Publication:

Ultra-high-resolution UAV-imaging and supervised deep learning for accurate detection of Alternaria solani in potato fields

Date

2024
Journal article
https://doi.org/10.3389/fpls.2024.1206998
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dc.contributor.authorWieme, Jana
dc.contributor.authorLeroux, Sam
dc.contributor.authorCool, Simon R.
dc.contributor.authorVan Beek, Jonathan
dc.contributor.authorPieters, Jan G.
dc.contributor.authorMaes, Wouter H.
dc.contributor.imecauthorWieme, Jana
dc.contributor.imecauthorLeroux, Sam
dc.contributor.orcidimecLeroux, Sam::0000-0003-3792-5026
dc.date.accessioned2024-11-25T14:56:12Z
dc.date.available2024-04-01T18:09:11Z
dc.date.available2024-04-02T06:54:02Z
dc.date.available2024-11-25T14:56:12Z
dc.date.embargo2024-03-05
dc.date.issued2024
dc.description.wosFundingTextThe author(s) declare financial support was received for the research, authorship, and/or publication of this article. JW is funded by grant 1SE3921N of Research Foundation Flanders (FWO). Experimental data was gathered within the Proeftuin Smart Farming 4.0 project (180503) within the Industry 4.0 Living Labs with funding from Flanders innovation & entrepreneurship (VLAIO, Belgium) and in the Horizon 2020 project SmartAgriHubs - Connecting the dots to unleash the innovation potential for digital transformation of the European agrifood sector with funding from the European Union under grant agreement No. 818182.
dc.identifier.doi10.3389/fpls.2024.1206998
dc.identifier.issn1664-462X
dc.identifier.pmidMEDLINE:38504902
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/43755
dc.publisherFRONTIERS MEDIA SA
dc.source.beginpageArt. 1206998
dc.source.endpageN/A
dc.source.issueN/A
dc.source.journalFRONTIERS IN PLANT SCIENCE
dc.source.numberofpages17
dc.source.volume15
dc.subject.keywordsEARLY BLIGHT
dc.subject.keywordsRESISTANCE
dc.title

Ultra-high-resolution UAV-imaging and supervised deep learning for accurate detection of Alternaria solani in potato fields

dc.typeJournal article
dspace.entity.typePublication
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