Publication:
Optimizing Data Collection in Precision Agriculture – Comparing Remote Sensing and in Situ Analyses

dc.authorscopusid57990379200
dc.authorscopusid57190133139
dc.authorscopusid57194973080
dc.authorscopusid16052385200
dc.authorscopusid6504149893
dc.contributor.authorKebede, E.A.
dc.contributor.authorVasileva, S.
dc.contributor.authorIvanov, B.
dc.contributor.authorDengiz, O.
dc.contributor.authorBojinov, B.
dc.date.accessioned2025-12-11T00:32:58Z
dc.date.issued2024
dc.departmentOndokuz Mayıs Üniversitesien_US
dc.department-temp[Kebede] Endalkachew Abebe, Agricultural University of Plovdiv, Plovdiv, Bulgaria, Department of Soil Science and Plant Nutrition, Ondokuz Mayis Üniversitesi, Samsun, Turkey; [Vasileva] Silviya, Agricultural University of Plovdiv, Plovdiv, Bulgaria; [Ivanov] Bozhidar, Institute of Agricultural Economics – Sofia, Sofia, Bulgaria; [Dengiz] Orhan, Department of Soil Science and Plant Nutrition, Ondokuz Mayis Üniversitesi, Samsun, Turkey; [Bojinov] Bojin M., Agricultural University of Plovdiv, Plovdiv, Bulgariaen_US
dc.description.abstractRemote sensing has a potential application in assessing and monitoring the plants’ biophysical properties using the spectral responses of plants and soils within the electromagnetic spectrum. However, only a few reports compare the performance of different remote sensing approaches against in-situ spectral measurement. The current study assessed potential applications of open data source satellite images (Sentinel 2 and Landsat 9) in estimating the biophysical properties of a crop on a study farm. A Landsat 9 (30 m resolution) and Sentinel-2 (10 m resolution) satellite images with less than 10% cloud cover have been extracted from the open data sources for the period of December 2021 – April 2022. In addition, SpectraVue 710s Leaf Spectrometer was used to measure the spectral response of the crop in April at five different locations within the same field. Results obtained by different data collection methods were compared to evaluate them for applicability in precision agriculture. © 2024, Agricultural Academy, Bulgaria. All rights reserved.en_US
dc.identifier.endpage16en_US
dc.identifier.issn1310-0351
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85188254358
dc.identifier.scopusqualityQ3
dc.identifier.startpage11en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12712/37298
dc.identifier.volume30en_US
dc.language.isoenen_US
dc.publisherAgricultural Academy, Bulgariaen_US
dc.relation.ispartofBulgarian Journal of Agricultural Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectNDVIen_US
dc.subjectPrecision Agricultureen_US
dc.subjectVegetation Indicesen_US
dc.titleOptimizing Data Collection in Precision Agriculture – Comparing Remote Sensing and in Situ Analysesen_US
dc.typeArticleen_US
dspace.entity.typePublication

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