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The Estimation of the Long-Term Agricultural Output with a Robust Machine Learning Prediction Model

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  • Chin-Hung Kuan

    (Department of Information Management, National Taiwan University of Science and Technology, Taipei City 106, Taiwan)

  • Yungho Leu

    (Department of Information Management, National Taiwan University of Science and Technology, Taipei City 106, Taiwan)

  • Wen-Shin Lin

    (Department of Plant Industry, National Pingtung University of Science and Technology, Pingtung 912, Taiwan)

  • Chien-Pang Lee

    (Department of Maritime Information and Technology, National Kaohsiung University of Science and Technology, Kaohsiung City 805, Taiwan)

Abstract

Recently, annual agricultural data have been highly volatile as a result of climate change and national economic trends. Therefore, such data might not be enough to develop good agricultural policies for stabilizing agricultural output. A good agricultural output prediction model to assist agricultural policymaking has thus become essential. However, the highly volatile data would affect the prediction model’s performance. For this reason, this study proposes a marriage in honey bees optimization/support vector regression (MBO/SVR) model to minimize the effects of highly volatile data (outliers) and enhance prediction accuracy. We verified the performance of the MBO/SVR model by using the annual total agricultural output collected from the official Agricultural Statistics Yearbook of the Council of Agriculture, Taiwan. Taiwan’s annual total agricultural output integrates agricultural, livestock and poultry, fishery, and forest products. The results indicated that the MBO/SVR model had a lower mean absolute percentage error (MAPE), root mean square percentage error (RMSPE), and relative root mean squared error ( r -RMSE) than those of the models it was compared to. Furthermore, the MBO/SVR model predicted long-term agricultural output more accurately and achieved higher directional symmetry (DS) than the other models. Accordingly, the MBO/SVR model is a robust, high-prediction-accuracy model for predicting long-term agricultural output to assist agricultural policymaking.

Suggested Citation

  • Chin-Hung Kuan & Yungho Leu & Wen-Shin Lin & Chien-Pang Lee, 2022. "The Estimation of the Long-Term Agricultural Output with a Robust Machine Learning Prediction Model," Agriculture, MDPI, vol. 12(8), pages 1-15, July.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:8:p:1075-:d:869234
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    References listed on IDEAS

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