Open AccessRice Yield Forecasting using Support Vector Machine
Author(s) -
Sunil Kumar,
Vivek Kumar,
Richa Sharma
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d7236.118419
Subject(s) - support vector machine , christian ministry , matlab , soft computing , generalization , polynomial kernel , cross validation , radial basis function kernel , machine learning , computer science , data mining , yield (engineering) , kernel (algebra) , artificial intelligence , mathematics , kernel method , artificial neural network , mathematical analysis , philosophy , materials science , theology , combinatorics , metallurgy , operating system
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 oneagainst-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.