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Related Experiment Video

Updated: Jan 22, 2026

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Low-Cost Three-Dimensional Modeling of Crop Plants.

Jorge Martinez-Guanter1, Ángela Ribeiro2, Gerassimos G Peteinatos3

  • 1Department of Aerospace Engineering and Fluids Mechanics, Escuela Técnica Superior de Ingeniería Agronómica (ETSIA), Universidad de Sevilla, 41013 Sevilla, Spain.

Sensors (Basel, Switzerland)
|July 3, 2019
PubMed
Summary

Two low-cost 3D plant modeling systems, Structure from Motion (SfM) and RGB-Depth Kinect v2, were evaluated for plant phenotyping. Both methods showed accurate results, with SfM offering better detail reconstruction and height accuracy for agronomic management.

Keywords:
RGB-DStructure from Motionplant phenotyping

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Plant Biology

Background:

  • Plant modeling is crucial for understanding plant development and optimizing agronomic management.
  • Accurate 3D reconstruction of plants is essential for advanced plant phenotyping.
  • Assessing the cost-effectiveness and accuracy of low-cost 3D modeling systems is vital for practical implementation.

Purpose of the Study:

  • To evaluate the suitability of two low-cost 3D plant reconstruction systems: Structure from Motion (SfM) and an RGB-Depth Kinect v2 sensor.
  • To compare the accuracy, detail reconstruction, and processing time of SfM and Kinect v2 for modeling diverse crops.
  • To validate the generated 3D models against ground truth data for key plant parameters.

Main Methods:

  • Utilized a low-cost Structure from Motion (SfM) technique for 3D plant crop reconstruction.
  • Employed an RGB-Depth Kinect v2 sensor with a similar image acquisition procedure for 3D reconstruction.
  • Processed data to create dense point clouds and 3D-polygon meshes for maize, sugar beet, and sunflower plants.

Main Results:

  • Both SfM and Kinect v2 methods demonstrated strong consistency and good correlations with ground truth data (plant height, leaf area index, dry biomass).
  • The SfM method showed slightly superior performance in reconstructing fine details and estimating plant height accurately.
  • While SfM processing was relatively fast, the RGB-D (Kinect v2) method offered faster 3D model creation.

Conclusions:

  • Both low-cost 3D modeling systems (SfM and Kinect v2) are suitable for plant reconstruction in various scenarios, offering a favorable time-cost relationship.
  • These systems provide robust results with great potential for both indoor and outdoor plant phenotyping and agronomic decision-making.
  • The study highlights the viability of accessible technologies for detailed plant analysis, supporting broader adoption in agricultural research.