001022057 001__ 1022057
001022057 005__ 20240226075423.0
001022057 0247_ $$2doi$$a10.3389/frai.2023.1187301
001022057 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-01194
001022057 0247_ $$2pmid$$a37261332
001022057 0247_ $$2WOS$$aWOS:000998420800001
001022057 037__ $$aFZJ-2024-01194
001022057 041__ $$aEnglish
001022057 082__ $$a004
001022057 1001_ $$0P:(DE-HGF)0$$aStavness, Ian$$b0$$eCorresponding author
001022057 245__ $$aEditorial: Computer vision in plant phenotyping and agriculture
001022057 260__ $$aLausanne$$bFrontiers Media$$c2023
001022057 3367_ $$2DRIVER$$aarticle
001022057 3367_ $$2DataCite$$aOutput Types/Journal article
001022057 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1706700196_5585
001022057 3367_ $$2BibTeX$$aARTICLE
001022057 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001022057 3367_ $$00$$2EndNote$$aJournal Article
001022057 520__ $$aPlant phenotyping is the process of identifying a plant’s structural and functional characteristics. Plant phenotyping is used by plant scientists to uncover mechanisms of plant physiology, eg, to characterize how plants respond to biotic and abiotic stress. Phenotyping is also used by plant breeders to evaluate cultivars in a plant population for beneficial characteristics in order to inform the selection of progeny to move forward within a multiyear breeding process. In an attempt to reduce the time and cost required to phenotype large plant populations, image-based phenotyping has become popular over the past 10 years. Extracting phenotypic information from images of plants and crops presents a number of challenging real-world computer vision problems, such as analyzing images with highly self-similar repeating patterns and analyzing densely packed and occluded plant organs. This Research Topic is associated with the 7th Computer Vision in Plant Phenotyping and Agriculture (CVPPA) workshop, which was held at the International Conference on Computer Vision (ICCV) on 11 October 2021. During the workshop, 18 full-length papers and 14 extended abstracts were presented. This Research Topic includes three papers that are extended versions of abstracts presented at the workshop. The Research Topic also includes 11 new articles that fall under the general scope of CVPPA but were not previously presented at the workshop.The papers in this Research Topic explore a number of high-priority challenges in image-based phenotyping, including curating new datasets, developing few-and zero-shot analysis approaches that do not require …
001022057 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001022057 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001022057 7001_ $$0P:(DE-HGF)0$$aGiuffrida, Valerio$$b1
001022057 7001_ $$0P:(DE-Juel1)129394$$aScharr, Hanno$$b2$$ufzj
001022057 773__ $$0PERI:(DE-600)2957496-1$$a10.3389/frai.2023.1187301$$gVol. 6, p. 1187301$$p1187301$$tFrontiers in artificial intelligence$$v6$$x2624-8212$$y2023
001022057 8564_ $$uhttps://juser.fz-juelich.de/record/1022057/files/frai-06-1187301.pdf$$yOpenAccess
001022057 8564_ $$uhttps://juser.fz-juelich.de/record/1022057/files/frai-06-1187301.gif?subformat=icon$$xicon$$yOpenAccess
001022057 8564_ $$uhttps://juser.fz-juelich.de/record/1022057/files/frai-06-1187301.jpg?subformat=icon-1440$$xicon-1440$$yOpenAccess
001022057 8564_ $$uhttps://juser.fz-juelich.de/record/1022057/files/frai-06-1187301.jpg?subformat=icon-180$$xicon-180$$yOpenAccess
001022057 8564_ $$uhttps://juser.fz-juelich.de/record/1022057/files/frai-06-1187301.jpg?subformat=icon-640$$xicon-640$$yOpenAccess
001022057 909CO $$ooai:juser.fz-juelich.de:1022057$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
001022057 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129394$$aForschungszentrum Jülich$$b2$$kFZJ
001022057 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5112$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
001022057 9141_ $$y2023
001022057 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001022057 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
001022057 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bFRONT ARTIF INTELL : 2022$$d2023-08-19
001022057 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2023-08-19
001022057 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2023-08-19
001022057 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central$$d2023-08-19
001022057 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2021-05-13T09:20:17Z
001022057 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2021-05-13T09:20:17Z
001022057 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Anonymous peer review$$d2021-05-13T09:20:17Z
001022057 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2023-08-19
001022057 915__ $$0StatID:(DE-HGF)0112$$2StatID$$aWoS$$bEmerging Sources Citation Index$$d2023-08-19
001022057 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2023-08-19
001022057 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2023-08-19
001022057 920__ $$lyes
001022057 9201_ $$0I:(DE-Juel1)IAS-8-20210421$$kIAS-8$$lDatenanalyse und Maschinenlernen$$x0
001022057 980__ $$ajournal
001022057 980__ $$aVDB
001022057 980__ $$aUNRESTRICTED
001022057 980__ $$aI:(DE-Juel1)IAS-8-20210421
001022057 9801_ $$aFullTexts