Extraction of Wheat Spike Phenotypes From Field-Collected Lidar Data and Exploration of Their Relationships With Wheat Yield-Reference-Cited by-同舟云学术

Extraction of Wheat Spike Phenotypes From Field-Collected Lidar Data and Exploration of Their Relationships With Wheat Yield

Published:2023 Issue: Volume:61 Page:1-13
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Liu Zhonghua¹,Jin Shichao²,Liu Xiaoqiang¹,Yang Qiuli³,Li Qing²,Zang Jingrong²,Li Zhaofeng⁴,Hu Tianyu¹,Guo Zifeng⁵,Wu Jin⁶,Jiang Dong²,Su Yanjun¹^ORCID

Affiliation:

1. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China

2. Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, College of Agriculture, Nanjing Agricultural University, Nanjing, China

3. College of Geography and Remote Sensing Science, Xinjiang University, Urumqi, China

4. Key Laboratory of Oasis Eco-Agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi, China

5. China National Botanical Garden, Beijing, China

6. Research Area of Ecology and Biodiversity, School of Biological Sciences, The University of Hong Kong, Hong Kong, China

Funder

Strategic Priority Research Program of Chinese Academy of Sciences

Fundamental Research Funds for the Central Universities

Open Project of Key Laboratory of Oasis Eco-Agriculture, Xinjiang Production and Construction Corps

National Natural Science Foundation of China

Hong Kong University (HKU) Seed Funding for Strategic Interdisciplinary Research Scheme

Innovation Technology Fund (funding support to State Key Laboratories in Hong Kong of Agrobiotechnology) of the Hong Kong Special Administrative Region (HKSAR), China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10319834.pdf?arnumber=10319834

Reference71 articles.

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4. Point-voxel CNN for efficient 3D deep learning;liu;arXiv 1907 03739,2019

5. Manipulation and prediction of spike morphology traits for the improvement of grain yield in wheat

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