Evidence map›Paper›PMID 31261757›Full record

ArticleSensors (Basel, Switzerland)2019

Low-Cost Three-Dimensional Modeling of Crop Plants.

Jorge Martinez-Guanter, Ángela Ribeiro, Gerassimos G Peteinatos, Manuel Pérez-Ruiz, Roland Gerhards, José María Bengochea-Guevara, Jannis Machleb, Dionisio Andújar

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Article
  2. Phenological growth stages of Finger millet (Frontiers in plant science · 2026
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  5. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jorge Martinez-GuanterDepartment of Aerospace Engineering and Fluids Mechanics, Escuela Técnica Superior de Ingeniería Agronómica (ETSIA), Universidad de Sevilla, 41013 Sevilla, Spain.ORCID 0000-0002-4848-816X
Ángela RibeiroCentre for Automation and Robotics, CSIC-UPM, Arganda del Rey, 28500 Madrid, Spain.ORCID 0000-0001-5807-8132
Gerassimos G PeteinatosDepartment of Weed Science, Institute of Phytomedicine, University of Hohenheim, Otto-Sander-Straße 5, 70599 Stuttgart, Germany.
Manuel Pérez-RuizDepartment of Aerospace Engineering and Fluids Mechanics, Escuela Técnica Superior de Ingeniería Agronómica (ETSIA), Universidad de Sevilla, 41013 Sevilla, Spain. manuelperez@us.es.ORCID 0000-0002-3681-1572
Roland GerhardsDepartment of Weed Science, Institute of Phytomedicine, University of Hohenheim, Otto-Sander-Straße 5, 70599 Stuttgart, Germany.
José María Bengochea-GuevaraCentre for Automation and Robotics, CSIC-UPM, Arganda del Rey, 28500 Madrid, Spain.ORCID 0000-0003-4081-7325
Jannis MachlebDepartment of Weed Science, Institute of Phytomedicine, University of Hohenheim, Otto-Sander-Straße 5, 70599 Stuttgart, Germany.
Dionisio AndújarCentre for Automation and Robotics, CSIC-UPM, Arganda del Rey, 28500 Madrid, Spain. d.andujar@csic.es.

Funding

Comunidad de Madrid RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub ("Robótica aplicada a la mejora de la calidadde vida de los ciudadanos. fase IV"; S2018/NMT-4331Spanish Ministry of Economy and Competitiveness AGL2017-83325-C4-3-RSpanish Ministry of Economy and Competitiveness CAS18/00123Spanish Ministry of Economy and Competitiveness RYC-2016-20355
6 · The paper itself

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.

Indexed as

AgricultureCrops, AgriculturalAlgorithmsBiomassImaging, Three-DimensionalPhenotypePlant LeavesVegetablesZea maysplant phenotypingRGB-DStructure from Motion

Identifiers

PMID31261757
PMCPMC6651267

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.