Abstract
The United States seems to have become the primary source of global corn production and export, making corn production critical to the economic activities of many countries. Many previous studies provide yield forecasts. Ground-based telemetry via satellites has recently emerged and attempts to predict vegetation indices for yield. However, except vegetation index, we should know more about vegetation area and coverage for overall consideration. Therefore, this study uses four major corn-producing areas in the United States and related data for the past nine years for model training, including multivariate linear regression, partial least squares regression, stepwise regression, and Gaussian kernel support vector regression. The experimental results show that the support vector regression with Gaussian kernel (radial basis function kernel) performs the best, and the R2 value reaches 0.94.
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Acknowledgment
We would like to thank National Science and Technology Council, Taiwan for generously supporting this research through project #112-2410-H-008-017-MY2.
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Ratna, S., Hsu, PY., Shih, YS., Cheng, MS., Chen, YC. (2023). Predicting Maize Yields with Satellite Information. In: Huynh, VN., Le, B., Honda, K., Inuiguchi, M., Kohda, Y. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2023. Lecture Notes in Computer Science(), vol 14375. Springer, Cham. https://doi.org/10.1007/978-3-031-46775-2_17
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