Global wheat head detection 2021: An improved dataset for benchmarking wheat head detection methods

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

doi:10.34133/2021/9846158

Global wheat head detection 2021: An improved dataset for benchmarking wheat head detection methods

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 DandrifosseAlexis 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

Research output: Contribution to journal › Article › peer-review

74 Scopus citations

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.

Original language	English (US)
Journal	Plant Phenomics
Volume	2021
DOIs	https://doi.org/10.34133/2021/9846158
State	Published - Jan 1 2021
Externally published	Yes

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David, E., Serouart, M., Smith, D., Madec, S., Velumani, K., Liu, S., Wang, X., Pinto, F., Shafiee, S., Tahir, I. S. A., Tsujimoto, H., Nasuda, S., Zheng, B., Kirchgessner, N., Aasen, H., Hund, A., Sadhegi-Tehran, P., Nagasawa, K., Ishikawa, G., ... Guo, W. (2021). Global wheat head detection 2021: An improved dataset for benchmarking wheat head detection methods. Plant Phenomics, 2021. https://doi.org/10.34133/2021/9846158

@article{54b5fe54c0e741d9873050355712a5af,

title = "Global wheat head detection 2021: An improved dataset for benchmarking wheat head detection methods",

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.",

author = "Etienne David and Mario Serouart and Daniel Smith and Simon Madec and Kaaviya Velumani and Shouyang Liu and Xu Wang and Francisco Pinto and Shahameh Shafiee and Tahir, {Izzat S.A.} and Hisashi Tsujimoto and Shuhei Nasuda and Bangyou Zheng and Norbert Kirchgessner and Helge Aasen and Andreas Hund and Pouria Sadhegi-Tehran and Koichi Nagasawa and Goro Ishikawa and S{\'e}bastien Dandrifosse and Alexis Carlier and Benjamin Dumont and Benoit Mercatoris and Byron Evers and Ken Kuroki and Haozhou Wang and Masanori Ishii and Badhon, {Minhajul A.} and Curtis Pozniak and LeBauer, {David Shaner} and Morten Lillemo and Jesse Poland and Scott Chapman and {de Solan}, Benoit and Fr{\'e}d{\'e}ric Baret and Ian Stavness and Wei Guo",

note = "Generated from Scopus record by KAUST IRTS on 2022-09-13",

year = "2021",

month = jan,

day = "1",

doi = "10.34133/2021/9846158",

language = "English (US)",

volume = "2021",

journal = "Plant Phenomics",

issn = "2643-6515",

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David, E, Serouart, M, Smith, D, Madec, S, Velumani, K, Liu, S, Wang, X, Pinto, F, Shafiee, S, Tahir, ISA, Tsujimoto, H, Nasuda, S, Zheng, B, Kirchgessner, N, Aasen, H, Hund, A, Sadhegi-Tehran, P, Nagasawa, K, Ishikawa, G, Dandrifosse, S, Carlier, A, Dumont, B, Mercatoris, B, Evers, B, Kuroki, K, Wang, H, Ishii, M, Badhon, MA, Pozniak, C, LeBauer, DS, Lillemo, M, Poland, J, Chapman, S, de Solan, B, Baret, F, Stavness, I & Guo, W 2021, 'Global wheat head detection 2021: An improved dataset for benchmarking wheat head detection methods', Plant Phenomics, vol. 2021. https://doi.org/10.34133/2021/9846158

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T1 - Global wheat head detection 2021: An improved dataset for benchmarking wheat head detection methods

AU - David, Etienne

AU - Serouart, Mario

AU - Smith, Daniel

AU - Madec, Simon

AU - Velumani, Kaaviya

AU - Liu, Shouyang

AU - Wang, Xu

AU - Pinto, Francisco

AU - Shafiee, Shahameh

AU - Tahir, Izzat S.A.

AU - Tsujimoto, Hisashi

AU - Nasuda, Shuhei

AU - Zheng, Bangyou

AU - Kirchgessner, Norbert

AU - Aasen, Helge

AU - Hund, Andreas

AU - Sadhegi-Tehran, Pouria

AU - Nagasawa, Koichi

AU - Ishikawa, Goro

AU - Dandrifosse, Sébastien

AU - Carlier, Alexis

AU - Dumont, Benjamin

AU - Mercatoris, Benoit

AU - Evers, Byron

AU - Kuroki, Ken

AU - Wang, Haozhou

AU - Ishii, Masanori

AU - Badhon, Minhajul A.

AU - Pozniak, Curtis

AU - LeBauer, David Shaner

AU - Lillemo, Morten

AU - Poland, Jesse

AU - Chapman, Scott

AU - de Solan, Benoit

AU - Baret, Frédéric

AU - Stavness, Ian

AU - Guo, Wei

N1 - Generated from Scopus record by KAUST IRTS on 2022-09-13

PY - 2021/1/1

Y1 - 2021/1/1

N2 - 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.

AB - 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.

UR - https://spj.sciencemag.org/journals/plantphenomics/2021/9846158/

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U2 - 10.34133/2021/9846158

DO - 10.34133/2021/9846158

M3 - Article

C2 - 34778804

SN - 2643-6515

VL - 2021

JO - Plant Phenomics

JF - Plant Phenomics

ER -

Global wheat head detection 2021: An improved dataset for benchmarking wheat head detection methods

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