Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11779/1159
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dc.contributor.advisorÖzlük, Özgür-
dc.contributor.authorAnuşlu, Timuçin-
dc.date.accessioned2019-11-12T13:41:59Z
dc.date.available2019-11-12T13:41:59Z
dc.date.issued2017-
dc.identifier.citationAnuşlu, T. (2017). Smart precision agriculture with autonomous irrigation system using rnn-based techniques, MEF Üniversitesi Fen Bilimleri Enstitüsü, İstanbul, Türkiyeen_US
dc.identifier.urihttps://hdl.handle.net/20.500.11779/1159-
dc.description.abstractThe study presents a solution to improve freshwater usage for irrigation in the agriculture by building a neural network model to predict soil moisture at 20 cm level with time series data over longer periods of time.en_US
dc.language.isoenen_US
dc.publisherMEF Üniversitesi, Fen Bilimleri Enstitüsüen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectLong Short-Term Memory Networksen_US
dc.subjectRecurrent Neural Networken_US
dc.subjectSmart Precision Agricultureen_US
dc.subjectIrrigation Systemen_US
dc.subjectInternet of Thingsen_US
dc.subjectUzun Kısa-Vadeli Hafıza Ağlarıen_US
dc.subjectYinelenen Yapay Sinir Ağlarıen_US
dc.subjectAkıllı Hassas Tarımen_US
dc.subjectSulama Sistemlerien_US
dc.subjectNesnelerin İnternetien_US
dc.titleSmart precision agriculture with autonomous irrigation system using rnn-based techniquesen_US
dc.title.alternativeYapay sınır ağlarına dayanan teknikler kullanan otonom sulama sistemleri ile akıllı hassas tarımen_US
dc.typeMaster's Degree Projecten_US
dc.relation.publicationcategoryYL-Bitirme Projesien_US
dc.departmentBüyük Veri Analitigi Yüksek Lisans Programıen_US
dc.institutionauthorAnuşlu, Timuçin-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.openairetypeMaster's Degree Project-
Appears in Collections:FBE, Yüksek Lisans, Proje Koleksiyonu
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