Model-based Segmentation of 3D Point Clouds for Phenotyping Sunflower Plants

William Gélard (1, 2) , Michel Devy (1) , Ariane Herbulot (1) , Philippe Burger (2)

1 LAAS-RAP - Équipe Robotique, Action et Perception
2 AGIR - AGroécologie, Innovations, teRritoires

This article presents a model-based segmentation method applied to 3D data acquired on sunflower plants. Our objective is the quantification of the plant growth using observations made automatically from sensors moved around plants. Here, acquisitions are made on isolated plants: a 3D point cloud is computed using Structure from Motion with RGB images acquired all around a plant. Then the proposed method is applied in order to segment and label the plant leaves, i.e. to split up the point cloud in regions corresponding to plant organs: stem, petioles, and leaves. Every leaf is then reconstructed with NURBS and its area is computed from the triangular mesh. Our segmentation method is validated comparing these areas with the ones measured manually using a planimeter: it is shown that differences between automatic and manual measurements are less than 10%. The present results open interesting perspectives in direction of high-throughput sunflower phenotyping.

Mots clés: labeling; 3D plant phenotyping; clustering; Structure from Motion; NURBS fitting; sunflowers

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

William Gélard, Michel Devy, Ariane Herbulot, Philippe Burger. Model-based Segmentation of 3D Point Clouds for Phenotyping Sunflower Plants. 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP 2017), Feb 2017, Porto, Portugal. pp.459-467, ⟨10.5220/0006126404590467⟩. ⟨hal-01526886⟩