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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.17783 |
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| _version_ | 1866915564476170240 |
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| author | Adebola, Simeon Kim, Chung Min Kerr, Justin Xie, Shuangyu Akella, Prithvi Rincon, Jose Luis Susa Solowjow, Eugen Goldberg, Ken |
| author_facet | Adebola, Simeon Kim, Chung Min Kerr, Justin Xie, Shuangyu Akella, Prithvi Rincon, Jose Luis Susa Solowjow, Eugen Goldberg, Ken |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_17783 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Botany-Bot: Digital Twin Monitoring of Occluded and Underleaf Plant Structures with Gaussian Splats Adebola, Simeon Kim, Chung Min Kerr, Justin Xie, Shuangyu Akella, Prithvi Rincon, Jose Luis Susa Solowjow, Eugen Goldberg, Ken Robotics Computer Vision and Pattern Recognition 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/. |
| title | Botany-Bot: Digital Twin Monitoring of Occluded and Underleaf Plant Structures with Gaussian Splats |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.17783 |