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Attention Mechanism-Combined LSTM for Grain Yield Prediction in China Using Multi-Source Satellite Imagery

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成果类型:
期刊论文
作者:
Liu, Fan;Jiang, Xiangtao;Wu, Zhenyu
通讯作者:
Jiang, XT
作者机构:
[Wu, Zhenyu; Jiang, Xiangtao; Liu, Fan; Jiang, XT] Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410018, Peoples R China.
通讯机构:
[Jiang, XT ] C
Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410018, Peoples R China.
语种:
英文
关键词:
grain yield prediction;remote sensing image;deep learning;CBAM;LSTM
期刊:
Sustainability
ISSN:
2071-1050
年:
2023
卷:
15
期:
12
页码:
9210-
基金类别:
This research was funded by the Science and Technology Innovation Program of Hunan Province, grant number: 2023JJ50058; National Key R&D Program of China, grant number: 2022YFD2200505.
机构署名:
本校为第一且通讯机构
院系归属:
计算机与信息工程学院
摘要:
Grain yield prediction affects policy making in various aspects such as agricultural production planning, food security assurance, and adjustment of foreign trade. Accurately predicting grain yield is of great significance in ensuring global food security. This paper is based on the MODIS remote sensing image data products from 2010 to 2020, and adds band information such as vegetation index and temperature to form composite remote sensing data as a dataset. Aiming at the lack of models for large-scale forecasting and the need for human intervention in traditional models, this paper proposes a...

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