Segmentation of structural parts of rosebush plants with 3d point-based deep learning methods

Kaya Turgut (1) , Helin Dutagaci (1) , Gilles Galopin (2) , David Rousseau (2, 3)

1 Eskisehir Osmangazi University
2 IRHS - Institut de Recherche en Horticulture et Semences
3 LARIS - Laboratoire Angevin de Recherche en Ingénierie des Systèmes

Segmentation of structural parts of 3D models of plants is an important step for plant phenotyping, especially for monitoring architectural and morphological traits. This work introduces a benchmark for assessing the performance of 3D point-based deep learning methods on organ segmentation of 3D plant models, specifically rosebush models. Six recent deep learning architectures that segment 3D point clouds into semantic parts were adapted and compared. The methods were tested on the ROSE-X data set, containing fully annotated 3D models of real rosebush plants. The contribution of incorporating synthetic 3D models generated through Lindenmayer systems into training data was also investigated.

Mots clés: Plant part segmentation; Point cloud; Virtual plant; Deep learning; Semantic segmentation; Phenotyping

Domaines: Horticulture

Kaya Turgut, Helin Dutagaci, Gilles Galopin, David Rousseau. Segmentation of structural parts of rosebush plants with 3d point-based deep learning methods. Plant Methods, 2022, 18 (20), pp.00857-3. ⟨10.1186/s13007-022-00857-3⟩. ⟨hal-03144153v2⟩