Selection of Important Features for Optimizing Crop Yield Prediction
Maya Gopal P S and
Additional contact information
Maya Gopal P S: VIT University, Chennai, India
Bhargavi R: School of Computing Science and Engineering, VIT University, Chennai, India
International Journal of Agricultural and Environmental Information Systems (IJAEIS), 2019, vol. 10, issue 3, 54-71
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.
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 18/IJAEIS.2019070104 (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:igg:jaeis0:v:10:y:2019:i:3:p:54-71
Access Statistics for this article
International Journal of Agricultural and Environmental Information Systems (IJAEIS) is currently edited by Frederic Andres
More articles in International Journal of Agricultural and Environmental Information Systems (IJAEIS) from IGI Global
Bibliographic data for series maintained by Journal Editor ().