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

Astor, Thomas; Dayananda, Supriya; Nautiyal, Sunil; Wachendorf, Michael

dc.date.accessioned	2020-11-20T11:53:54Z
dc.date.available	2020-11-20T11:53:54Z
dc.date.issued	2020-10-19
dc.identifier	doi:10.17170/kobra-202011202229
dc.identifier.uri	http://hdl.handle.net/123456789/12007
dc.description.sponsorship	Gefördert durch den Publikationsfonds der Universität Kassel
dc.language.iso	eng
dc.rights	Namensnennung 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	*
dc.subject	multi-source data combination	eng
dc.subject	vegetable biomass	eng
dc.subject	hyperspectral	eng
dc.subject	point cloud analysis	eng
dc.subject.ddc	004
dc.subject.ddc	540
dc.title	Vegetable Crop Biomass Estimation Using Hyperspectral and RGB 3D UAV Data	eng
dc.type	Aufsatz
dcterms.abstract	Remote 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.accessRights	open access
dcterms.creator	Astor, Thomas
dcterms.creator	Dayananda, Supriya
dcterms.creator	Nautiyal, Sunil
dcterms.creator	Wachendorf, Michael
dc.relation.doi	doi:10.3390/agronomy10101600
dc.subject.swd	Fernerkundung	ger
dc.subject.swd	Cloud Computing	ger
dc.subject.swd	Biomasse	ger
dc.type.version	publishedVersion
dcterms.source.identifier	EISSN 2073-4395
dcterms.source.issue	Issue 10
dcterms.source.journal	Agronomy	eng
dcterms.source.volume	Volume 10
kup.iskup	false
dcterms.source.articlenumber	1600