BonnBeetClouds3D (doi:10.60507/FK2/34W30T)

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BonnBeetClouds3D

doi:10.60507/FK2/34W30T

bonndata

2024-01-29

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Marks, Elias; Bömer, Jonas; Magistri, Federico; Sah, Anurag; Behley, Jens; Stachniss, Cyrill, 2024, "BonnBeetClouds3D", https://doi.org/10.60507/FK2/34W30T, bonndata, V1

BonnBeetClouds3D

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

doi:10.60507/FK2/34W30T

Marks, Elias (University of Bonn)

Bömer, Jonas (Institute of Sugar Beet Research, Göttingen)

Magistri, Federico (University of Bonn)

Sah, Anurag (University of Bonn)

Behley, Jens (University of Bonn)

Stachniss, Cyrill (University of Bonn)

bonndata

Marks, Elias

Marks, Elias

2023-12-22

https://doi.org/10.60507/FK2/34W30T

Agricultural Sciences, Computer and Information Science

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.

Point clouds

@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} }

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

@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} }

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BonnBeetClouds3Dv0.zip

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README.md

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