Improving active canopy sensor-based in-season rice nitrogen status diagnosis and recommendation using multi-source data fusion with machine learning

被引：0
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作者：
Lu, Junjun [1 ,2 ]
Dai, Erfu [1 ]
Miao, Yuxin [3 ]
Kusnierek, Krzysztof [4 ]
机构：
[1] Lhasa Plateau Ecosystem Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing,100101, China
[2] School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo,454000, China
[3] Precision Agriculture Center, Department of Soil, Water and Climate, University of Minnesota, St. Paul,MN,55108, United States
[4] Center for Precision Agriculture, Norwegian Institute of Bioeconomy Research (NIBIO), Nylinna 226, Kapp,2849, Norway
来源：
Journal of Cleaner Production | 2022年 / 380卷
关键词：
This research was funded by Norwegian Ministry of Foreign Affairs (SINOGRAIN II; CHN-17/0019; Doctoral Fund Program of Henan Polytechnic University ( B2019-5 ); Key Scientific Research Projects of Higher Education Institutions in Henan Province ( 20A210013 ); and Henan Scientific and Technological Projection ( 202102110032 ). We also would like to thank the help from Yuan Gao; Wen Yang; Huamin Zhu and Fengyan Liu at Jiansanjiang Institute of Agricultural Science; Cheng Liu and Guangming Zhao at Qixing Research and Development Center; Guojun Li at Jiansanjiang Agriculture Bureau; Dr. Qiang Cao from Nanjing Agricultural University; Dr. Zhichao Chen; Dr. Chengyuan Hao and Yu Zhang from Henan Polytechnic University; and Yinkun Yao; Hongye Wang; Shanyu Huang; Jianning Shen; Wei Shi; Weichao Sun and Hainie Zha from China Agricultural University.This research was funded by Norwegian Ministry of Foreign Affairs (SINOGRAIN II; CHN-17/0019); Doctoral Fund Program of Henan Polytechnic University (B2019-5); Key Scientific Research Projects of Higher Education Institutions in Henan Province (20A210013); and Henan Scientific and Technological Projection (202102110032). We also would like to thank the help from Yuan Gao; Weichao Sun and Hainie Zha from China Agricultural University;
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