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

被引：0
|
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;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [21] Evaluating different approaches to non-destructive nitrogen status diagnosis of rice using portable RapidSCAN active canopy sensor
    Junjun Lu
    Yuxin Miao
    Wei Shi
    Jingxin Li
    Fei Yuan
    Scientific Reports, 7
  • [22] Evaluating different approaches to non-destructive nitrogen status diagnosis of rice using portable RapidSCAN active canopy sensor
    Lu, Junjun
    Miao, Yuxin
    Shi, Wei
    Li, Jingxin
    Yuan, Fei
    SCIENTIFIC REPORTS, 2017, 7
  • [23] Non-destructive potato petiole nitrate-nitrogen prediction using chlorophyll meter and multi-source data fusion with machine learning
    Wakahara, Seiya
    Miao, Yuxin
    Mcnearney, Matthew
    Rosen, Carl J.
    EUROPEAN JOURNAL OF AGRONOMY, 2025, 164
  • [24] Machine learning combined with multi-source data fusion for rapid quality assessment of yellow rice wine with different aging years
    Zhang, Zhi-Tong
    Li, Yu
    Bai, Lei
    Chen, Pan
    Jiang, Yue
    Qi, Yali
    Guan, Huanhuan
    Liang, Yaxuan
    Yuan, Dongping
    Lu, Tulin
    Yan, Guojun
    MICROCHEMICAL JOURNAL, 2024, 199
  • [25] POI Recommendation Method Based on Multi-Source Information Fusion Using Deep Learning in Location-Based Social Networks
    Sun, Liqiang
    JOURNAL OF INFORMATION PROCESSING SYSTEMS, 2021, 17 (02): : 352 - 368
  • [26] Developing an Active Canopy Sensor-Based Integrated Precision Rice Management System for Improving Grain Yield and Quality, Nitrogen Use Efficiency, and Lodging Resistance
    Lu, Junjun
    Wang, Hongye
    Miao, Yuxin
    Zhao, Liqin
    Zhao, Guangming
    Cao, Qiang
    Kusnierek, Krzysztof
    REMOTE SENSING, 2022, 14 (10)
  • [27] Improving the prediction performance of leaf water content by coupling multi-source data with machine learning in rice (Oryza sativa L.)
    Zhang, Xuenan
    Xu, Haocong
    She, Yehong
    Hu, Chao
    Zhu, Tiezhong
    Wang, Lele
    Wu, Liquan
    You, Cuicui
    Ke, Jian
    Zhang, Qiangqiang
    He, Haibing
    PLANT METHODS, 2024, 20 (01)
  • [28] Improving the prediction performance of leaf water content by coupling multi-source data with machine learning in rice (Oryza sativa L.)
    Xuenan Zhang
    Haocong Xu
    Yehong She
    Chao Hu
    Tiezhong Zhu
    Lele Wang
    Liquan Wu
    Cuicui You
    Jian Ke
    Qiangqiang Zhang
    Haibing He
    Plant Methods, 20
  • [29] Enhancing the streamflow simulation of a process-based hydrological model using machine learning and multi-source data
    Lei, Huajin
    Li, Hongyi
    Hu, Wanpin
    ECOLOGICAL INFORMATICS, 2024, 82
  • [30] Site Selection Prediction for Coffee Shops Based on Multi-Source Space Data Using Machine Learning Techniques
    Zhao, Jiaqi
    Zong, Baiyi
    Wu, Ling
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2023, 12 (08)
12345