Vegetable Crop Biomass Estimation Using Hyperspectral and RGB 3D UAV Data

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dc.date.accessioned2020-11-20T11:53:54Z
dc.date.available2020-11-20T11:53:54Z
dc.date.issued2020-10-19
dc.description.sponsorshipGefördert durch den Publikationsfonds der Universität Kassel
dc.identifierdoi:10.17170/kobra-202011202229
dc.identifier.urihttp://hdl.handle.net/123456789/12007
dc.language.isoeng
dc.relation.doidoi:10.3390/agronomy10101600
dc.rightsNamensnennung 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectmulti-source data combinationeng
dc.subjectvegetable biomasseng
dc.subjecthyperspectraleng
dc.subjectpoint cloud analysiseng
dc.subject.ddc004
dc.subject.ddc540
dc.subject.swdFernerkundungger
dc.subject.swdCloud Computingger
dc.subject.swdBiomasseger
dc.titleVegetable Crop Biomass Estimation Using Hyperspectral and RGB 3D UAV Dataeng
dc.typeAufsatz
dc.type.versionpublishedVersion
dcterms.abstractRemote sensing (RS) has been an e ective tool to monitor agricultural production systems, but for vegetable crops, precision agriculture has received less interest to date. The objective of this study was to test the predictive performance of two types of RS data—crop height information derived from point clouds based on RGB UAV data, and reflectance information from terrestrial hyperspectral imagery—to predict fresh matter yield (FMY) for three vegetable crops (eggplant, tomato, and cabbage). The study was conducted in an experimental layout in Bengaluru, India, at five dates in summer 2017. The prediction accuracy varied strongly depending on the RS dataset used. For all crops, a good predictive performance with cross-validated prediction error < 10% was achieved. The growth stage of the crops had no significant e ect on the prediction accuracy, although increasing trends of an underestimation of FMY with later sampling dates for eggplant and tomato were found. The study proves that an estimation of vegetable FMY using RS data is successful throughout the growing season. Di erent RS datasets were best for biomass prediction of the three vegetables, indicating that multi-sensory data collection should be preferred to single sensor use, as no one sensor system is superior.eng
dcterms.accessRightsopen access
dcterms.creatorAstor, Thomas
dcterms.creatorDayananda, Supriya
dcterms.creatorNautiyal, Sunil
dcterms.creatorWachendorf, Michael
dcterms.source.articlenumber1600
dcterms.source.identifierEISSN 2073-4395
dcterms.source.issueIssue 10
dcterms.source.journalAgronomyeng
dcterms.source.volumeVolume 10
kup.iskupfalse

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