Prediction of Rice Cultivation in India—Support Vector Regression Approach with Various Kernels for Non-Linear Patterns
| Autor: | B. M. Nayana, Christophe Chesneau, Kolla Rohith Kumar, Chinnarao Kurangi, Kalpana Polisetty, Kiran Kumar Paidipati |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Polynomial
010504 meteorology & atmospheric sciences Mean squared error media_common.quotation_subject rice cultivation Pharmaceutical Science 010501 environmental sciences 01 natural sciences Statistics Production (economics) Pharmacology (medical) Radial basis function support vector regression with kernels lcsh:Agriculture (General) 0105 earth and related environmental sciences Mathematics media_common Variables food security prediction lcsh:S1-972 Support vector machine RMSE and MAE Complementary and alternative medicine lcsh:TA1-2040 Kernel (statistics) Hyperparameter optimization lcsh:Engineering (General). Civil engineering (General) |
| Zdroj: | AgriEngineering, Vol 3, Iss 12, Pp 182-198 (2021) AgriEngineering Volume 3 Issue 2 Pages 12-198 |
| ISSN: | 2624-7402 |
| Popis: | The prediction of rice yields plays a major role in reducing food security problems in India and also suggests that government agencies manage the over or under situations of production. Advanced machine learning techniques are playing a vital role in the accurate prediction of rice yields in dealing with nonlinear complex situations instead of traditional statistical methods. In the present study, the researchers made an attempt to predict the rice yield through support vector regression (SVR) models with various kernels (linear, polynomial, and radial basis function) for India overall and the top five rice producing states by considering influence parameters, such as the area under cultivation and production, as independent variables for the years 1962–2018. The best-fitted models were chosen based on the cross-validation and hyperparameter optimization of various kernel parameters. The root-mean-square error (RMSE) and mean absolute error (MAE) were calculated for the training and testing datasets. The results revealed that SVR with various kernels fitted to India overall, as well as the major rice producing states, would explore the nonlinear patterns to understand the precise situations of yield prediction. This study will be helpful for farmers as well as the central and state governments for estimating rice yield in advance with optimal resources. |
| Databáze: | OpenAIRE |
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