A novel LiDAR-Based instrument for high-throughput, 3D measurement of morphological traits in maize and sorghum

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

Research output: Contribution to journalArticlepeer-review

61 Scopus citations


Recently, imaged-based approaches have developed rapidly for high-throughput plant phenotyping (HTPP). Imaging reduces a 3D plant into 2D images, which makes the retrieval of plant morphological traits challenging. We developed a novel LiDAR-based phenotyping instrument to generate 3D point clouds of single plants. The instrument combined a LiDAR scanner with a precision rotation stage on which an individual plant was placed. A LabVIEW program was developed to control the scanning and rotation motion, synchronize the measurements from both devices, and capture a 360° view point cloud. A data processing pipeline was developed for noise removal, voxelization, triangulation, and plant leaf surface reconstruction. Once the leaf digital surfaces were reconstructed, plant morphological traits, including individual and total leaf area, leaf inclination angle, and leaf angular distribution, were derived. The system was tested with maize and sorghum plants. The results showed that leaf area measurements by the instrument were highly correlated with the reference methods (R2 > 0.91 for individual leaf area; R2 > 0.95 for total leaf area of each plant). Leaf angular distributions of the two species were also derived. This instrument could fill a critical technological gap for indoor HTPP of plant morphological traits in 3D.

Original languageEnglish (US)
Article number1187
JournalSensors (Switzerland)
Issue number4
StatePublished - Apr 13 2018


  • 3D point cloud
  • High-throughput plant phenotyping
  • Leaf angular distribution
  • Leaf area
  • Leaf inclination angle

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering


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