PlantSegNet: 3D point cloud instance segmentation of nearby plant organs with identical semantics

Ariyan Zarei ,  Bosheng Li ,  James C. Schnable ,  Eric Lyons ,  Duke Pauli ,  Kobus Barnard ,  and  Bedrich Benes

Computers and Electronics in Agriculture - 2024

Computers and Electronics in Agriculture - 2024

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Zarei, Ariyan, et al. “PlantSegNet: 3D Point Cloud Instance Segmentation of Nearby Plant Organs with Identical Semantics.” Computers and Electronics in Agriculture, vol. 221, 2024, p. 108922, https://doi.org/https://doi.org/10.1016/j.compag.2024.108922.

@article{Zarei24COMPAG,
  title = {PlantSegNet: 3D point cloud instance segmentation of nearby plant organs with identical semantics},
  journal = {Computers and Electronics in Agriculture},
  volume = {221},
  pages = {108922},
  year = {2024},
  issn = {0168-1699},
  doi = {https://doi.org/10.1016/j.compag.2024.108922},
  author = {Zarei, Ariyan and Li, Bosheng and Schnable, James C. and Lyons, Eric and Pauli, Duke and Barnard, Kobus and Benes, Bedrich},
  keywords = {Phenotyping, Digital twins, Point clouds, Procedural modeling, Plant geometry, Sorghum},
  abstract = {In this study, we introduce PlantSegNet, a novel neural network model for instance segmentation of nearby objects with similar geometric structures. Our work addresses the challenges of instance segmentation of plant point clouds, including the difficulty of annotating and labeling point clouds, the loss of local structural information in neural network components, and the generation of large numbers of incorrect small clusters due to poor choices of the loss function. One of the key contributions of our approach is a digital twin of sorghum, i.e., a procedural sorghum model, which was used to generate point clouds of sorghum fields. This allowed us to create a large-scale, annotated, synthetic dataset of sorghum plants that we used to train our PlantSegNet model. We demonstrated the effectiveness of our method in segmenting instances of sorghum leaves grown in outdoor field settings. To the best of our knowledge, this is the first study to address this specific instance segmentation problem for plants grown in such a setting. We compared our proposed method with other state-of-the-art methods for indoor settings, including SGPN and TreePartNet, on both synthetic and real data. Our results show that PlantSegNet outperforms these methods regarding accuracy, robustness, and efficiency.},
  image = {Benes-COMPAG-2024-COMPAG.png},
  pdf = {Benes-COMPAG-2024-COMPAG.pdf}
}