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.

中文翻译：

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利用卫星和气象数据预测极端天气条件下玉米产量的人工智能方法

本文描述了美国中西部 (US) 在极端天气条件下优化玉米产量预测模型的开发。我们使用卫星图像和气象数据在主要生长期测试了六种不同的人工智能 (AI) 模型。为了检查极端天气事件的影响，我们通过考虑玉米生长的特征来定义干旱和热浪，并从历史数据库中选择案例进行敏感性测试。特别是，我们对深度神经网络 (DNN) 模型的超参数进行了优化，以确保最佳配置以提高精度。干旱案例的结果表明，就均方根误差 (RMSE) 而言，我们的 DNN 模型比其他五个 AI 模型的准确度高出约 51-98%。对于热浪情况，我们的 DNN 模型在 RMSE 方面的准确度提高了大约 30-77%。干旱情况的相关系数为 0.954，热浪情况的相关系数为 0.887-0.914。此外，尽管热浪持续时间增加，但我们的 DNN 模型的准确性非常稳定。这表明优化后的 DNN 模型可以为极端天气条件下的玉米产量提供可靠的预测，并且可以在未来的工作中扩展到其他各种作物的预测模型。