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A Comparison Between Major Artificial Intelligence Models for Crop Yield Prediction: Case Study of the Midwestern United States, 2006–2015

1
Geomatics Research Institute, Pukyong National University, Busan 48513, Korea
2
Center for Climate Physics, Institute for Basic Science, and Department of Atmospheric Sciences, Pusan National University, Busan 46241, Korea
3
Department of Geoinformatic Engineering, Inha University, Incheon 22212, Korea
4
Department of Applied Plant Science, Chonnam National University, Gwangju 61186, Korea
5
Department of Environment, Energy, and Geoinfomatics, Sejong University, Seoul 05006, Korea
6
Department of Spatial Information Engineering, Pukyong National University, Busan 48513, Korea
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(5), 240; https://doi.org/10.3390/ijgi8050240
Received: 10 April 2019 / Revised: 11 May 2019 / Accepted: 17 May 2019 / Published: 21 May 2019
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Abstract

This paper compares different artificial intelligence (AI) models in order to develop the best crop yield prediction model for the Midwestern United States (US). Through experiments to examine the effects of phenology using three different periods, we selected the July–August (JA) database as the best months to predict corn and soybean yields. Six different AI models for crop yield prediction are tested in this research. Then, a comprehensive and objective comparison is conducted between the AI models. Particularly for the deep neural network (DNN) model, we performed an optimization process to ensure the best configurations for the layer structure, cost function, optimizer, activation function, and drop-out ratio. In terms of mean absolute error (MAE), our DNN model with the JA database was approximately 21–33% and 17–22% more accurate for corn and soybean yields, respectively, than the other five AI models. This indicates that corn and soybean yields for a given year can be forecasted in advance, at the beginning of September, approximately a month or more ahead of harvesting time. A combination of the optimized DNN model and spatial statistical methods should be investigated in future work, to mitigate partly clustered errors in some regions. View Full-Text
Keywords: crop yield; artificial intelligence; satellite product; meteorological dataset crop yield; artificial intelligence; satellite product; meteorological dataset
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Kim, N.; Ha, K.-J.; Park, N.-W.; Cho, J.; Hong, S.; Lee, Y.-W. A Comparison Between Major Artificial Intelligence Models for Crop Yield Prediction: Case Study of the Midwestern United States, 2006–2015. ISPRS Int. J. Geo-Inf. 2019, 8, 240.

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