Annotated 3D Point Cloud Dataset of Broad-Leaf Legumes Captured by High-Throughput Phenotyping Platform

Galba, A and Masner, J and Kholova, J and Kartal, S and Stočes, M and Mikeš, V and Šimek, P and Prokopová, S and Fiala, R and Karrer, T and Tóth, A (2025) Annotated 3D Point Cloud Dataset of Broad-Leaf Legumes Captured by High-Throughput Phenotyping Platform. Scientific Data, 12. pp. 1-8. ISSN 2052-4463

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Abstract

This data descriptor presents novel, annotated 3D point cloud plant scans generated by a high-throughput phenotyping platform (LeasyScan, ICRISAT, India). It focuses on broad-leaf legume species (mungbean, common bean, cowpea, and lima bean). The dataset, generated by PlantEye(R) F600 technology, captures multispectral 3D scans of plant canopies. It includes 223 scans, providing detailed organ-level segmentation annotations for embryonic leaves, leaves, petioles, stems, and whole plants. The dataset fills a critical gap in plant phenomics research by offering a base of annotated data to support AI model development efforts in 3D computer vision. Data preprocessing, annotation procedures, and potential applications in crop research disciplines are further discussed. The dataset, preprocessing code, annotations, and a MIAPPE-compliant data sheet are also presented via the GitHub repository for further updates and expansion.

Item Type:	Article
Divisions:	Global Research Program - Accelerated Crop Improvement
CRP:	UNSPECIFIED
Uncontrolled Keywords:	Plant breeding, Plant physiology, High-Throughput Phenotyping Platform, Legumes, broad-leaf
Subjects:	Others > Plant Breeding Others > Genetics and Genomics Others > Plant Physiology
Depositing User:	Mr Nagaraju T
Date Deposited:	13 Nov 2025 08:51
Last Modified:	13 Nov 2025 08:51
URI:	http://oar.icrisat.org/id/eprint/13388
Official URL:	https://www.nature.com/articles/s41597-025-06049-7
Projects:	UNSPECIFIED
Funders:	UNSPECIFIED
Acknowledgement:	The results and knowledge included herein have been obtained owing to support from the following grants: Internal grant agency of the Faculty of Economics and Management, Czech University of Life Sciences Prague, grant no. 2023B0005 (Oborově zaměřené datové modely pro podporu iniciativy Open Science a principu FAIR); Ministry of Agriculture of the Czech Republic, grant number QK23020058 (Precision agriculture and digitization in the Czech Republic). We also want to thank the Segments.ai platform for the university license that was provided. Additional acknowledgments go to Anbazhagan Krithika, Sunita Choudhary, Baddam Rekha, and their students from ICRISAT for help with the initial data annotation protocol testing and the first round of annotations.
Links:	Google Scholar

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