Crop height estimation of sorghum from high resolution multispectral images using the structure from motion (SfM) algorithm

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dc.authoridTunca, Emre/0000-0001-6869-9602en_US
dc.authoridCetin Taner, Sakine/0000-0002-7333-4250en_US
dc.authoridAKAY, HASAN/0000-0003-1198-8686en_US
dc.authorscopusid57204446671en_US
dc.authorscopusid24344113900en_US
dc.authorscopusid58042661200en_US
dc.authorscopusid56868366700en_US
dc.authorwosidTunca, Emre/IQT-3077-2023en_US
dc.authorwosidCetin Taner, Sakine/JUV-5054-2023en_US
dc.authorwosidAKAY, HASAN/T-9305-2018en_US
dc.contributor.authorTunca, E.
dc.contributor.authorKoksal, E. S.
dc.contributor.authorTaner, S. Cetin
dc.contributor.authorAkay, H.
dc.date.accessioned2024-08-23T16:04:59Z
dc.date.available2024-08-23T16:04:59Z
dc.date.issued2023en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractCrop height (CH) is the key indicators of crop growth, biomass and yield. However, obtaining CH information with manual measurement is inefficient for larger areas. High-resolution unmanned air vehicle (UAV) images offer a new alternative to traditional CH measurements. In this study, we compared three approaches to estimate sorghum CH using high-resolution multispectral images based on structure from motion (SfM) algorithm and spectral vegetation indices. In the first approach, CH was estimated based on the difference between the Digital Surface Model (DSM) map and Digital Terrain Model (DTM) map generated from UAV images captured immediately after the sowing. In the second approach, DTM was generated from DSM. In the last approach, CH was estimated using the spectral vegetation indices. High-resolution multispectral images were obtained at 40 m above ground level elevation. Ground control points were laid around the study area, and these point positions were determined using a GPS device. DSM and DTM images were generated from 3D point cloud data and the SfM algorithm. Results showed that the SfM technique could estimate sorghum CH accurately using DSM, DTM and GCPs (R2 = 0.97, RMSE = 8.77 cm, MAPE = 5.98%). Also, a high correlation was observed between estimated and measured sorghum CH using DTM maps generated from DSM maps (R2, RMSE, MAPE were 0.94, 12.2 cm, 6.66%). Moreover, GNDVI was the best vegetation index to estimate sorghum CH (R2 = 0.81, RMSE = 24.6 cm, MAPE = 12.56%). Overall, this study demonstrates the UAV potential for CH estimates and reducing the cost of obtaining CH information.en_US
dc.description.sponsorshipThis research was supported by Scientific and Technological Research Council of Turkey (Grant Numbers: 118O831) [118O831]; Scientific and Technological Research Council of Turkeyen_US
dc.description.sponsorshipThis research was supported by Scientific and Technological Research Council of Turkey (Grant Numbers: 118O831)en_US
dc.identifier.doi10.1007/s13762-023-05265-1
dc.identifier.issn1735-1472
dc.identifier.issn1735-2630
dc.identifier.scopus2-s2.0-85174972451en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1007/s13762-023-05265-1
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14442
dc.identifier.wosWOS:001096134100002en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofInternational Journal of Environmental Science and Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectUnmanned air vehicleen_US
dc.subjectSorghumen_US
dc.subjectCrop height estimationen_US
dc.subjectStructure from motionen_US
dc.subjectGNDVIen_US
dc.subjectDigital surface modelen_US
dc.subjectUav-Based Rgben_US
dc.subjectPlant Heighten_US
dc.subjectVegetation Indexen_US
dc.subjectBiomassen_US
dc.subjectDensityen_US
dc.titleCrop height estimation of sorghum from high resolution multispectral images using the structure from motion (SfM) algorithmen_US
dc.typeArticleen_US

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