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, E., Madec, S., Sadeghi-Tehran, P., Aasen, H., Zheng, B., Liu, S., Kirchgessner, N., Ishikawa, G., Nagasawa, K., Badhon, M. A., Pozniak, C., de Solan, B., Hund, A., Chapman, S. C., Baret, F., Stavness, I., & Guo, W. Plant Phenomics, 2020:1–12, August, 2020. ![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 [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{david_global_2020,
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}},
volume = {2020},
issn = {2643-6515},
shorttitle = {Global {Wheat} {Head} {Detection} ({GWHD}) {Dataset}},
url = {https://spj.sciencemag.org/journals/plantphenomics/2020/3521852/},
doi = {10.34133/2020/3521852},
abstract = {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.},
language = {en},
urldate = {2022-04-12},
journal = {Plant Phenomics},
author = {David, Etienne and Madec, Simon and Sadeghi-Tehran, Pouria and Aasen, Helge and Zheng, Bangyou and Liu, Shouyang and Kirchgessner, Norbert and Ishikawa, Goro and Nagasawa, Koichi and Badhon, Minhajul A. and Pozniak, Curtis and de Solan, Benoit and Hund, Andreas and Chapman, Scott C. and Baret, Frédéric and Stavness, Ian and Guo, Wei},
month = aug,
year = {2020},
pages = {1--12},
}
Downloads: 0
{"_id":"vZwsxqmoztincGPja","bibbaseid":"david-madec-sadeghitehran-aasen-zheng-liu-kirchgessner-ishikawa-etal-globalwheatheaddetectiongwhddatasetalargeanddiversedatasetofhighresolutionrgblabelledimagestodevelopandbenchmarkwheatheaddetectionmethods-2020","author_short":["David, E.","Madec, S.","Sadeghi-Tehran, P.","Aasen, H.","Zheng, B.","Liu, S.","Kirchgessner, N.","Ishikawa, G.","Nagasawa, K.","Badhon, M. A.","Pozniak, C.","de Solan, B.","Hund, A.","Chapman, S. C.","Baret, F.","Stavness, I.","Guo, W."],"bibdata":{"bibtype":"article","type":"article","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","volume":"2020","issn":"2643-6515","shorttitle":"Global Wheat Head Detection (GWHD) Dataset","url":"https://spj.sciencemag.org/journals/plantphenomics/2020/3521852/","doi":"10.34133/2020/3521852","abstract":"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.","language":"en","urldate":"2022-04-12","journal":"Plant Phenomics","author":[{"propositions":[],"lastnames":["David"],"firstnames":["Etienne"],"suffixes":[]},{"propositions":[],"lastnames":["Madec"],"firstnames":["Simon"],"suffixes":[]},{"propositions":[],"lastnames":["Sadeghi-Tehran"],"firstnames":["Pouria"],"suffixes":[]},{"propositions":[],"lastnames":["Aasen"],"firstnames":["Helge"],"suffixes":[]},{"propositions":[],"lastnames":["Zheng"],"firstnames":["Bangyou"],"suffixes":[]},{"propositions":[],"lastnames":["Liu"],"firstnames":["Shouyang"],"suffixes":[]},{"propositions":[],"lastnames":["Kirchgessner"],"firstnames":["Norbert"],"suffixes":[]},{"propositions":[],"lastnames":["Ishikawa"],"firstnames":["Goro"],"suffixes":[]},{"propositions":[],"lastnames":["Nagasawa"],"firstnames":["Koichi"],"suffixes":[]},{"propositions":[],"lastnames":["Badhon"],"firstnames":["Minhajul","A."],"suffixes":[]},{"propositions":[],"lastnames":["Pozniak"],"firstnames":["Curtis"],"suffixes":[]},{"propositions":["de"],"lastnames":["Solan"],"firstnames":["Benoit"],"suffixes":[]},{"propositions":[],"lastnames":["Hund"],"firstnames":["Andreas"],"suffixes":[]},{"propositions":[],"lastnames":["Chapman"],"firstnames":["Scott","C."],"suffixes":[]},{"propositions":[],"lastnames":["Baret"],"firstnames":["Frédéric"],"suffixes":[]},{"propositions":[],"lastnames":["Stavness"],"firstnames":["Ian"],"suffixes":[]},{"propositions":[],"lastnames":["Guo"],"firstnames":["Wei"],"suffixes":[]}],"month":"August","year":"2020","pages":"1–12","bibtex":"@article{david_global_2020,\n\ttitle = {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}},\n\tvolume = {2020},\n\tissn = {2643-6515},\n\tshorttitle = {Global {Wheat} {Head} {Detection} ({GWHD}) {Dataset}},\n\turl = {https://spj.sciencemag.org/journals/plantphenomics/2020/3521852/},\n\tdoi = {10.34133/2020/3521852},\n\tabstract = {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\n http://www.global-wheat.com/and\n aimed at developing and benchmarking methods for wheat head detection.},\n\tlanguage = {en},\n\turldate = {2022-04-12},\n\tjournal = {Plant Phenomics},\n\tauthor = {David, Etienne and Madec, Simon and Sadeghi-Tehran, Pouria and Aasen, Helge and Zheng, Bangyou and Liu, Shouyang and Kirchgessner, Norbert and Ishikawa, Goro and Nagasawa, Koichi and Badhon, Minhajul A. and Pozniak, Curtis and de Solan, Benoit and Hund, Andreas and Chapman, Scott C. and Baret, Frédéric and Stavness, Ian and Guo, Wei},\n\tmonth = aug,\n\tyear = {2020},\n\tpages = {1--12},\n}\n\n\n\n","author_short":["David, E.","Madec, S.","Sadeghi-Tehran, P.","Aasen, H.","Zheng, B.","Liu, S.","Kirchgessner, N.","Ishikawa, G.","Nagasawa, K.","Badhon, M. A.","Pozniak, C.","de Solan, B.","Hund, A.","Chapman, S. C.","Baret, F.","Stavness, I.","Guo, W."],"key":"david_global_2020","id":"david_global_2020","bibbaseid":"david-madec-sadeghitehran-aasen-zheng-liu-kirchgessner-ishikawa-etal-globalwheatheaddetectiongwhddatasetalargeanddiversedatasetofhighresolutionrgblabelledimagestodevelopandbenchmarkwheatheaddetectionmethods-2020","role":"author","urls":{"Paper":"https://spj.sciencemag.org/journals/plantphenomics/2020/3521852/"},"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/zotero-group/howcanoewang/4660314","dataSources":["Bw2R3bNsNP7z89tPm","ucgw8KTyMdD4wN9Tx","GBXPD4xSbvGqXeR24","AHMB37rGQdtcwer6D"],"keywords":[],"search_terms":["global","wheat","head","detection","gwhd","dataset","large","diverse","dataset","high","resolution","rgb","labelled","images","develop","benchmark","wheat","head","detection","methods","david","madec","sadeghi-tehran","aasen","zheng","liu","kirchgessner","ishikawa","nagasawa","badhon","pozniak","de solan","hund","chapman","baret","stavness","guo"],"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","year":2020}