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

Sensors · 3 Apr 2018 · 10.3390/s18041077

Abstract

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.

Plant phenotyping relevance

低コストSfM/MVSによる植物3D再構築を開発・適用し、草丈・葉面積・乾物バイオマスを実測値で検証しており、植物形質取得手法が研究の中心である。

abstractThis 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.
abstractThese 3D models were validated with ground truth values (e.g., plant height, leaf area (LA) and plant dry biomass) using regression methods.
abstract3D 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.

Code and data availability

The article describes SfM-MVS reconstruction of weed plants (Xanthium strumarium, Datura ferox, Sorghum halepense) and validation of height/leaf-area estimates, but the supplied blocks contain no public dataset, image, code, model, or supplement availability statement. Only the CC BY license URL appears, which is not a

No evidence-backed public reproduction asset is currently recorded.

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