Skip to main navigation
Skip to search
Skip to main content
Illinois Experts Home
LOGIN & Help
Link opens in a new tab
Search content at Illinois Experts
Home
Profiles
Research units
Research & Scholarship
Datasets
Honors
Press/Media
Activities
DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation
Tao Lin
, Renhai Zhong
, Yudi Wang
, Jinfan Xu
, Hao Jiang
, Jialu Xu
, Yibin Ying
,
Luis Rodriguez
, K. C. Ting
, Haifeng Li
Agricultural and Biological Engineering
Information Trust Institute
National Center for Supercomputing Applications (NCSA)
Institute for Sustainability, Energy, and Environment
Research output
:
Contribution to journal
›
Article
›
peer-review
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Earth and Planetary Sciences
Crop Growth
50%
Crop Yield
100%
Cumulative Effects
100%
Food Security
50%
Long Short-Term Memory Network
50%
Meteorological Factors
100%
Multitask Learning
100%
Time Series
50%
Computer Science
Conventional Method
33%
Estimation Accuracy
66%
Learning Framework
100%
Learning Mechanism
33%
Long Short-Term Memory Network
33%
Multitask Learning
66%
Random Decision Forest
33%
Temporal Feature
33%
United States of America
33%
Keyphrases
Attention-based Long Short-term Memory
25%
Spatial-temporal Learning
100%