Comparison of Linear and Non-linear Models for Coconut Yield Prediction in Coimbatore Using Weather Parameters and External Factors
International Journal of Environment and Climate Change,
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.
- yield prediction
- penalized regression models
- numerical analysis
How to Cite
Mahesha A, Lakshman N. Influence of weather variables on coconut (Cocos nucifera L.) yield. Mausam. 1993; 44(1):102-104.
Peiris TSG, Hansen JW, Lareef Zubai. Use of seasonal climate information to predict coconut production in Sri Lanka. Int. J. Climatol. 2008;28:103–110.
Pathmeswaran C, Lokupitiya E, Waidyarathne KP, Lokupitiya RS. Impact of extreme weather events on coconut productivity in three climatic zones of Sri Lanka. Eur J Agron. 2018;96:47–53.
Balabin RM, Lomakina EI, Safieva RZ. Neural network (ANN) approach to biodiesel analysis: Analysis of biodiesel density, kinematic viscosity, methanol and water contents using near infrared (NIR) spectroscopy, Fuel. 2011;90(5):2007–2015.
Jayashree LS, Palakkal N, Papageorgiou EI, Papageorgiou K. Application of fuzzy cognitive maps in precision agriculture: A case study on coconut yield management of southern India’s Malabar region. Neural Comput & Applic. 2015;26:1963–1978.
Bapak Das, Bhakti Nair, Vadivel Arunachalam, Viswanatha Reddy K. Comparative evaluation of linear and nonlinear weather-based models for coconut yield prediction in the west coast of India. Int. J. Biometeorol., 2020; 64:1111–1123.
Singh RS, Patel C, Yadav MK, Singh KK. Yield forecasting of rice and wheat crops for eastern Uttar Pradesh. Journal of Agrometerology. 2014;16(2):199.
Draper NR, Smith H. Applied regression analysis. John Wiley & Sons, Hoboken; 1998.
Tibshirani R. Regression shrinkage and selction via the lasso. Journal of the royal Statistical Society: Series B (Methological). 1996;58(1):267-268.
Hoerl AE, Kennard RW. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics. 1970;12(1):55-67.
Cho S, Kim Oh S, Kim K, Park T. Elastic net regularization approaches for genome wide association studies of rheumatoid arthiritis. BMC proceedings. 2009;3(7): 1-6.
Piaskowski JL, Brown D, Campbell KG. Near infrared calibration of soluble stem carbohydrates for predicting drought tolerance in spring wheat. Agronomy Journal. 2016;108(1):258-293.
Hastie T, Qian J. Glmnet vignette. 2014; 9:1-30.
Friedman J, Hastie T, Tsbshirani R. Glmnet: Lasso and elastic net regularized generalized linear models. R Package Version. 2009;1(4):1-24.
Abstract View: 34 times
PDF Download: 13 times