Botany-Bot: Digital Twin Monitoring of Occluded and Underleaf Plant Structures with Gaussian Splats

Simeon Adebola, Chung Min Kim, Justin Kerr, Shuangyu Xie, Prithvi Akella +3 more
10/20/2025
cs.ROcs.CV

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Abstract

Commercial plant phenotyping systems using fixed cameras cannot perceive many plant details due to leaf occlusion. In this paper, we present Botany-Bot, a system for building detailed "annotated digital twins" of living plants using two stereo cameras, a digital turntable inside a lightbox, an industrial robot arm, and 3D segmentated Gaussian Splat models. We also present robot algorithms for manipulating leaves to take high-resolution indexable images of occluded details such as stem buds and the underside/topside of leaves. Results from experiments suggest that Botany-Bot can segment leaves with 90.8% accuracy, detect leaves with 86.2% accuracy, lift/push leaves with 77.9% accuracy, and take detailed overside/underside images with 77.3% accuracy. Code, videos, and datasets are available at https://berkeleyautomation.github.io/Botany-Bot/.

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Cite this paper

@article{adebola2025botanybot,
  title  = {Botany-Bot: Digital Twin Monitoring of Occluded and Underleaf Plant Structures with Gaussian Splats},
  author = {Simeon Adebola and Chung Min Kim and Justin Kerr and Shuangyu Xie and Prithvi Akella and Jose Luis Susa Rincon and Eugen Solowjow and Ken Goldberg},
  year   = {2025},
  eprint = {2510.17783},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2510.17783v1}
}

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