3D Reconstruction of Plant Leaves for High-Throughput Phenotyping

Feiyu Zhu, Suresh Thapa, Tiao Gao, Yufeng Ge, Harkamal Walia, Hongfeng Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Generating 3D digital representations of plants is indispensable for researchers to gain a detailed understanding of plant dynamics. Emerging high-throughput plant phenotyping techniques can capture plant point clouds that, however, often contain imperfections and make it a changeling task to generate accurate 3D reconstructions. We present an end-to-end pipeline to reconstruct surfaces from point clouds of maize and rice plants. In particular, we propose a two-step clustering approach to accurately segment the points of each individual plant component according to maize and rice properties. We further employ surface fitting and edge fitting to ensure the smoothness of resulting surfaces. Realistic visualization results are obtained through post-processing, including texturing and lighting. Our experimental study has explored the parameter space and demonstrated the effectiveness of our pipeline for high-throughput plant phenotyping.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
EditorsYang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4285-4293
Number of pages9
ISBN (Electronic)9781538650356
DOIs
StatePublished - Jan 22 2019
Event2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States
Duration: Dec 10 2018Dec 13 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

Conference

Conference2018 IEEE International Conference on Big Data, Big Data 2018
CountryUnited States
CitySeattle
Period12/10/1812/13/18

Keywords

  • 3D reconstruction
  • high-throughput plant phenotyping
  • point cloud

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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  • Cite this

    Zhu, F., Thapa, S., Gao, T., Ge, Y., Walia, H., & Yu, H. (2019). 3D Reconstruction of Plant Leaves for High-Throughput Phenotyping. In Y. Song, B. Liu, K. Lee, N. Abe, C. Pu, M. Qiao, N. Ahmed, D. Kossmann, J. Saltz, J. Tang, J. He, H. Liu, & X. Hu (Eds.), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 (pp. 4285-4293). [8622428] (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2018.8622428