A Comparative Study of 3D Plant Modeling Systems Based on Low-Cost 2D LiDAR and Kinect

Harold Murcia, David Sanabria, Dehyro Méndez, Manuel G. Forero

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Morphological information of plants is an essential resource for different agricultural machine vision applications, which can be obtained from 3D models through reconstruction algorithms. Three dimensional modeling of a plant is an XYZ spatial representation used to determine its physical parameters from, for example, a point cloud. Currently two low-cost methods have gained popularity in terms of 3D object reconstructions in 360 employing rotating platforms, based on 2D LiDAR and Kinect. In this paper, these two techniques are compared by getting a 3D model of a Dracaena braunii specie and evaluating their performance. The results are shown in terms of their accuracy and time consumption using a Kinect V1 and a LiDAR URG-04LX-UG01, a well-performance low-cost scanning rangefinder from Hokuyo manufacturer. In terms of error calculation, the Kinect-based system probed to be more accurate than the LiDAR-based, with an error less than 20% in all plant measurements. In addition, the point cloud density reached with Kinect was approximately four times higher than with LiDAR. But, acquisition and processing time was about twice than LiDAR system.

Original languageEnglish
Title of host publicationPattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings
EditorsEdgar Roman-Rangel, Ángel Fernando Kuri-Morales, José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, José Arturo Olvera-López
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9783030770037
StatePublished - 2021
Event13th Mexican Conference on Pattern Recognition, MCPR 2021 - Virtual, Online
Duration: 23 Jun 202126 Jun 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12725 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th Mexican Conference on Pattern Recognition, MCPR 2021
CityVirtual, Online


  • 3D modeling
  • Kinect
  • LiDAR
  • Low-cost
  • Phenotyping
  • Point clouds


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