3D plant phenotyping in sunflower using architecture-based organ segmentation from 3D point clouds

William Gélard (1, 2) , Philippe Burger (2) , Pierre Casadebaig (2) , Nicolas Langlade (3) , Philippe Debaeke (2) , Michel Devy (1) , Ariane Herbulot (1)

1 LAAS-RAP - Équipe Robotique, Action et Perception
2 AGIR - AGroécologie, Innovations, teRritoires
3 LIPM - Laboratoire des interactions plantes micro-organismes

This paper presents a 3D phenotyping method applied to sunflower, allowing to compute the leaf area of an isolated plant. This is a preliminary step towards the automated monitoring of leaf area and plant growth through the plant life cycle. First, a model-based segmentation method is applied to 3D data derived from RGB images acquired on sunflower plants grown in pots. The RGB image acquisitions are made all around the isolated plant with a single hand-held standard camera (Sony A5100) and a 3D point cloud is computed using Structure from Motion and Multiple-view Stereo techniques [1, 2]. To do that, we used Bundler [3] and PMVS [4], Open Source libraries which can produce an accurate point cloud for plant phenotyping [5]. Then a model-based segmentation method is applied in order to segment and label the plant leaves, i.e. to split up the point cloud in regions, one for the stem, the other ones for the leaves. The leaf label is determined using the elevation of its petiole insertion point on the stem, and the relative orientation with respect to the previous leaf.

Mots clés: 3D plant phenotyping; Sunflower plant; Clustering; Labeling

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Sciences agricoles

William Gélard, Philippe Burger, Pierre Casadebaig, Nicolas Langlade, Philippe Debaeke, et al.. 3D plant phenotyping in sunflower using architecture-based organ segmentation from 3D point clouds. 5th International Workshop on Image Analysis Methods for the Plant Sciences, Sep 2016, Angers, France. ⟨hal-01380234⟩