, Gurukula Kangri Vishwavidyalaya, Haridwar, India.
3R.K. Sharma, Professor, Department of Computer Science and Engineering, Thapar Institute of Engineering & Technology, Patiala, India.

Manuscript received on November 22, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on November 30, 2019. | PP: 2588-2593 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7236118419/2019©BEIESP | DOI: 10.35940/ijrte.D7236.118419

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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: In the domain of Soft Computing, Support Vector Machines (SVMs) have acquired considerable significance. These are widely used in making predictions, owing to their ability of generalization. This paper is about the development of SVM based classification models for the prediction of rice yield in India. Experiments have been conducted involving one-against-one multi classification method, k-fold cross validation and polynomial kernel function for SVM training. Rice production data of India has been sourced from Directorate of Economics and Statistics, Ministry of Agriculture, Government of India, for this work. The best prediction accuracy for the 4-year relative average increase has been achieved as 75.06% using 4-fold cross validation method. MATLAB software has been used for experimentation in this work.
Keywords: Support Vector Machines, Rice Yield, Forecasting, Training Patterns.
Scope of the Article: Machine Design.