An Artificial Intelligence Approach to Prediction of Corn Yields under Extreme Weather Conditions Using Satellite and Meteorological Data
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 exami...
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MDPI AG
2020-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/11/3785 |
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| author | Nari Kim Sang-Il Na Chan-Won Park Morang Huh Jaiho Oh Kyung-Ja Ha Jaeil Cho Yang-Won Lee |
| author_facet | Nari Kim Sang-Il Na Chan-Won Park Morang Huh Jaiho Oh Kyung-Ja Ha Jaeil Cho Yang-Won Lee |
| author_sort | Nari Kim |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T19:31:12Z |
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| id | doaj.art-f53374f5f1b842b98c05c760b687977a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T19:31:12Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f53374f5f1b842b98c05c760b687977a2023-11-20T02:10:26ZengMDPI AGApplied Sciences2076-34172020-05-011011378510.3390/app10113785An Artificial Intelligence Approach to Prediction of Corn Yields under Extreme Weather Conditions Using Satellite and Meteorological DataNari Kim0Sang-Il Na1Chan-Won Park2Morang Huh3Jaiho Oh4Kyung-Ja Ha5Jaeil Cho6Yang-Won Lee7Geomatics Research Institute, Pukyong National University, Busan 48513, KoreaClimate Change and Agro-Ecology Division, National Institute of Agricultural Sciences, Jeollabuk-do 55365, KoreaClimate Change and Agro-Ecology Division, National Institute of Agricultural Sciences, Jeollabuk-do 55365, KoreaNano Weather Incorporation, Gyeonggi-do 13449, KoreaNano Weather Incorporation, Gyeonggi-do 13449, KoreaIBS Center for Climate Physics and Department of Atmospheric Sciences, Pusan National University, Busan 46241, KoreaDepartment of Applied Plant Science, Chonnam National University, Gwangju 61186, KoreaDepartment of Spatial Information Engineering, Pukyong National University, Busan 48513, KoreaThis 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.https://www.mdpi.com/2076-3417/10/11/3785corn yieldextreme weatherartificial intelligencesatellite imagemeteorological data |
| spellingShingle | Nari Kim Sang-Il Na Chan-Won Park Morang Huh Jaiho Oh Kyung-Ja Ha Jaeil Cho Yang-Won Lee An Artificial Intelligence Approach to Prediction of Corn Yields under Extreme Weather Conditions Using Satellite and Meteorological Data Applied Sciences corn yield extreme weather artificial intelligence satellite image meteorological data |
| title | An Artificial Intelligence Approach to Prediction of Corn Yields under Extreme Weather Conditions Using Satellite and Meteorological Data |
| title_full | An Artificial Intelligence Approach to Prediction of Corn Yields under Extreme Weather Conditions Using Satellite and Meteorological Data |
| title_fullStr | An Artificial Intelligence Approach to Prediction of Corn Yields under Extreme Weather Conditions Using Satellite and Meteorological Data |
| title_full_unstemmed | An Artificial Intelligence Approach to Prediction of Corn Yields under Extreme Weather Conditions Using Satellite and Meteorological Data |
| title_short | An Artificial Intelligence Approach to Prediction of Corn Yields under Extreme Weather Conditions Using Satellite and Meteorological Data |
| title_sort | artificial intelligence approach to prediction of corn yields under extreme weather conditions using satellite and meteorological data |
| topic | corn yield extreme weather artificial intelligence satellite image meteorological data |
| url | https://www.mdpi.com/2076-3417/10/11/3785 |
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