Deep-learning-ready RGB-depth images of seedling development
In the era of machine learning-driven plant imaging, the production of annotated datasets is a very important contribution. In this data paper, a unique annotated dataset of seedling emergence kinetics is proposed. It is composed of almost 70,000 RGB-depth frames and more than 700,000 plant annotati...
Saved in:
| Published in | Plant methods Vol. 21; no. 1; p. 16 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
BioMed Central Ltd
11.02.2025
BioMed Central BMC |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | In the era of machine learning-driven plant imaging, the production of annotated datasets is a very important contribution. In this data paper, a unique annotated dataset of seedling emergence kinetics is proposed. It is composed of almost 70,000 RGB-depth frames and more than 700,000 plant annotations. The dataset is shown valuable for training deep learning models and performing high-throughput phenotyping by imaging. The ability of such models to generalize to several species and outperform the state-of-the-art owing to the delivered dataset is demonstrated. We also discuss how this dataset raises new questions in plant phenotyping. |
|---|---|
| AbstractList | In the era of machine learning-driven plant imaging, the production of annotated datasets is a very important contribution. In this data paper, a unique annotated dataset of seedling emergence kinetics is proposed. It is composed of almost 70,000 RGB-depth frames and more than 700,000 plant annotations. The dataset is shown valuable for training deep learning models and performing high-throughput phenotyping by imaging. The ability of such models to generalize to several species and outperform the state-of-the-art owing to the delivered dataset is demonstrated. We also discuss how this dataset raises new questions in plant phenotyping. In the era of machine learning-driven plant imaging, the production of annotated datasets is a very important contribution. In this data paper, a unique annotated dataset of seedling emergence kinetics is proposed. It is composed of almost 70,000 RGB-depth frames and more than 700,000 plant annotations. The dataset is shown valuable for training deep learning models and performing high-throughput phenotyping by imaging. The ability of such models to generalize to several species and outperform the state-of-the-art owing to the delivered dataset is demonstrated. We also discuss how this dataset raises new questions in plant phenotyping. Keywords: RGB-depth, Seedling kinetics, Deep learning, Data set Abstract In the era of machine learning-driven plant imaging, the production of annotated datasets is a very important contribution. In this data paper, a unique annotated dataset of seedling emergence kinetics is proposed. It is composed of almost 70,000 RGB-depth frames and more than 700,000 plant annotations. The dataset is shown valuable for training deep learning models and performing high-throughput phenotyping by imaging. The ability of such models to generalize to several species and outperform the state-of-the-art owing to the delivered dataset is demonstrated. We also discuss how this dataset raises new questions in plant phenotyping. In the era of machine learning-driven plant imaging, the production of annotated datasets is a very important contribution. In this data paper, a unique annotated dataset of seedling emergence kinetics is proposed. It is composed of almost 70,000 RGB-depth frames and more than 700,000 plant annotations. The dataset is shown valuable for training deep learning models and performing high-throughput phenotyping by imaging. The ability of such models to generalize to several species and outperform the state-of-the-art owing to the delivered dataset is demonstrated. We also discuss how this dataset raises new questions in plant phenotyping.In the era of machine learning-driven plant imaging, the production of annotated datasets is a very important contribution. In this data paper, a unique annotated dataset of seedling emergence kinetics is proposed. It is composed of almost 70,000 RGB-depth frames and more than 700,000 plant annotations. The dataset is shown valuable for training deep learning models and performing high-throughput phenotyping by imaging. The ability of such models to generalize to several species and outperform the state-of-the-art owing to the delivered dataset is demonstrated. We also discuss how this dataset raises new questions in plant phenotyping. |
| Audience | Academic |
| Author | Bouhlel, Nizar Marchi, Muriel Couasnet, Geoffroy Sarniguet, Alain Rousseau, David Mercier, Félix Barret, Matthieu El Ghaziri, Angelina |
| Author_xml | – sequence: 1 givenname: Félix surname: Mercier fullname: Mercier, Félix organization: Université d'Angers, 40 Rue de Rennes, 49000, Angers, France – sequence: 2 givenname: Geoffroy surname: Couasnet fullname: Couasnet, Geoffroy organization: Université d'Angers, 40 Rue de Rennes, 49000, Angers, France – sequence: 3 givenname: Angelina surname: El Ghaziri fullname: El Ghaziri, Angelina organization: UMR1345, Institut de Recherche en Horticulture et Semences (IRHS), 49071, Beaucouzé, France – sequence: 4 givenname: Nizar surname: Bouhlel fullname: Bouhlel, Nizar organization: UMR1345, Institut de Recherche en Horticulture et Semences (IRHS), 49071, Beaucouzé, France – sequence: 5 givenname: Alain surname: Sarniguet fullname: Sarniguet, Alain organization: INRAE, 42 Rue Georges Morel, 49071, Beaucouzé, France – sequence: 6 givenname: Muriel surname: Marchi fullname: Marchi, Muriel organization: INRAE, 42 Rue Georges Morel, 49071, Beaucouzé, France – sequence: 7 givenname: Matthieu surname: Barret fullname: Barret, Matthieu organization: INRAE, 42 Rue Georges Morel, 49071, Beaucouzé, France – sequence: 8 givenname: David surname: Rousseau fullname: Rousseau, David email: david.rousseau@univ-angers.fr, david.rousseau@univ-angers.fr organization: INRAE, 42 Rue Georges Morel, 49071, Beaucouzé, France. david.rousseau@univ-angers.fr |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39934882$$D View this record in MEDLINE/PubMed https://hal.science/hal-04948208$$DView record in HAL |
| BookMark | eNqNkk1vEzEQhi1URNvAH-CAcoTDFo-_LSGh0JY2UiSkAmfLux5vttrshvUmov8ehwRouIB8sDV-_M7M6zknJ13fISEvgV4AGPU2AadUF5TJggLnouBPyBlooQphAE4enU_JeUr3lApgXD0jp9xaLoxhZ-TdFeK6aNEPXdPVxYA-PEzvbj4UAdfjctqsfI1p2sdpQgxtRqYBt9j26xV243PyNPo24YvDPiFfP15_ubwtFp9u5pezRRGEoWMBVVlWErmvbAmSlTRKE4USUsaKR25iqaTwIVSVimip1CAQqBLUiqg0lHxC5nvd0Pt7tx5yVcOD633jfgb6oXZ-GJuqRWdK9FwzqZnwwkiwJogS9S4tB5OLmJD3e631plxhqHIbg2-PRI9vumbp6n7rsuWgd8ZNyJu9wvKvd7ezhdvFqLDCMGq2kNnXh2xD_22DaXSrJlXYtr7DfpMcZ1SBBWnl_6D5p7XV9N8oKGmksMpk9GKP1j6703Sxz01VeQVcNVWep9jk-MwwzZgAzf50d3iQmRG_j7XfpOTmn--O2VePvfxtxq_x4j8A-dnP0Q |
| ContentType | Journal Article |
| Copyright | 2025. The Author(s). COPYRIGHT 2025 BioMed Central Ltd. Attribution The Author(s) 2025 2025 |
| Copyright_xml | – notice: 2025. The Author(s). – notice: COPYRIGHT 2025 BioMed Central Ltd. – notice: Attribution – notice: The Author(s) 2025 2025 |
| DBID | NPM ISR 7X8 7S9 L.6 1XC VOOES 5PM DOA |
| DOI | 10.1186/s13007-025-01334-3 |
| DatabaseName | PubMed Gale In Context: Science MEDLINE - Academic AGRICOLA AGRICOLA - Academic Hyper Article en Ligne (HAL) Hyper Article en Ligne (HAL) (Open Access) PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | PubMed MEDLINE - Academic AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | PubMed AGRICOLA AGRICOLA MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Botany Physics Computer Science |
| EISSN | 1746-4811 |
| EndPage | 16 |
| ExternalDocumentID | oai_doaj_org_article_8bea3725724a485198d4be7152b3185e PMC11817399 oai_HAL_hal_04948208v1 A827224172 39934882 |
| Genre | Journal Article |
| GeographicLocations | France |
| GeographicLocations_xml | – name: France |
| GroupedDBID | -A0 0R~ 123 29O 2WC 2XV 3V. 5VS 7X2 7X7 8FE 8FH 8FI 8FJ AAFWJ AAHBH AAJSJ ABDBF ABUWG ACGFO ACGFS ACIHN ACPRK ACRMQ ACUHS ADBBV ADINQ ADRAZ ADUKV AEAQA AENEX AEUYN AFKRA AFRAH AHBYD AHMBA AHYZX ALIPV ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS ATCPS BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BHPHI BMC BPHCQ BVXVI C24 C6C CCPQU CS3 DIK DU5 E3Z EBD EBLON EBS ECGQY ESX F5P FYUFA GROUPED_DOAJ GX1 HCIFZ HMCUK HYE IAG IAO IGH IGS IHR ISR ITC KQ8 LK8 M0K M7P M~E NPM O5R O5S OK1 P2P PGMZT PIMPY PQQKQ PROAC RBZ RNS ROL RPM RSV SBL SOJ TR2 TUS UKHRP WOQ XSB ~8M AASML AFPKN OVT PHGZM PHGZT PMFND 7X8 PQGLB 7S9 L.6 1XC VOOES 5PM PUEGO |
| ID | FETCH-LOGICAL-d480t-1cbbc5e3ac9b152b0f58f46455fc3f38fb654addcc6fe905714e1064094f671b3 |
| IEDL.DBID | DOA |
| ISSN | 1746-4811 |
| IngestDate | Wed Aug 27 01:30:05 EDT 2025 Thu Aug 21 18:29:03 EDT 2025 Wed May 21 12:41:40 EDT 2025 Fri Jul 11 17:32:35 EDT 2025 Fri Jul 11 18:33:24 EDT 2025 Thu Jul 10 18:42:59 EDT 2025 Tue Jun 10 21:01:26 EDT 2025 Fri Jun 27 05:13:08 EDT 2025 Sat Feb 15 01:21:27 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Deep learning Data set RGB-depth Seedling kinetics |
| Language | English |
| License | 2025. The Author(s). Attribution: http://creativecommons.org/licenses/by Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-d480t-1cbbc5e3ac9b152b0f58f46455fc3f38fb654addcc6fe905714e1064094f671b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-7397-8953 0000-0002-7633-8476 0000-0001-6232-0200 0000-0002-4861-8550 |
| OpenAccessLink | https://doaj.org/article/8bea3725724a485198d4be7152b3185e |
| PMID | 39934882 |
| PQID | 3165854968 |
| PQPubID | 24069 |
| PageCount | 1 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_8bea3725724a485198d4be7152b3185e pubmedcentral_primary_oai_pubmedcentral_nih_gov_11817399 hal_primary_oai_HAL_hal_04948208v1 proquest_miscellaneous_3206191595 proquest_miscellaneous_3200257970 proquest_miscellaneous_3165854968 gale_infotracacademiconefile_A827224172 gale_incontextgauss_ISR_A827224172 pubmed_primary_39934882 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-02-11 |
| PublicationDateYYYYMMDD | 2025-02-11 |
| PublicationDate_xml | – month: 02 year: 2025 text: 2025-02-11 day: 11 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England – name: London |
| PublicationTitle | Plant methods |
| PublicationTitleAlternate | Plant Methods |
| PublicationYear | 2025 |
| Publisher | BioMed Central Ltd BioMed Central BMC |
| Publisher_xml | – name: BioMed Central Ltd – name: BioMed Central – name: BMC |
| SSID | ssj0041236 |
| Score | 2.3745806 |
| Snippet | In the era of machine learning-driven plant imaging, the production of annotated datasets is a very important contribution. In this data paper, a unique... Abstract In the era of machine learning-driven plant imaging, the production of annotated datasets is a very important contribution. In this data paper, a... |
| SourceID | doaj pubmedcentral hal proquest gale pubmed |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database |
| StartPage | 16 |
| SubjectTerms | Bioinformatics Computer Science Data Analysis, Statistics and Probability data collection Deep learning Development Image processing Machine learning phenotype Physics Physiological aspects Plants RGB-depth seedling emergence Seedling kinetics Seedlings species Technology application |
| Title | Deep-learning-ready RGB-depth images of seedling development |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/39934882 https://www.proquest.com/docview/3165854968 https://www.proquest.com/docview/3200257970 https://www.proquest.com/docview/3206191595 https://hal.science/hal-04948208 https://pubmed.ncbi.nlm.nih.gov/PMC11817399 https://doaj.org/article/8bea3725724a485198d4be7152b3185e |
| Volume | 21 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwELVo4cAF8d0UWIUKiZPVOHZiW-KygZYFQYUWKq24WHZid3sgWTW7SP33zCTZ0oBULlxySBxpNDMeP2tm3hDySviEZ84H6qXzVOjEUpVyTV1lZUiCl6rrcv18ks9OxcdFtrg26gtrwnp64F5xh8p5yyU4ViqsAHigVSWcl3DsOGz89Rh94czbXqb6GCyQU2TbIqPywxaTNpLi6FaAPFxQPlD0XwXinSXWQf4NMv-slbx2-BzfJ_cG1BhPe2kfkFu-fkjuFA0gu8tH5M0771d0mP9wRgEGVpfx_H1BK79aL-PzHxAz2rgJcQtHFbafx9XvUqHH5PT46NvbGR2mItBKqGRNWelcmXluS-1QC0nIVMD8ZBZKHrgKLs8ERK2yzIPXAMeY8AzzdVqEXDLHn5Dduqn9Hom5UznYpCo180LboBD-lSGpWB5sVsmIFKgks-qJLwxSUXcvwEBmMJD5l4EicoAqNkg2UWM1y5ndtK358HVupiqVCCFkGpHXw6LQgJpLOzQHgJzITzVaeQCmGok0m34y-K6jukkT9ZNF5OXWkga2C-ZAbO2bTWs4A8gFd-Jc3bAGC1cyqWVy4xq4egIWzCLytPeQK5kQ80FcBFHVyHdGQo-_1OfLjtob24Al_L__PzT_jNxNO5dPKWPPye76YuNfAIRauwnZkQs5IbeLo5Mv80m3d-A5L77_Ajq5Gq4 |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Deep-learning-ready+RGB-depth+images+of+seedling+development&rft.jtitle=Plant+methods&rft.au=Mercier%2C+F%C3%A9lix&rft.au=Couasnet%2C+Geoffroy&rft.au=El+Ghaziri%2C+Angelina&rft.au=Bouhlel%2C+Nizar&rft.date=2025-02-11&rft.pub=BioMed+Central&rft.issn=1746-4811&rft.eissn=1746-4811&rft.volume=21&rft_id=info:doi/10.1186%2Fs13007-025-01334-3&rft_id=info%3Apmid%2F39934882&rft.externalDBID=HAS_PDF_LINK&rft.externalDocID=oai_HAL_hal_04948208v1 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1746-4811&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1746-4811&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1746-4811&client=summon |