MODELING AND FORECASTING OF AREA AND PRODUCTION OF SORGHUM OF KARNATAKA USING SUPPORT VECTOR MACHINE AND ARTIFICIAL NEURAL NETWORK MODELS

N. VIJAY¹*, G.C. MISHRA^2
1Central Muga Eri Research and Training Institute, Central Silk Board, Ministry of Textile, Govt of India, Lahdoigarh, Jorhat, 785700, Assam, India
²Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, 221005, India
* Corresponding Author : ntvijay@gmail.com

Received : 01-11-2018     Accepted : 27-11-2018     Published : 30-11-2018
Volume : 10     Issue : 22       Pages : 7535 - 7538
Int J Agr Sci 10.22 (2018):7535-7538

Keywords : Artificial Neural Network, Support Vector Machine, Root Mean Square Errors, Mean Absolute Percentage Errors and prediction
Academic Editor : Santosha Rathod
Conflict of Interest : None declared
Acknowledgements/Funding : Authors are thankful to Indian Council of Agricultural Research for providing Senior Research Fellowship (SRF). Authors are also thankful to Banaras Hindu University, Varanasi, Uttar Pradesh, 221005, India
Author Contribution : All authors equally contributed

The aim of this study was to compare the performance of time series models based on artificial neural network and support vector machine techniques to predict the area and production of sorghum crop. The data consist of area and production of sorghum crop area (‘000 ha) and production (‘000 MT) from 1955-56 to 2014-15 were collected from “Agricultural Statistics at a Glance 2014-15, Karnataka, India [2]. The support vector machine and artificial neural network models used to predict the area and production of sorghum crop, which is one of most important millet crop of Karnataka. The models’ results are compared using three criteria, i.e., Mean Squared Error (MSE), Root-Mean-Square Errors (RMSE), and Mean Absolute Percentage Error (MAPE). A comparison of support vector machine results with artificial neural network method indicates that support vector machine is better than the artificial neural network method in prediction of the area and production of sorghum.

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