Low-Cost Three-Dimensional Modeling of Crop Plants

Martínez Guanter, Jorge and Ribeiro Seijas, Angela and Peteinatos, Gerassimos G. and Pérez Ruiz, Manuel and Gerhards, Roland and Bengochea Guevara, José María and Machleb, Jannis and Andújar Sánchez, Dionisio (2019). Low-Cost Three-Dimensional Modeling of Crop Plants. "Sensors", v. 19 ; pp. 1-14. ISSN 1424-8220. https://doi.org/10.3390/s19132883.

Description

Title: Low-Cost Three-Dimensional Modeling of Crop Plants
Author/s:
  • Martínez Guanter, Jorge
  • Ribeiro Seijas, Angela
  • Peteinatos, Gerassimos G.
  • Pérez Ruiz, Manuel
  • Gerhards, Roland
  • Bengochea Guevara, José María
  • Machleb, Jannis
  • Andújar Sánchez, Dionisio
Item Type: Article
Título de Revista/Publicación: Sensors
Date: June 2019
ISSN: 1424-8220
Volume: 19
Subjects:
Freetext Keywords: plant phenotyping; RGB-D; Structure from Motion; RGB-D
Faculty: Centro de Automática y Robótica (CAR) UPM-CSIC
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Plant modeling can provide a more detailed overview regarding the basis of plant development throughout the life cycle. Three-dimensional processing algorithms are rapidly expanding in plant phenotyping programmes and in decision-making for agronomic management. Several methods have already been tested, but for practical implementations the trade-off between equipment cost, computational resources needed and the fidelity and accuracy in the reconstruction of the end-details needs to be assessed and quantified. This study examined the suitability of two low-cost systems for plant reconstruction. A low-cost Structure from Motion (SfM) technique was used to create 3D models for plant crop reconstruction. In the second method, an acquisition and reconstruction algorithm using an RGB-Depth Kinect v2 sensor was tested following a similar image acquisition procedure. The information was processed to create a dense point cloud, which allowed the creation of a 3D-polygon mesh representing every scanned plant. The selected crop plants corresponded to three different crops (maize, sugar beet and sunflower) that have structural and biological differences. The parameters measured from the model were validated with ground truth data of plant height, leaf area index and plant dry biomass using regression methods. The results showed strong consistency with good correlations between the calculated values in the models and the ground truth information. Although, the values obtained were always accurately estimated, differences between the methods and among the crops were found. The SfM method showed a slightly better result with regard to the reconstruction the end-details and the accuracy of the height estimation. Although the use of the processing algorithm is relatively fast, the use of RGB-D information is faster during the creation of the 3D models. Thus, both methods demonstrated robust results and provided great potential for use in both for indoor and outdoor scenarios. Consequently, these low-cost systems for 3D modeling are suitable for several situations where there is a need for model generation and also provide a favourable time-cost relationship.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainRYC-2016-20355UnspecifiedUnspecifiedUnspecified
Government of SpainAGL2017-83325-C4-3-RUnspecifiedUnspecifiedDiseño, desarrollo y evaluación de sistemas autónomos para la inspección terrestre efectiva y la actuación precisa en cultivos extensivos y leñosos
Government of SpainAGL2017-83325-C4-1-RUnspecifiedUnspecifiedNuevas herramientas tecnológicas, agronómicas e informáticas para la gestión de malas hierbas
Government of SpainCAS18/00123UnspecifiedUnspecifiedUnspecified
Madrid Regional GovernmentS2018/NMT-4331RoboCity2030-DIH-CMUnspecifiedRobótica aplicada a la mejora de la calidadde vida de los ciudadanos. Fase IV

More information

Item ID: 67172
DC Identifier: https://oa.upm.es/67172/
OAI Identifier: oai:oa.upm.es:67172
DOI: 10.3390/s19132883
Official URL: https://www.mdpi.com/1424-8220/19/13/2883
Deposited by: Memoria Investigacion
Deposited on: 26 Aug 2022 06:01
Last Modified: 30 Nov 2022 09:00
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