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Title:Prepoznava invazivnih polžev z uporabo globokega učenja : diplomsko delo
Authors:ID Herodež, Kristjan (Author)
ID Fister, Iztok (Mentor) More about this mentor... New window
ID Vrbančič, Grega (Comentor)
Files:.pdf VS_Herodez_Kristjan_2023.pdf (3,43 MB)
MD5: CEE85F65B035B64FCD6B580C0B378141
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Zaključno delo se osredotoča na podvejo umetne inteligence, ki se imenuje strojno učenje. V zaključnem delu predstavljamo uporabo in implementacijo strojnega učenja na različnih področjih. Znotraj zaključnega dela se podrobneje osredotočamo na pametno kmetijstvo, katerega osrednja tematika v tej nalogi je odkrivanje škodljivcev, ki so v našem primeru polži Arion rufus. Kot rešitev problema je predstavljeno globoko učenje oz. uporaba konvolucijskih nevronskih mrež. V ta namen omenimo tudi različne pristope za učenje modelov računalniškega vida. Rešitev smo našli v pristopu YOLO (You only look once) v katerem smo izdelali naš model vida in ga primerjali s podobno študijo.
Keywords:Arion rufus, Globoko učenje, Pametno kmetijstvo, Umetna inteligenca
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[K. Herodež]
Year of publishing:2023
Number of pages:1 spletni vir (1 datoteka PDF (IX, 46 f.))
PID:20.500.12556/DKUM-85437 New window
UDC:004.85(043.2)
COBISS.SI-ID:171251203 New window
Publication date in DKUM:05.10.2023
Views:243
Downloads:30
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:29.08.2023

Secondary language

Language:English
Title:Detection of invasive snails using deep learning
Abstract:The thesis focuses on a branch of artificial intelligence called machine learning. In it we present the use and implementation of machine learning in various fields. Primary focus is given to the branch of smart agriculture, whose central theme in this assignment is solving the problem of pest detection, which in our case are Arion rufus snails. Deep learning is presented as a solution to the problem using convolutional neural networks. For this purpose, we also mention different approaches for creating models of computer vision. We found a solution in the YOLO (You only look once) approach, in which we created our vision model and compared it with a similar study.
Keywords:Arion rufus, Deep learning, Smart agriculture, Artificial intelligence


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