Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging

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dc.contributor.authorJanoušek, Jiřícs
dc.contributor.authorMarcoň, Petrcs
dc.contributor.authorDohnal, Přemyslcs
dc.contributor.authorJambor, Václavcs
dc.contributor.authorSynková, Hanacs
dc.contributor.authorRaichl, Petrcs
dc.coverage.issue12cs
dc.coverage.volume15cs
dc.date.accessioned2023-07-24T06:03:42Z
dc.date.available2023-07-24T06:03:42Z
dc.date.issued2023-06-16cs
dc.description.abstractEstimating the optimum harvest time and yield embodies an essential food security factor. Vegetation indices have proven to be an effective tool for widescale in-field plant health mapping. A drone-based multispectral camera then conveniently allows acquiring data on the condition of the plant. This article examines and discusses the relationships between vegetation indices and nutritiolnal values that have been determined via chemical analysis of plant samples collected in the field. In this context, emphasis is placed on the normalized difference red edge index (NDRE), normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and nutritional values, such as those of dry matter. The relationships between the variables were correlated and described by means of regression models. This produced equations that are applicable for estimating the quantity of dry matter and thus determining the optimum corn harvest time. The obtained equations were validated on five different types of corn hybrids in fields within the South Moravian Region, Moravia, the Czech Republic.en
dc.formattextcs
dc.format.extent1-19cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationRemote Sensing. 2023, vol. 15, issue 12, p. 1-19.en
dc.identifier.doi10.3390/rs15123152cs
dc.identifier.issn2072-4292cs
dc.identifier.orcidD-2123-2012cs
dc.identifier.orcidAAE-2611-2021cs
dc.identifier.other183996cs
dc.identifier.researcherid0000-0002-9940-4966cs
dc.identifier.researcherid0000-0001-7349-8426cs
dc.identifier.researcherid0000-0003-1163-4458cs
dc.identifier.researcherid0000-0003-3341-1204cs
dc.identifier.scopus37063396000cs
dc.identifier.scopus37062829400cs
dc.identifier.scopus57224678357cs
dc.identifier.urihttp://hdl.handle.net/11012/213600
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofRemote Sensingcs
dc.relation.urihttps://www.mdpi.com/2072-4292/15/12/3152cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2072-4292/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectcornen
dc.subjectmultispectral imagingen
dc.subjectvegetation indicesen
dc.subjectnutritional analysisen
dc.subjectcorrelationen
dc.subjectphotogrammetryen
dc.subjectoptimal harvest timeen
dc.subjectUAVen
dc.titlePredicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imagingen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-183996en
sync.item.dbtypeVAVen
sync.item.insts2023.07.25 08:53:12en
sync.item.modts2023.07.25 08:14:38en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav teoretické a experimentální elektrotechnikycs
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