Back to Search Start Over

Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods

Authors :
Etienne David
Mario Serouart
Daniel Smith
Simon Madec
Kaaviya Velumani
Shouyang Liu
Xu Wang
Francisco Pinto
Shahameh Shafiee
Izzat S. A. Tahir
Hisashi Tsujimoto
Shuhei Nasuda
Bangyou Zheng
Norbert Kirchgessner
Helge Aasen
Andreas Hund
Pouria Sadhegi-Tehran
Koichi Nagasawa
Goro Ishikawa
Sébastien Dandrifosse
Alexis Carlier
Benjamin Dumont
Benoit Mercatoris
Byron Evers
Ken Kuroki
Haozhou Wang
Masanori Ishii
Minhajul A. Badhon
Curtis Pozniak
David Shaner LeBauer
Morten Lillemo
Jesse Poland
Scott Chapman
Benoit de Solan
Frédéric Baret
Ian Stavness
Wei Guo
Source :
Plant Phenomics, Vol 2021 (2021)
Publication Year :
2021
Publisher :
American Association for the Advancement of Science (AAAS), 2021.

Abstract

The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience, a few avenues for improvements have been identified regarding data size, head diversity, and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and complemented by adding 1722 images from 5 additional countries, allowing for 81,553 additional wheat heads. We now release in 2021 a new version of the Global Wheat Head Detection dataset, which is bigger, more diverse, and less noisy than the GWHD_2020 version.

Details

Language :
English
ISSN :
26436515
Volume :
2021
Database :
Directory of Open Access Journals
Journal :
Plant Phenomics
Publication Type :
Academic Journal
Accession number :
edsdoj.6363f47b04262b456b55f5e2a3ba9
Document Type :
article
Full Text :
https://doi.org/10.34133/2021/9846158