A deep learning framework combining CNN and GRU for improving wheat yield estimates using time series remotely sensed multi-variables-Reference-Cited by-同舟云学术

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A deep learning framework combining CNN and GRU for improving wheat yield estimates using time series remotely sensed multi-variables

Published:2023-03 Issue: Volume:206 Page:107705
ISSN:0168-1699
Container-title:Computers and Electronics in Agriculture
language:en
Short-container-title:Computers and Electronics in Agriculture

Author:

Wang Jie,Wang Pengxin,Tian Huiren,Tansey Kevin,Liu Junming,Quan Wenting

Funder

Royal Society

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference49 articles.

1. Deep learning-based sentiment classification of evaluative text based on multi-feature fusion;Abdi;Inform. Process. Manag.,2019

2. A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data;Becker-Reshef;Remote Sens. Environ.,2010

3. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics;Bolton;Agr. Forest Meteorol.,2013

4. Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches;Cai;Agr. Forest Meteorol.,2019

5. Wheat yield predictions at a county and field scale with deep learning, machine learning, and google earth engine;Cao;Eur. J. Agron.,2021

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