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Comparison of Linear and Non-linear Models for Coconut Yield Prediction in Coimbatore Using Weather Parameters and External Factors

K. Madeshwaran, A. Eswari, M. R. Duraisamy, Selvi, B. Senthamizh, S. Praneetha, B. Senthamizh Selvi

International Journal of Environment and Climate Change · pp. 1141–1150 · Published 5 Aug 2022

10.9734/ijecc/2022/v12i1131090

Abstract

Coconut is the world's most significant plantation crop, and it is grown in practically every country. As Coimbatore is the leading producer of coconut in Tamil Nadu, followed by Thanjavur and Kanyakumari, this study is centred on the Coimbatore area. West Coast Tall is a popular cultivar that produces more than other types. The West Coast Tall (WCT) cultivar was used in this research. In this paper, four models were developed such as Ridge, Least Absolute Shrinkage Selection Operator (LASSO), Elastic net (ELNET) regression methods and Artificial Neural Networks (ANN). Further, we validate this model using field-level data from TNAU coconut research farm for two years. The purpose of this communication is to find the best fit model for prediction of coconut yield using weather parameters and external factors in Coimbatore district. The models were selected based on different performance metrics such as RMSE, MAPE, MAE, and R2. Among the four models developed in the study, the ELNET model is found to be best model for prediction of coconut yield based on weather and external factors for the available data in the studied region.

Coconut yield prediction ANN penalized regression models numerical analysis

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