Deep Hierarchical Temporal Data Fusion Improves Yield Estimation under Extreme Climate Stress
Author:
Affiliation:
1. Zhejiang University,College of Biosystems Engineering and Food Science,Hangzhou,China
2. Zhejiang University,Institute of Applied Remote Sensing and Information Technology,Hangzhou,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10640349/10640352/10641993.pdf?arnumber=10641993
Reference17 articles.
1. Influence of extreme weather disasters on global crop production
2. Greater Sensitivity to Drought Accompanies Maize Yield Increase in the U.S. Midwest
3. A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level
4. DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation
5. Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data
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