Estimating crop cover fraction from digital color images

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dc.contributor.authorKarakus P.
dc.contributor.authorKarabork H.
dc.date.accessioned2020-03-26T19:43:47Z
dc.date.available2020-03-26T19:43:47Z
dc.date.issued2017
dc.departmentSelçuk Üniversitesien_US
dc.description4th International GeoAdvances Workshop - GeoAdvances 2017: ISPRS Workshop on Multi-Dimensional and Multi-Scale Spatial Data Modeling -- 14 October 2017 through 15 October 2017 -- 132000en_US
dc.description.abstractThe use of automated methods to estimate crop cover fraction from digital color images has increased in recent years. Crop cover fraction can determine accurate, fast and inexpensive with this methods. A digital color images was acquired over each of the 30 sample fields in 2014 year at 2-3 week intervals. Study area has 15 sunflower fields and 15 corn fields. Digital color images were collected during 4 months, namely over the course of the growing season from sowing until harvesting to determine crop cover fraction. We used two approach to estimate crop cover fraction. In first method, the images were transformed from the RGB (red, green, blue) color space to the HSI (hue, intensity, saturation) color space. We used an object-based image analysis approach to classify the images into green vegetation and the other materials. In the second method, The Green Crop Tracker is less labor and time intensive than the object-based classification approach, is a viable alternative to ground-based methods. By comparing object-based classification method and Green Crop Tracker software 2014 growing season, results were obtained: There were high correlations between the estimations obtained by object-based classification method and Green Crop Tracker software (for 2014 R2=0.89). The relationship between two methods for 2014-23 sunflower field was calculated R2=0.97. © Authors 2017.en_US
dc.identifier.doi10.5194/isprs-archives-XLII-4-W6-67-2017en_US
dc.identifier.endpage68en_US
dc.identifier.issn1682-1750en_US
dc.identifier.issue4W6en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage67en_US
dc.identifier.urihttps://dx.doi.org/10.5194/isprs-archives-XLII-4-W6-67-2017
dc.identifier.urihttps://hdl.handle.net/20.500.12395/35756
dc.identifier.volume42en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInternational Society for Photogrammetry and Remote Sensingen_US
dc.relation.ispartofInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archivesen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectCornen_US
dc.subjectCrop cover fractionen_US
dc.subjectDigital Cameraen_US
dc.subjectObject-based classificationen_US
dc.subjectRGB to HSIen_US
dc.subjectSunfloweren_US
dc.subjectThe green crop trackeren_US
dc.titleEstimating crop cover fraction from digital color imagesen_US
dc.typeConference Objecten_US

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