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Comparative Analysis of Statistical and Machine Learning Techniques for Rice Yield Forecasting for Chhattisgarh, India

Author

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  • Anurag Satpathi

    (Department of Agrometeorology, College of Agriculture, G.B. Pant University of Agriculture and Technology, Pantnagar 263153, India)

  • Parul Setiya

    (Department of Agrometeorology, College of Agriculture, G.B. Pant University of Agriculture and Technology, Pantnagar 263153, India)

  • Bappa Das

    (ICAR Central Coastal Agricultural Research Institute, Old Goa 403402, India)

  • Ajeet Singh Nain

    (Department of Agrometeorology, College of Agriculture, G.B. Pant University of Agriculture and Technology, Pantnagar 263153, India)

  • Prakash Kumar Jha

    (Sustainable Intensification Innovation Lab, Kansas State University, Manhattan, KS 66506, USA)

  • Surendra Singh

    (Columbia Basin Agricultural Research Center, Oregon State University, Adams, OR 97810, USA)

  • Shikha Singh

    (Hermiston Agricultural Research and Extension Center, Oregon State University, Hermiston, OR 97838, USA)

Abstract

Crop yield forecasting before harvesting is critical for the creation, implementation, and optimization of policies related to food safety as well as for agro-product storage and marketing. Crop growth and development are influenced by the weather. Therefore, models using weather variables can provide reliable predictions of crop yields. It can be tough to select the best crop production forecasting model. Therefore, in this study, five alternative models, viz., stepwise multiple linear regression (SMLR), an artificial neural network (ANN), the least absolute shrinkage and selection operator (LASSO), an elastic net (ELNET), and ridge regression, were compared in order to discover the best model for rice yield prediction. The outputs from individual models were used to build ensemble models using the generalized linear model (GLM), random forest (RF), cubist and ELNET methods. For the previous 21 years, historical rice yield statistics and meteorological data were collected for three districts under three separate agro-climatic zones of Chhattisgarh, viz., Raipur in the Chhattisgarh plains, Surguja in the northern hills, and Bastar in the southern plateau. The models were calibrated using 80% of these datasets, and the remaining 20% was used for the validation of models. The present study concluded that for rice crop yield forecasting, the performance of the ANN was good for the Raipur ( R c a l 2 = 1, R v a l 2 = 1 and R M S E c a l = 0.002, R M S E v a l = 0.003) and Surguja ( R c a l 2 = 1, R v a l 2 = 0.99 and R M S E c a l = 0.004, R M S E v a l = 0.214) districts as compared to the other models, whereas for Bastar, ELNET ( R c a l 2 = 90, R v a l 2 = 0.48) and LASSO ( R c a l 2 = 93, R v a l 2 = 0.568) performed better. The performance of the ensemble model was better compared to the individual models. For Raipur and Surguja, the performance of all the ensemble methods was comparable, whereas for Bastar, random forest (RF) performed better, with R 2 = 0.85 and 0.81 for calibration and validation, respectively, as compared to the GLM, cubist, and ELNET approach.

Suggested Citation

  • Anurag Satpathi & Parul Setiya & Bappa Das & Ajeet Singh Nain & Prakash Kumar Jha & Surendra Singh & Shikha Singh, 2023. "Comparative Analysis of Statistical and Machine Learning Techniques for Rice Yield Forecasting for Chhattisgarh, India," Sustainability, MDPI, vol. 15(3), pages 1-18, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2786-:d:1056816
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    References listed on IDEAS

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    5. Johnathon Shook & Tryambak Gangopadhyay & Linjiang Wu & Baskar Ganapathysubramanian & Soumik Sarkar & Asheesh K Singh, 2021. "Crop yield prediction integrating genotype and weather variables using deep learning," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-19, June.
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    2. Jin, Keyan & Zhong, Ziqi & Zhao, Elena Yifei, 2024. "Sustainable digital marketing under big data: an AI random forest model approach," LSE Research Online Documents on Economics 121402, London School of Economics and Political Science, LSE Library.
    3. Saim Khalid & Hadi Mohsen Oqaibi & Muhammad Aqib & Yaser Hafeez, 2023. "Small Pests Detection in Field Crops Using Deep Learning Object Detection," Sustainability, MDPI, vol. 15(8), pages 1-19, April.

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