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Novel combination artificial neural network models could not outperform individual models for weather-based cashew yield prediction

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Abstract

Cashew is an important cash crop which is ecologically sensitive, making it vulnerable to climate change. So, the present study compares the performance of stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), elastic net, and artificial neural network (ANN) individually against the ANN model combined with SLR, LASSO, elastic net, and principal components analysis (PCA) for prediction of cashew yield based on weather parameters. The model performances were evaluated using three approaches: (1) Taylor plot; (2) statistical metrics like coefficient of determination (R2), root mean square error (RMSE), and normalized RMSE (nRMSE); and (3) ranking followed by Kruskal–Wallis and Dunn’s post hoc test. The results revealed that during calibration, the R2 and RMSE ranged from 0.486 to 0.999 and 2.184 to 88.040 kg ha−1, respectively, while RMSE and nRMSE varied from 3.561 to 242.704 kg ha−1 and 0.799 to 89.949%, respectively, during validation. Kruskal–Wallis and Dunn’s post hoc test revealed LASSO as the best model which was at par with ELNET, SLR, and ELNET-ANN. So, these models can be used for cashew yield prediction for the study area well in advance.

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Data availability

The datasets and code generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The India Meteorological Department is duly acknowledged for providing weather data of different stations. This work was supported by the Indian Council of Agricultural Research under Institute project at ICAR-Central Coastal Agricultural Research Institute, Old Goa, Goa, India.

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  1. ICAR-Central Coastal Agricultural Research Institute, Old Goa 403402, Goa, India

    Bappa Das, Dayesh Murgaonkar, S. Navyashree & Parveen Kumar

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  1. Bappa Das
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  2. Dayesh Murgaonkar
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  3. S. Navyashree
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Correspondence to Bappa Das or Parveen Kumar.

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Das, B., Murgaonkar, D., Navyashree, S. et al. Novel combination artificial neural network models could not outperform individual models for weather-based cashew yield prediction. Int J Biometeorol 66, 1627–1638 (2022). https://doi.org/10.1007/s00484-022-02306-1

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