Remote sensing of maize plant height at different growth stages using UAV-based digital surface models (DSM)

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dc.contributor.authorOehme, Leon Hinrich
dc.contributor.authorReineke, Alice-Jacqueline
dc.contributor.authorWeiß, Thea Mi
dc.contributor.authorWürschum, Tobias
dc.contributor.authorHe, Xiongkui
dc.contributor.authorMüller, Joachim
dc.date.accessioned2024-10-23T12:25:48Z
dc.date.available2024-10-23T12:25:48Z
dc.date.issued2022de
dc.description.abstractPlant height of maize is related to lodging resistance and yield and is highly heritable but also polygenic, and thus is an important trait in maize breeding. Various manual methods exist to determine the plant height of maize, yet they are labor-intensive and time consuming. Therefore, we established digital surface models (DSM) based on RGB-images captured by an unmanned aerial vehicle (UAV) at five different dates throughout the growth period to rapidly estimate plant height of 400 maize genotypes. The UAV-based estimation of plant height (PHUAV) was compared to the manual measurement from the ground to the highest leaf (PHL), to the tip of the manually straightened highest leaf (PHS) and, on the final date, to the top of the tassel (PHT). The best results were obtained for estimating both PHL (0.44 ≤ R2 ≤ 0.51) and PHS (0.50 ≤ R2 ≤ 0.61) from 39 to 68 days after sowing (DAS). After calibration the mean absolute percentage error (MAPE) between PHUAV and PHS was in a range from 12.07% to 19.62%. It is recommended to apply UAV-based maize height estimation from 0.2 m average plant height to maturity before the plants start to senesce and change the leaf color.en
dc.identifier.swb1799723178
dc.identifier.urihttps://hohpublica.uni-hohenheim.de/handle/123456789/16799
dc.identifier.urihttps://doi.org/10.3390/agronomy12040958
dc.language.isoengde
dc.rights.licensecc_byde
dc.source2073-4395de
dc.sourceAgronomy; Vol. 12, No. 4 (2022) 958de
dc.subjectPlant height
dc.subjectUnmanned aerial vehicle (UAV)
dc.subjectMaize
dc.subjectHigh-throughput phenotyping
dc.subjectDigital surface model (DSM)
dc.subjectPhotogrammetry
dc.subjectDrone
dc.subject.ddc630
dc.titleRemote sensing of maize plant height at different growth stages using UAV-based digital surface models (DSM)en
dc.type.diniArticle
dcterms.bibliographicCitationAgronomy, 12 (2022), 4, 958. https://doi.org/10.3390/agronomy12040958. ISSN: 2073-4395
dcterms.bibliographicCitation.issn2073-4395
dcterms.bibliographicCitation.issue4
dcterms.bibliographicCitation.journaltitleAgronomy
dcterms.bibliographicCitation.volume12
local.export.bibtex@article{Oehme2022, url = {https://hohpublica.uni-hohenheim.de/handle/123456789/16799}, doi = {10.3390/agronomy12040958}, author = {Oehme, Leon Hinrich and Reineke, Alice-Jacqueline and Weiß, Thea Mi et al.}, title = {Remote Sensing of Maize Plant Height at Different Growth Stages Using UAV-Based Digital Surface Models (DSM)}, journal = {Agronomy}, year = {2022}, volume = {12}, number = {4}, }
local.export.bibtexAuthorOehme, Leon Hinrich and Reineke, Alice-Jacqueline and Weiß, Thea Mi et al.
local.export.bibtexKeyOehme2022
local.export.bibtexType@article

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Institut für Agrartechnik
Institut für Pflanzenzüchtung, Saatgutforschung und Populationsgenetik