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J-GLOBAL ID:202202266712787698   整理番号:22A0324482

機械学習と深層学習法を用いたコムギ収量予測における太陽誘導クロロフィル蛍光データの優位性の調査【JST・京大機械翻訳】

Exploring the superiority of solar-induced chlorophyll fluorescence data in predicting wheat yield using machine learning and deep learning methods
著者 (25件):
Liu Yuanyuan
(Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China)
Liu Yuanyuan
(University of Chinese Academy of Sciences, Beijing 100049, China)
Wang Shaoqiang
(Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China)
Wang Shaoqiang
(University of Chinese Academy of Sciences, Beijing 100049, China)
Wang Shaoqiang
(Lab of Reginal Ecological Processes and Environmental Change, School of Geography and Information Engineering, Chinese University of Geosciences (Wuhan), Hubei Province 441000, China)
Wang Xiaobo
(Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China)
Wang Xiaobo
(University of Chinese Academy of Sciences, Beijing 100049, China)
Chen Bin
(Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China)
Chen Bin
(University of Chinese Academy of Sciences, Beijing 100049, China)
Chen Jinghua
(Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China)
Chen Jinghua
(University of Chinese Academy of Sciences, Beijing 100049, China)
Wang Junbang
(Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China)
Wang Junbang
(University of Chinese Academy of Sciences, Beijing 100049, China)
Huang Mei
(Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China)
Huang Mei
(University of Chinese Academy of Sciences, Beijing 100049, China)
Wang Zhaosheng
(Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China)
Wang Zhaosheng
(University of Chinese Academy of Sciences, Beijing 100049, China)
Ma Li
(Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China)
Ma Li
(University of Chinese Academy of Sciences, Beijing 100049, China)
Wang Pengyuan
(Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China)
Wang Pengyuan
(University of Chinese Academy of Sciences, Beijing 100049, China)
Amir Muhammad
(Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China)
Amir Muhammad
(University of Chinese Academy of Sciences, Beijing 100049, China)
Zhu Kai
(Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China)
Zhu Kai
(University of Chinese Academy of Sciences, Beijing 100049, China)

資料名:
Computers and Electronics in Agriculture  (Computers and Electronics in Agriculture)

巻: 192  ページ: Null  発行年: 2022年 
JST資料番号: T0337A  ISSN: 0168-1699  資料種別: 逐次刊行物 (A)
記事区分: 原著論文  発行国: オランダ (NLD)  言語: 英語 (EN)
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