Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach

As the world's leading corn producer, the United States supplies more than 30% of the global corn production. Accurate and timely estimation of corn yield is therefore essential for commodity trading and global food security. Recently, several deep learning models have been explored for corn yi...

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Bibliographic Details
Published in:	Remote sensing of environment 2021-06, Vol.259, p.112408, Article 112408
Main Authors:	Ma, Yuchi, Zhang, Zhou, Kang, Yanghui, Özdoğan, Mutlu
Format:	Article
Language:	English
Subjects:	Agricultural production agricultural productivity Bayesian analysis Bayesian neural network Bayesian theory climate confidence interval Confidence intervals Corn Corn belt Corn Belt region Corn yield Corn yield prediction Crop production Crop yield Deep learning environment Environmental effects Environmental stress Evaluation Extreme weather Food security Growing season heat Learning algorithms Machine learning Mathematical models Neural networks prediction Prediction models Remote sensing Satellite observation satellites Seasonal variability Seasons Soil properties time series analysis Training uncertainty Uncertainty analysis Uncertainty estimation Vegetables Water stress Weather yield forecasting
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