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Selection of Important Features for Optimizing Crop Yield Prediction

Selection of Important Features for Optimizing Crop Yield Prediction

Maya Gopal P S, Bhargavi R
Copyright: © 2019 |Volume: 10 |Issue: 3 |Pages: 18
ISSN: 1947-3192|EISSN: 1947-3206|EISBN13: 9781522566748|DOI: 10.4018/IJAEIS.2019070104
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MLA

Maya Gopal P S, and Bhargavi R. "Selection of Important Features for Optimizing Crop Yield Prediction." IJAEIS vol.10, no.3 2019: pp.54-71. http://doi.org/10.4018/IJAEIS.2019070104

APA

Maya Gopal P S & Bhargavi R. (2019). Selection of Important Features for Optimizing Crop Yield Prediction. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 10(3), 54-71. http://doi.org/10.4018/IJAEIS.2019070104

Chicago

Maya Gopal P S, and Bhargavi R. "Selection of Important Features for Optimizing Crop Yield Prediction," International Journal of Agricultural and Environmental Information Systems (IJAEIS) 10, no.3: 54-71. http://doi.org/10.4018/IJAEIS.2019070104

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Abstract

In agriculture, crop yield prediction is critical. Crop yield depends on various features including geographic, climate and biological. This research article discusses five Feature Selection (FS) algorithms namely Sequential Forward FS, Sequential Backward Elimination FS, Correlation based FS, Random Forest Variable Importance and the Variance Inflation Factor algorithm for feature selection. Data used for the analysis was drawn from secondary sources of the Tamil Nadu state Agriculture Department for a period of 30 years. 75% of data was used for training and 25% data was used for testing. The performance of the feature selection algorithms are evaluated by Multiple Linear Regression. RMSE, MAE, R and RRMSE metrics are calculated for the feature selection algorithms. The adjusted R2 was used to find the optimum feature subset. Also, the time complexity of the algorithms was considered for the computation. The selected features are applied to Multilinear regression, Artificial Neural Network and M5Prime. MLR gives 85% of accuracy by using the features which are selected by SFFS algorithm.