The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/bca55ad7-da67-408d-a083-01376124f514

Ci, Jianchao; Wang, Xin; Rapado Rincon, David; Burusa, Akshay Kumar; Kootstra, Gert (2024): Data underlying the publication: 3D pose of tomato peduncle nodes using deep keypoint detection and point cloud. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/bca55ad7-da67-408d-a083-01376124f514.v1

Dataset

• Horticultural Crops
• Horticultural Production
• Other Plant Production and Plant Primary Products
• Engineering
• Agricultural and Veterinary Sciences
• Plant Production and Plant Primary Products

Deep learning, Detectron2, PeduncleKeypoint, Point cloud, Pose estimation

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Jianchao Ci , Xin Wang , David Rapado Rincon , Akshay Kumar Burusa , Gert Kootstra

This is the dataset used for paper '3D pose of tomato peduncle nodes using deep keypoint detection and point cloud'. The dataset contains 503 train images and 149 test images and their depth images and pose annotations. To test the impact of different view poses for detection results. The images were taken from 5 different angles for each peduncle node.

• 2024-12-13 first online, published, posted

4TU.ResearchData

image/jpg .csv .json

3D pose estimation of tomato peduncle nodes using deep keypoint detection and point cloud

Agricultural Biosystems Engineering Group, Department of Plant Sciences, Wageningen University and Research