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Combination of UAV and deep learning to estimate wheat yield at ripening stage: The potential of phenotypic features.

J. Peng, D. Wang, W. Zhu, T. Yang, Z. Liu, E. Rezaei, J. Li, Z. Sun, and X. Xin. Int. J. Appl. Earth Obs. Geoinformation, (November 2023)

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Xiaoping Dong

Xiaoping Xie

Xiaoping Zhang

Xiaoping Yang

Xiaoping Che

Other publications of authors with the same name

Comparative Verification of Leaf Area Index Products for Different Grassland Types in Inner Mongolia, China.B. Shen, J. Guo, Z. Li, J. Chen, W. Fang, M. Kussainova, A. Amarjargal, A. Pulatov, R. Yan, O. Anenkhonov and 2 other author(s). Remote. Sens., 15 (19): 4736 (October 2023)Integration of remote sensing and GIS technology to evaluate grassland ecosystem health in north China.Z. Qin, B. Xu, X. Xin, Q. Zhou, H. Zhang, and J. Liu. IGARSS, page 4034-4037. IEEE, (2004)Modeling net primary productivity of terrestrial ecosystems in the semi-arid climate of the Mongolian Plateau using LSWI-based CASA ecosystem model.G. Bao, Y. Bao, Z. Qin, X. Xin, Y. Bao, S. Bayarsaikan, Y. Zhou, and B. Chuntai. Int. J. Appl. Earth Obs. Geoinformation, (2016)A Satellite-Based Model for Simulating Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands.R. Ge, H. He, X. Ren, L. Zhang, P. Li, N. Zeng, G. Yu, L. Zhang, S. Yu, F. Zhang and 9 other author(s). Remote. Sens., 10 (1): 149 (2018)Deep Multi-Order Spatial-Spectral Residual Feature Extractor for Weak Information Mining in Remote Sensing Imagery.X. Zhang, A. Zhang, Y. Sun, J. Wang, H. Pang, J. Peng, Y. Chen, J. Zhang, V. Giannico, T. Legesse and 2 other author(s). Remote. Sens., 16 (11): 1957 (June 2024)Remote sensing products validation activity and observation network in China.R. Jin, X. Li, M. Ma, Y. Ge, T. Che, Q. Xiao, K. Zhao, and X. Xin. IGARSS, page 7623-7626. IEEE, (2016)Modeling Aboveground Biomass in Hulunber Grassland Ecosystem by Using Unmanned Aerial Vehicle Discrete Lidar.D. Wang, X. Xin, Q. Shao, M. Brolly, Z. Zhu, and J. Chen. Sensors, 17 (1): 180 (2017)Evaluation and Intercomparison of MODIS and GEOV1 Global Leaf Area Index Products over Four Sites in North China.Z. Li, H. Tang, B. Zhang, G. Yang, and X. Xin. Sensors, 15 (3): 6196-6216 (2015)Combination of UAV and deep learning to estimate wheat yield at ripening stage: The potential of phenotypic features.J. Peng, D. Wang, W. Zhu, T. Yang, Z. Liu, E. Rezaei, J. Li, Z. Sun, and X. Xin. Int. J. Appl. Earth Obs. Geoinformation, (November 2023)Remote sensing monitoring on dynamic status of grassland productivity and animal loading balance in Northern China.B. Xu, X. Xin, Z. Qin, Z. Shi, H. Liu, Z. Chen, G. Yang, W. Wu, Y. Chen, and X. Wu. IGARSS, page 2306-2309. IEEE, (2004)

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Disambiguation of "Xin, Xiaoping"

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Combination of UAV and deep learning to estimate wheat yield at ripening stage: The potential of phenotypic features.

Please choose a person to relate this publication to

Xiaoping Dong

Xiaoping Xie

Xiaoping Zhang

Xiaoping Yang

Xiaoping Che

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Xin, Xiaoping"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Combination of UAV and deep learning to estimate wheat yield at ripening stage: The potential of phenotypic features.

Please choose a person to relate this publication to

Xiaoping Dong

Xiaoping Xie

Xiaoping Zhang

Xiaoping Yang

Xiaoping Che

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

Combination of UAV and deep learning to estimate wheat yield at ripening stage: The potential of phenotypic features.