An Artificial Intelligence Approach to Prediction of Corn Yields under Extreme Weather Conditions Using Satellite and Meteorological Data

Kim, Nari; Na, Sang-Il; Park, Chan-Won; Huh, Morang; Oh, Jaiho; Ha, Kyung-Ja; Cho, Jaeil; Lee, Yang-Won

doi:10.3390/app10113785

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An Artificial Intelligence Approach to Prediction of Corn Yields under Extreme Weather Conditions Using Satellite and Meteorological Data

¹,

²,

³,

⁴

⁵

and

Yang-Won Lee

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Geomatics Research Institute, Pukyong National University, Busan 48513, Korea

Climate Change and Agro-Ecology Division, National Institute of Agricultural Sciences, Jeollabuk-do 55365, Korea

Nano Weather Incorporation, Gyeonggi-do 13449, Korea

⁴

IBS Center for Climate Physics and Department of Atmospheric Sciences, Pusan National University, Busan 46241, Korea

⁵

Department of Applied Plant Science, Chonnam National University, Gwangju 61186, Korea

⁶

Department of Spatial Information Engineering, Pukyong National University, Busan 48513, Korea

Author to whom correspondence should be addressed.

Appl. Sci. 2020, 10(11), 3785; https://doi.org/10.3390/app10113785

Received: 29 March 2020 / Revised: 19 May 2020 / Accepted: 26 May 2020 / Published: 29 May 2020

(This article belongs to the Special Issue Machine Learning Techniques Applied to Geospatial Big Data)

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Abstract

This paper describes the development of an optimized corn yield prediction model under extreme weather conditions for the Midwestern United States (US). We tested six different artificial intelligence (AI) models using satellite images and meteorological data for the dominant growth period. To examine the effects of extreme weather events, we defined the drought and heatwave by considering the characteristics of corn growth and selected the cases for sensitivity tests from a historical database. In particular, we conducted an optimization for the hyperparameters of the deep neural network (DNN) model to ensure the best configuration for accuracy improvement. The result for drought cases showed that our DNN model was approximately 51–98% more accurate than the other five AI models in terms of root mean square error (RMSE). For the heatwave cases, our DNN model showed approximately 30–77% better accuracy in terms of RMSE. The correlation coefficient was 0.954 for drought cases and 0.887–0.914 for heatwave cases. Moreover, the accuracy of our DNN model was very stable, despite the increases in the duration of the heatwave. It indicates that the optimized DNN model can provide robust predictions for corn yield under conditions of extreme weather and can be extended to other prediction models for various crops in future work. View Full-Text

Keywords: corn yield; extreme weather; artificial intelligence; satellite image; meteorological data

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MDPI and ACS Style

Kim, N.; Na, S.-I.; Park, C.-W.; Huh, M.; Oh, J.; Ha, K.-J.; Cho, J.; Lee, Y.-W. An Artificial Intelligence Approach to Prediction of Corn Yields under Extreme Weather Conditions Using Satellite and Meteorological Data. Appl. Sci. 2020, 10, 3785. https://doi.org/10.3390/app10113785

AMA Style

Kim N, Na S-I, Park C-W, Huh M, Oh J, Ha K-J, Cho J, Lee Y-W. An Artificial Intelligence Approach to Prediction of Corn Yields under Extreme Weather Conditions Using Satellite and Meteorological Data. Applied Sciences. 2020; 10(11):3785. https://doi.org/10.3390/app10113785

Chicago/Turabian Style

Kim, Nari, Sang-Il Na, Chan-Won Park, Morang Huh, Jaiho Oh, Kyung-Ja Ha, Jaeil Cho, and Yang-Won Lee. 2020. "An Artificial Intelligence Approach to Prediction of Corn Yields under Extreme Weather Conditions Using Satellite and Meteorological Data" Applied Sciences 10, no. 11: 3785. https://doi.org/10.3390/app10113785

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An Artificial Intelligence Approach to Prediction of Corn Yields under Extreme Weather Conditions Using Satellite and Meteorological Data

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