BonnBeetClouds3D

Citation Metadata

Title title

BonnBeetClouds3D

Subtitle subtitle

A Dataset Towards Point Cloud-based Organ-level Phenotyping of Sugar Beet Plants under Field Conditions

Author author

1. Author Name

   Marks, Elias

   Author Affiliation

   University of Bonn

   Author Identifier Scheme

   ORCID

   Author Identifier

   Type Name

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   https://orcid.org/0000-0003-1322-9317

   Expandedvalue

   Person Name

   Marks, Elias

   @id

   https://orcid.org/0000-0003-1322-9317

   Scheme

   ORCID

   @type

   https://schema.org/Person

2. Author Name

   Bömer, Jonas

   Author Affiliation

   Institute of Sugar Beet Research, Göttingen

3. Author Name

   Magistri, Federico

   Author Affiliation

   University of Bonn

   Author Identifier Scheme

   ORCID

   Author Identifier

   Type Name

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   https://orcid.org/0000-0003-2815-5760

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   Person Name

   Magistri, Federico

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   Scheme

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4. Author Name

   Sah, Anurag

   Author Affiliation

   University of Bonn

5. Author Name

   Behley, Jens

   Author Affiliation

   University of Bonn

   Author Identifier Scheme

   ORCID

   Author Identifier

   Type Name

   authorIdentifier

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   Person Name

   Behley, Jens

   @id

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   Scheme

   ORCID

   @type

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6. Author Name

   Stachniss, Cyrill

   Author Affiliation

   University of Bonn

   Author Identifier Scheme

   ORCID

   Author Identifier

   Type Name

   authorIdentifier

   Multiple

   False

   Type Class

   primitive

   Value

   https://orcid.org/0000-0003-1173-6972

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   Person Name

   Stachniss, Cyrill

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Dataset Contact datasetContact

1. Dataset Contact Name

   Marks, Elias

   Dataset Contact Affiliation

   University of Bonn

   Dataset Contact Email

   emarks1@uni-bonn.de

Ds Description dsDescription

1. Ds Description Value

   Agricultural production is facing severe challenges in the next decades induced by climate change and the need for sustainability, reducing its impact on the environment. Advancement in field management through non-chemical weed- ing by robots in combination with monitoring of crops by autonomous unmanned aerial vehicles (UAVs) and breeding of novel and more resilient crop varieties are helpful to address these challenges. The analysis of plant traits is called phenotyping, and is an essential activity in plant breeding, it however involves a great amount of manual labor. With this paper, we address the problem of automatic fine-grained organ- level geometric analysis needed for precision phenotyping. However, the availability of real-world data for such fine- grained perception tasks in this domain is relatively scarce compared to other domains such as autonomous driving. To work towards closing this gap, we propose a novel dataset that was acquired using UAVs capturing high-resolution im- ages of a real breeding trial. This has the big advantage of containing a multitude of plant varieties, leading to a great morphological and appearance diversity covered by our dataset. This enables the development of approaches for autonomous phenotyping that generalize well to different varieties. Based on overlapping high-resolution images from multiple viewing angles, we compute photogrammetric dense point clouds via bundle adjustment that capture the geometric structure of the plants. We provide detailed and accurate point-wise labels for individual plants, individual leaves, salient points on the leaves such as the tip and the base. Additionally we include measurements of phenotypic traits performed by experts from the German Federal Plant Variety Office (”Bundessortenamt) on the real plants, allowing to evaluate approaches not only on segmentation and keypoint detection, but also directly on the downstream tasks. The provided labeled point clouds enable fine-grained plant analysis and opens the door for further progress in the development of automatic phenotyping approaches, but also enable further research in closely related application areas such as surface reconstruction, point cloud completion, and semantic interpretation of point clouds.

   Ds Description Date

   2023-12-22

Subject subject

1. Agricultural Sciences
2. Computer and Information Science

Subject Refinement subjectRefinement

1. Subject Refinement Value

   Phenotyping

2. Subject Refinement Value

   Robotics

3. Subject Refinement Value

   Computer vision

Free Keyword freeKeyword

1. Free Keyword Value

   Robotics

2. Free Keyword Value

   Phenotyping

3. Free Keyword Value

   Computer vision

4. Free Keyword Value

   UAV

5. Free Keyword Value

   Sugar beet

Publication publication

1. Publication Citation

   @misc{marks2023arxiv, title={BonnBeetClouds3D: A Dataset Towards Point Cloud-based Organ-level Phenotyping of Sugar Beet Plants under Field Conditions}, author={Elias Marks and Jonas Bömer and Federico Magistri and Anurag Sah and Jens Behley and Cyrill Stachniss}, year={2023}, eprint={2312.14706}, archivePrefix={arXiv}, primaryClass={cs.CV} }

   Publication IDType

   doi

   Publication IDNumber

   https://doi.org/10.48550/arXiv.2312.14706

   Publication URL

   https://arxiv.org/abs/2312.14706

Depositor depositor

Marks, Elias

Date Of Deposit dateOfDeposit

2023-12-22

Kind Of Data kindOfData

1. Point clouds