Science China Life Sciences

, Volume 61, Issue 3, pp 328–339 | Cite as

Crop 3D—a LiDAR based platform for 3D high-throughput crop phenotyping

  • Qinghua GuoEmail author
  • Fangfang Wu
  • Shuxin Pang
  • Xiaoqian Zhao
  • Linhai Chen
  • Jin Liu
  • Baolin Xue
  • Guangcai Xu
  • Le Li
  • Haichun Jing
  • Chengcai Chu
Research Paper


With the growing population and the reducing arable land, breeding has been considered as an effective way to solve the food crisis. As an important part in breeding, high-throughput phenotyping can accelerate the breeding process effectively. Light detection and ranging (LiDAR) is an active remote sensing technology that is capable of acquiring three-dimensional (3D) data accurately, and has a great potential in crop phenotyping. Given that crop phenotyping based on LiDAR technology is not common in China, we developed a high-throughput crop phenotyping platform, named Crop 3D, which integrated LiDAR sensor, high-resolution camera, thermal camera and hyperspectral imager. Compared with traditional crop phenotyping techniques, Crop 3D can acquire multi-source phenotypic data in the whole crop growing period and extract plant height, plant width, leaf length, leaf width, leaf area, leaf inclination angle and other parameters for plant biology and genomics analysis. In this paper, we described the designs, functions and testing results of the Crop 3D platform, and briefly discussed the potential applications and future development of the platform in phenotyping. We concluded that platforms integrating LiDAR and traditional remote sensing techniques might be the future trend of crop high-throughput phenotyping.


crop breeding phenotypic traits data fusion LiDAR high-throughput integrated platform 


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This work was supported by the Strategic Program of Molecular Module-Based Designer Breeding Systems (XDA08040107), and the Instrument Developing Project of the Chinese Academy of Sciences (2014129).


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Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Qinghua Guo
    • 1
    Email author
  • Fangfang Wu
    • 1
    • 2
  • Shuxin Pang
    • 1
  • Xiaoqian Zhao
    • 1
    • 2
  • Linhai Chen
    • 1
    • 2
  • Jin Liu
    • 1
  • Baolin Xue
    • 1
  • Guangcai Xu
    • 1
  • Le Li
    • 3
  • Haichun Jing
    • 1
  • Chengcai Chu
    • 4
  1. 1.State Key Laboratory of Vegetation and Environmental Change, Institute of BotanyChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.College of Life Science and TechnologyBeijing Normal UniversityBeijingChina
  4. 4.State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental BiologyChinese Academy of SciencesBeijingChina

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