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Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods
The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health, size, maturity stage, and the presence of awns. Several studies have developed methods for wheat head detection from high...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AAAS
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7706323/ https://www.ncbi.nlm.nih.gov/pubmed/33313551 http://dx.doi.org/10.34133/2020/3521852 |
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author | David, Etienne Madec, Simon Sadeghi-Tehran, Pouria Aasen, Helge Zheng, Bangyou Liu, Shouyang Kirchgessner, Norbert Ishikawa, Goro Nagasawa, Koichi Badhon, Minhajul A. Pozniak, Curtis de Solan, Benoit Hund, Andreas Chapman, Scott C. Baret, Frédéric Stavness, Ian Guo, Wei |
author_facet | David, Etienne Madec, Simon Sadeghi-Tehran, Pouria Aasen, Helge Zheng, Bangyou Liu, Shouyang Kirchgessner, Norbert Ishikawa, Goro Nagasawa, Koichi Badhon, Minhajul A. Pozniak, Curtis de Solan, Benoit Hund, Andreas Chapman, Scott C. Baret, Frédéric Stavness, Ian Guo, Wei |
author_sort | David, Etienne |
collection | PubMed |
description | The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health, size, maturity stage, and the presence of awns. Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms. However, these methods have generally been calibrated and validated on limited datasets. High variability in observational conditions, genotypic differences, development stages, and head orientation makes wheat head detection a challenge for computer vision. Further, possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex. Through a joint international collaborative effort, we have built a large, diverse, and well-labelled dataset of wheat images, called the Global Wheat Head Detection (GWHD) dataset. It contains 4700 high-resolution RGB images and 190000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes. Guidelines for image acquisition, associating minimum metadata to respect FAIR principles, and consistent head labelling methods are proposed when developing new head detection datasets. The GWHD dataset is publicly available at http://www.global-wheat.com/and aimed at developing and benchmarking methods for wheat head detection. |
format | Online Article Text |
id | pubmed-7706323 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | AAAS |
record_format | MEDLINE/PubMed |
spelling | pubmed-77063232020-12-10 Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods David, Etienne Madec, Simon Sadeghi-Tehran, Pouria Aasen, Helge Zheng, Bangyou Liu, Shouyang Kirchgessner, Norbert Ishikawa, Goro Nagasawa, Koichi Badhon, Minhajul A. Pozniak, Curtis de Solan, Benoit Hund, Andreas Chapman, Scott C. Baret, Frédéric Stavness, Ian Guo, Wei Plant Phenomics Research Article The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health, size, maturity stage, and the presence of awns. Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms. However, these methods have generally been calibrated and validated on limited datasets. High variability in observational conditions, genotypic differences, development stages, and head orientation makes wheat head detection a challenge for computer vision. Further, possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex. Through a joint international collaborative effort, we have built a large, diverse, and well-labelled dataset of wheat images, called the Global Wheat Head Detection (GWHD) dataset. It contains 4700 high-resolution RGB images and 190000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes. Guidelines for image acquisition, associating minimum metadata to respect FAIR principles, and consistent head labelling methods are proposed when developing new head detection datasets. The GWHD dataset is publicly available at http://www.global-wheat.com/and aimed at developing and benchmarking methods for wheat head detection. AAAS 2020-08-20 /pmc/articles/PMC7706323/ /pubmed/33313551 http://dx.doi.org/10.34133/2020/3521852 Text en Copyright © 2020 Etienne David et al. https://creativecommons.org/licenses/by/4.0/ Exclusive Licensee Nanjing Agricultural University. Distributed under a Creative Commons Attribution License (CC BY 4.0). |
spellingShingle | Research Article David, Etienne Madec, Simon Sadeghi-Tehran, Pouria Aasen, Helge Zheng, Bangyou Liu, Shouyang Kirchgessner, Norbert Ishikawa, Goro Nagasawa, Koichi Badhon, Minhajul A. Pozniak, Curtis de Solan, Benoit Hund, Andreas Chapman, Scott C. Baret, Frédéric Stavness, Ian Guo, Wei Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods |
title | Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods |
title_full | Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods |
title_fullStr | Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods |
title_full_unstemmed | Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods |
title_short | Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods |
title_sort | global wheat head detection (gwhd) dataset: a large and diverse dataset of high-resolution rgb-labelled images to develop and benchmark wheat head detection methods |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7706323/ https://www.ncbi.nlm.nih.gov/pubmed/33313551 http://dx.doi.org/10.34133/2020/3521852 |
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