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
  • [31] Global air-sea CO2 flux inversion based on multi-source data fusion and machine learning
    Chen, Yongqiang
    Wang, Siyi
    He, Wenping
    CHAOS SOLITONS & FRACTALS, 2025, 192
  • [32] Advanced data-driven FBG sensor-based pavement monitoring system using multi-sensor data fusion and an unsupervised learning approach
    Golmohammadi, Ali
    Hernando, David
    van den Bergh, Wim
    Hasheminejad, Navid
    MEASUREMENT, 2025, 242
  • [33] Using Multi-Sensor Data Fusion Techniques and Machine Learning Algorithms for Improving UAV-Based Yield Prediction of Oilseed Rape
    Zhu, Hongyan
    Liang, Shikai
    Lin, Chengzhi
    He, Yong
    Xu, Jun-Li
    DRONES, 2024, 8 (11)
  • [34] Securing China's rice harvest: unveiling dominant factors in production using multi-source data and hybrid machine learning models
    Mokhtar, Ali
    He, Hongming
    Nabil, Mohsen
    Kouadri, Saber
    Salem, Ali
    Elbeltagi, Ahmed
    SCIENTIFIC REPORTS, 2024, 14 (01):
  • [35] Implicit modelling and dynamic update of tunnel unfavourable geology based on multi-source data fusion using support vector machine
    Yang, Binru
    Ding, Yulin
    Zhu, Qing
    Zhang, Liguo
    Wu, Haoyu
    Guo, Yongxin
    Liu, Mingwei
    Wang, Wei
    GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS, 2024, 18 (01) : 257 - 274
  • [36] PREDICTING CARBON STORAGE IN THE YUNNAN-KWEICHOW PLATEAU WETLANDS USING A FUSION OF MULTI-SOURCE REMOTE SENSING DATA AND MACHINE LEARNING
    Cai, Fangliang
    Tang, Bo-Hui
    Jiang, Xinran
    Huang, Liang
    Fu, Zhitao
    Fan, Dong
    IGARSS 2024-2024 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, IGARSS 2024, 2024, : 4806 - 4809
  • [37] Integrated UAV-Based Multi-Source Data for Predicting Maize Grain Yield Using Machine Learning Approaches
    Guo, Yahui
    Zhang, Xuan
    Chen, Shouzhi
    Wang, Hanxi
    Jayavelu, Senthilnath
    Cammarano, Davide
    Fu, Yongshuo
    REMOTE SENSING, 2022, 14 (24)
  • [38] Improving the spatial and temporal estimation of ecosystem respiration using multi-source data and machine learning methods in a rainfed winter wheat cropland
    Lu, Ruhua
    Zhang, Pei
    Fu, Zhaopeng
    Jiang, Jie
    Wu, Jiancheng
    Cao, Qiang
    Tian, Yongchao
    Zhu, Yan
    Cao, Weixing
    Liu, Xiaojun
    SCIENCE OF THE TOTAL ENVIRONMENT, 2023, 871
  • [39] Simulation of satellite reflectance data using high-frequency ground based hyperspectral canopy measurements for in-season estimation of grain yield and grain nitrogen status in winter wheat
    Prey, Lukas
    Schmidhalter, Urs
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2019, 149 : 176 - 187
  • [40] Improving spatio-temporal precipitation estimates in data scarce river basins: an application of machine learning-based multi-source data merging
    Mohammed, Juhar
    Mengiste, Yenesew
    Singh, Vijay P.
    STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2023, 37 (04) : 1353 - 1369
12345