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Open AccessArticle

Three-Dimensional Modeling of Weed Plants Using Low-Cost Photogrammetry

1
Institute of Agricultural Sciences, CSIC, 28006 Madrid, Spain
2
National Museum of Natural Sciences, CSIC, 28006 Madrid, Spain
3
Centre for Automation and Robotics, CSIC-UPM, Arganda del Rey, 28500 Madrid, Spain
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(4), 1077; https://doi.org/10.3390/s18041077
Received: 27 February 2018 / Revised: 24 March 2018 / Accepted: 30 March 2018 / Published: 3 April 2018
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Spain 2018)
Sensing advances in plant phenotyping are of vital importance in basic and applied plant research. Plant phenotyping enables the modeling of complex shapes, which is useful, for example, in decision-making for agronomic management. In this sense, 3D processing algorithms for plant modeling is expanding rapidly with the emergence of new sensors and techniques designed to morphologically characterize. However, there are still some technical aspects to be improved, such as an accurate reconstruction of end-details. This study adapted low-cost techniques, Structure from Motion (SfM) and MultiView Stereo (MVS), to create 3D models for reconstructing plants of three weed species with contrasting shape and plant structures. Plant reconstruction was developed by applying SfM algorithms to an input set of digital images acquired sequentially following a track that was concentric and equidistant with respect to the plant axis and using three different angles, from a perpendicular to top view, which guaranteed the necessary overlap between images to obtain high precision 3D models. With this information, a dense point cloud was created using MVS, from which a 3D polygon mesh representing every plants’ shape and geometry was generated. These 3D models were validated with ground truth values (e.g., plant height, leaf area (LA) and plant dry biomass) using regression methods. The results showed, in general, a good consistency in the correlation equations between the estimated values in the models and the actual values measured in the weed plants. Indeed, 3D modeling using SfM algorithms proved to be a valuable methodology for weed phenotyping, since it accurately estimated the actual values of plant height and LA. Additionally, image processing using the SfM method was relatively fast. Consequently, our results indicate the potential of this budget system for plant reconstruction at high detail, which may be usable in several scenarios, including outdoor conditions. Future research should address other issues, such as the time-cost relationship and the need for detail in the different approaches. View Full-Text
Keywords: plant phenotyping; RGB imagery; structure from motion; multi-view stereo; digital surface models plant phenotyping; RGB imagery; structure from motion; multi-view stereo; digital surface models
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Andújar, D.; Calle, M.; Fernández-Quintanilla, C.; Ribeiro, Á.; Dorado, J. Three-Dimensional Modeling of Weed Plants Using Low-Cost Photogrammetry. Sensors 2018, 18, 1077.

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