Using UAV-Based Photogrammetry to Obtain Correlation between the Vegetation Indices and Chemical Analysis of Agricultural Crops

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dc.contributor.authorJanoušek, Jiřícs
dc.contributor.authorJambor, Václavcs
dc.contributor.authorMarcoň, Petrcs
dc.contributor.authorDohnal, Přemyslcs
dc.contributor.authorSynková, Hanacs
dc.contributor.authorFiala, Pavelcs
dc.coverage.issue10cs
dc.coverage.volume13cs
dc.date.issued2021-05-11cs
dc.description.abstractThe optimum corn harvest time differs between individual harvest scenarios, depending on the intended use of the crop and on the technical equipment of the actual farm. It is therefore economically significant to specify the period as precisely as possible. The harvest maturity of silage corn is currently determined from the targeted sampling of plants cultivated over large areas. In this context, the paper presents an alternative, more detail-oriented approach for estimating the correct harvest time; the method focuses on the relationship between the ripeness data obtained via photogrammetry and the parameters produced by the chemical analysis of corn. The relevant imaging methodology utilizing a spectral camera-equipped unmanned aerial vehicle (UAV) allows the user to acquire the spectral reflectance values and to compute the vegetation indices. Furthermore, the authors discuss the statistical data analysis centered on both the nutritional values found in the laboratory corn samples and on the information obtained from the multispectral images. This discussion is associated with a detailed insight into the computation of correlation coefficients. Statistically significant linear relationships between the vegetation indices, the normalized difference red edge index (NDRE) and the normalized difference vegetation index (NDVI) in particular, and nutritional values such as dry matter, starch, and crude protein are evaluated to indicate different aspects of and paths toward predicting the optimum harvest time. The results are discussed in terms of the actual limitations of the method, the benefits for agricultural practice, and planned research.en
dc.formattextcs
dc.format.extent1-20cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationRemote Sensing. 2021, vol. 13, issue 10, p. 1-20.en
dc.identifier.doi10.3390/rs13101878cs
dc.identifier.issn2072-4292cs
dc.identifier.orcid0000-0002-9940-4966cs
dc.identifier.orcid0000-0001-7349-8426cs
dc.identifier.orcid0000-0003-1163-4458cs
dc.identifier.orcid0000-0002-7203-9903cs
dc.identifier.other171496cs
dc.identifier.researcheridD-2123-2012cs
dc.identifier.researcheridAAE-2611-2021cs
dc.identifier.researcheridF-7778-2018cs
dc.identifier.scopus37063396000cs
dc.identifier.scopus37062829400cs
dc.identifier.scopus15049262200cs
dc.identifier.urihttp://hdl.handle.net/11012/196730
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofRemote Sensingcs
dc.relation.urihttps://www.mdpi.com/2072-4292/13/10/1878cs
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.subjectmultispectral imagingen
dc.subjectvegetation indicesen
dc.subjectnutritional analysisen
dc.subjectcorrelationen
dc.subjectphotogrammetryen
dc.subjectoptimal harvest timeen
dc.subjectUAVen
dc.titleUsing UAV-Based Photogrammetry to Obtain Correlation between the Vegetation Indices and Chemical Analysis of Agricultural Cropsen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-171496en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:42:48en
sync.item.modts2025.01.17 16:51:12en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav teoretické a experimentální elektrotechnikycs
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