气候变化领域集成服务门户(CCPortal): DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation

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DOI	10.1088/1748-9326/ab66cb
	DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation
	Lin T.; Zhong R.; Wang Y.; Xu J.; Jiang H.; Xu J.; Ying Y.; Rodriguez L.; Ting K.C.; Li H.
发表日期	2020
ISSN	17489318
卷号	15 期号:3
英文摘要	Large-scale crop yield estimation is critical for understanding the dynamics of global food security. Understanding and quantifying the temporal cumulative effect of crop growth and spatial variances across different regions remains challenging for large-scale crop yield estimation. In this study, a deep spatial-temporal learning framework, named DeepCropNet (DCN), has been developed to hierarchically capture the features for county-level corn yield estimation. The temporal features are learned by an attention-based long short-term memory network and the spatial features are learned by the multi-task learning (MTL) output layers. The DCN model has been applied to quantify the relationship between meteorological factors and the county-level corn yield in the US Corn Belt from 1981 to 2016. Three meteorological factors, including growing degree days, killing degree days, and precipitation, are used as time-series inputs. The results show that DCN provides an improved estimation accuracy (RMSE = 0.82 Mg ha-1) as compared to that of conventional methods such as LASSO (RMSE = 1.14 Mg ha-1) and Random Forest (RMSE = 1.05 Mg ha-1). Temporally, the attention values computed from the temporal learning module indicate that DCN captures the temporal cumulative effect and this temporal pattern is consistent across all states. Spatially, the spatial learning module improves the estimation accuracy based on the regional specific features captured by the MTL mechanism. The study highlights that the DCN model provides a promising spatial-temporal learning framework for corn yield estimation under changing meteorological conditions across large spatial regions. © 2020 The Author(s). Published by IOP Publishing Ltd.
英文关键词	attention mechanism; corn; deep learning; LSTM; multi-task learning; yield estimation
语种	英语
scopus关键词	Crops; Decision trees; Food supply; Learning systems; Linearization; Long short-term memory; Magnesium; Multi-task learning; Attention mechanisms; Conventional methods; corn; Global food security; LSTM; Meteorological condition; Meteorological factors; Yield estimation; Deep learning; algorithm; crop yield; estimation method; growth; maize; meteorology; spatiotemporal analysis; Corn Belt; United States; Zea mays
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/154168
作者单位	College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, 310058, China; International Campus, Zhejiang University, Haining, Zhejiang, 314400, China; China Academy of Electronic and Information Technology, Beijing, 100041, China; Faculty of Agricultural and Food Science, Zhejiang AandF University, Hangzhou, Zhejiang, 311300, China; Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States; School of Geosciences and Info-Physics, Central South University, Changsha, 410083, China; Henan Laboratory of Spatial Information Application on Ecological Environment Protection, Zhengzhou, 450000, China
推荐引用方式 GB/T 7714	Lin T.,Zhong R.,Wang Y.,et al. DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation[J],2020,15(3).
APA	Lin T..,Zhong R..,Wang Y..,Xu J..,Jiang H..,...&Li H..(2020).DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation.Environmental Research Letters,15(3).
MLA	Lin T.,et al."DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation".Environmental Research Letters 15.3(2020).