Please use this identifier to cite or link to this item: http://repository.ipb.ac.id/handle/123456789/108666
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dc.contributor.advisorSeminar, Kudang Boro-
dc.contributor.advisorSudradjat-
dc.contributor.authorYumna, Dafa Satria Nawal-
dc.date.accessioned2021-08-23T06:42:36Z-
dc.date.available2021-08-23T06:42:36Z-
dc.date.issued2021-
dc.identifier.urihttp://repository.ipb.ac.id/handle/123456789/108666-
dc.description.abstractTanaman teh (Camellia sinensis) merupakan salah satu komoditas perkebunan yang penting di Indonesia. Pendugaan produksi bermanfaat untuk memudahkan pemantauan kondisi tanaman. Informasi mengenai hasil produksi teh dapat dijadikan pertimbangan dalam pengambilan keputusan pada permasalahan yang terdapat di kebun teh. Penelitian ini dilakukan untuk menentukan model penduga produktivitas teh kering, melakukan pendugaan produksi teh kering dan melakukan pemetaan produktivitas teh kering berdasarkan hasil pendugaan pada tanaman teh di PTPN VIII Sinumbra menggunakan citra satelit Sentinel-2. Penelitian diawali dengan pengambilan data sekunder berupa data produksi teh di PTPN VIII Sinumbra dan pengunduhan citra satelit Sentinel-2 pada seluruh bulan pada tahun 2019 dan 2020. Koreksi citra dilakukan menggunakan plug-in Sen2Cor pada aplikasi SNAP, kemudian citra ditransformasikan dengan indeks vegetasi ARVI. ... dstid
dc.description.abstractTea plant (Camellia sinensis) is one of the important plantation commodities in Indonesia. Production estimation is useful for monitoring plant condition. Information about the tea production can be used as a consideration in decisions making on the problems contained in the tea field. This research was conducted to determine estimator model of the dry tea productivity, to estimate dry tea production and to mapping dry tea productivity based on the estimation results on tea plants at PTPN VIII Sinumbra using Sentinel-2 satellite imagery with a vegetation index approach. The study begins with retrieval of secondary data in the form of production data and downloading of Sentinel-2 satellite images for all months in 2019 and 2020. Image correction is carried out using the Sen2Cor plugin in the SNAP application, then the image is transformed with the ARVI vegetation index. The productivity estimator model was built using the median ARVI value for each block. ... dstid
dc.language.isoidid
dc.publisherIPB Universityid
dc.titlePendugaan Produksi Pucuk Teh Menggunakan Citra Satelit Sentinel-2 dengan Pendekatan Analisis Indeks Vegetasiid
dc.title.alternativeTea Leaf Shots Production Estimation Using Sentinel-2 Satellite Image with Vegetation Index Analysis Approach.id
dc.typeUndergraduate Thesisid
dc.subject.keywordestimationid
dc.subject.keywordmodelid
dc.subject.keywordproductivityid
dc.subject.keywordteaid
dc.subject.keywordvegetation indexid
Appears in Collections:UT - Agricultural and Biosystem Engineering

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Cover.pdf
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F14170083_Dafa Satria Nawal Yumna.pdf
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Fullteks1.46 MBAdobe PDFView/Open
Lampiran.pdf
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Lampiran271.47 kBAdobe PDFView/Open


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