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Article

Low-Cost Three-Dimensional Modeling of Crop Plants

1
Department of Aerospace Engineering and Fluids Mechanics, Escuela Técnica Superior de Ingeniería Agronómica (ETSIA), Universidad de Sevilla, 41013 Sevilla, Spain
2
Centre for Automation and Robotics, CSIC-UPM, Arganda del Rey, 28500 Madrid, Spain
3
Department of Weed Science, Institute of Phytomedicine, University of Hohenheim, Otto-Sander-Straße 5, 70599 Stuttgart, Germany
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(13), 2883; https://doi.org/10.3390/s19132883
Received: 22 April 2019 / Revised: 20 June 2019 / Accepted: 26 June 2019 / Published: 28 June 2019
(This article belongs to the Special Issue Sensors in Agriculture 2019)
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. View Full-Text
Keywords: plant phenotyping; RGB-D; Structure from Motion; RGB-D plant phenotyping; RGB-D; Structure from Motion; RGB-D
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MDPI and ACS Style

Martinez-Guanter, J.; Ribeiro, Á.; Peteinatos, G.G.; Pérez-Ruiz, M.; Gerhards, R.; Bengochea-Guevara, J.M.; Machleb, J.; Andújar, D. Low-Cost Three-Dimensional Modeling of Crop Plants. Sensors 2019, 19, 2883. https://doi.org/10.3390/s19132883

AMA Style

Martinez-Guanter J, Ribeiro Á, Peteinatos GG, Pérez-Ruiz M, Gerhards R, Bengochea-Guevara JM, Machleb J, Andújar D. Low-Cost Three-Dimensional Modeling of Crop Plants. Sensors. 2019; 19(13):2883. https://doi.org/10.3390/s19132883

Chicago/Turabian Style

Martinez-Guanter, Jorge, Ángela Ribeiro, Gerassimos G. Peteinatos, Manuel Pérez-Ruiz, Roland Gerhards, José M. Bengochea-Guevara, Jannis Machleb, and Dionisio Andújar. 2019. "Low-Cost Three-Dimensional Modeling of Crop Plants" Sensors 19, no. 13: 2883. https://doi.org/10.3390/s19132883

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