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Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat.

J. Jiang, Z. Zhang, Q. Cao, Y. Liang, B. Krienke, Y. Tian, Y. Zhu, W. Cao, and X. Liu. Remote. Sens., 12 (22): 3684 (2020)

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Yongchao Tian

Tian Tian

Yongchao Xu

Xiaoyong Tian

Min Tian

Other publications of authors with the same name

Improved Estimation of Leaf Chlorophyll Content from Non-Noon Reflectance Spectra of Wheat Canopies by Avoiding the Effect of Soil Background.D. Li, M. Jia, J. Zhu, X. Yao, Y. Tian, Y. Zhu, W. Cao, and T. Cheng. Agro-Geoinformatics, page 1-4. IEEE, (2018)Optimizing rice in-season nitrogen topdressing by coupling experimental and modeling data with machine learning algorithms.J. Zhang, Z. Fu, K. Zhang, J. Li, Q. Cao, Y. Tian, Y. Zhu, W. Cao, and X. Liu. Comput. Electron. Agric., (June 2023)Improving wheat yield prediction integrating proximal sensing and weather data with machine learning.G. Ruan, X. Li, F. Yuan, D. Cammarano, S. Ata-UI-Karim, X. Liu, Y. Tian, Y. Zhu, W. Cao, and Q. Cao. Comput. Electron. Agric., (2022)A newly developed method to extract the optimal hyperspectral feature for monitoring leaf biomass in wheat.M. Jia, W. Li, K. Wang, C. Zhou, T. Cheng, Y. Tian, Y. Zhu, W. Cao, and X. Yao. Comput. Electron. Agric., (2019)Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat.J. Jiang, Z. Zhang, Q. Cao, Y. Liang, B. Krienke, Y. Tian, Y. Zhu, W. Cao, and X. Liu. Remote. Sens., 12 (22): 3684 (2020)Predicting Rice Grain Yield Based on Dynamic Changes in Vegetation Indexes during Early to Mid-Growth Stages.K. Zhang, X. Ge, P. Shen, W. Li, X. Liu, Q. Cao, Y. Zhu, W. Cao, and Y. Tian. Remote. Sens., 11 (4): 387 (2019)Early Detection of Powdery Mildew Disease and Accurate Quantification of Its Severity Using Hyperspectral Images in Wheat.I. Khan, H. Liu, W. Li, A. Cao, X. Wang, H. Liu, T. Cheng, Y. Tian, Y. Zhu, W. Cao and 1 other author(s). Remote. Sens., 13 (18): 3612 (2021)A deep learning method for oriented and small wheat spike detection (OSWSDet) in UAV images.J. Zhao, J. Yan, T. Xue, S. Wang, X. Qiu, X. Yao, Y. Tian, Y. Zhu, W. Cao, and X. Zhang. Comput. Electron. Agric., (2022)Detection of wheat height using optimized multi-scan mode of LiDAR during the entire growth stages.T. Guo, Y. Fang, T. Cheng, Y. Tian, Y. Zhu, Q. Chen, X. Qiu, and X. Yao. Comput. Electron. Agric., (2019)Rapid Mosaicking of Unmanned Aerial Vehicle (UAV) Images for Crop Growth Monitoring Using the SIFT Algorithm.J. Zhao, X. Zhang, C. Gao, X. Qiu, Y. Tian, Y. Zhu, and W. Cao. Remote Sensing, 11 (10): 1226 (2019)

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Disambiguation of "Tian, Yongchao"

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Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat.

Please choose a person to relate this publication to

Yongchao Tian

Tian Tian

Yongchao Xu

Xiaoyong Tian

Min Tian

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Tian, Yongchao"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat.

Please choose a person to relate this publication to

Yongchao Tian

Tian Tian

Yongchao Xu

Xiaoyong Tian

Min Tian

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat.