Your browser does not allow JavaScript!
JavaScript is necessary for the proper functioning of this website. Please enable JavaScript or use a modern browser.
|
|
SLO
|
ENG
|
Cookies and privacy
DKUM
EPF - Faculty of Business and Economics
FE - Faculty of Energy Technology
FERI - Faculty of Electrical Engineering and Computer Science
FF - Faculty of Arts
FGPA - Faculty of Civil Engineering, Transportation Engineering and Architecture
FKBV - Faculty of Agriculture and Life Sciences
FKKT - Faculty of Chemistry and Chemical Engineering
FL - Faculty of Logistic
FNM - Faculty of Natural Sciences and Mathematics
FOV - Faculty of Organizational Sciences in Kranj
FS - Faculty of Mechanical Engineering
FT - Faculty of Tourism
FVV - Faculty of Criminal Justice and Security
FZV - Faculty of Health Sciences
MF - Faculty of Medicine
PEF - Faculty of Education
PF - Faculty of Law
UKM - University of Maribor Library
UM - University of Maribor
UZUM - University of Maribor Press
COBISS
Faculty of Business and Economic, Maribor
Faculty of Agriculture and Life Sciences, Maribor
Faculty of Logistics, Celje, Krško
Faculty of Organizational Sciences, Kranj
Faculty of Criminal Justice and Security, Ljubljana
Faculty of Health Sciences
Library of Technical Faculties, Maribor
Faculty of Medicine, Maribor
Miklošič Library FPNM, Maribor
Faculty of Law, Maribor
University of Maribor Library
Bigger font
|
Smaller font
Introduction
Search
Browsing
Upload document
For students
For employees
Statistics
Login
First page
>
Show document
Show document
Title:
Prepoznava invazivnih polžev z uporabo globokega učenja : diplomsko delo
Authors:
ID
Herodež, Kristjan
(Author)
ID
Fister, Iztok
(Mentor)
More about this mentor...
ID
Vrbančič, Grega
(Comentor)
Files:
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
UDC:
004.85(043.2)
COBISS.SI-ID:
171251203
Publication date in DKUM:
05.10.2023
Views:
329
Downloads:
50
Metadata:
Categories:
KTFMB - FERI
Cite this work
Plain text
BibTeX
EndNote XML
EndNote/Refer
RIS
ABNT
ACM Ref
AMA
APA
Chicago 17th Author-Date
Harvard
IEEE
ISO 690
MLA
Vancouver
:
HERODEŽ, Kristjan, 2023,
Prepoznava invazivnih polžev z uporabo globokega učenja : diplomsko delo
[online]. Bachelor’s thesis. Maribor : K. Herodež. [Accessed 21 January 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=85437
Copy citation
Average score:
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
(0 votes)
Your score:
Voting is allowed only for
logged in
users.
Share:
Similar works from our repository:
Generiranje gibov plezajočega robota za pregled in vzdrževanje mostov
Bluetooth krmiljenje plezajočega robota za vzdrževanje mostov
Prijemalo za plezajočega robota
Krmilnik za napajalnike LED s pulzno-širinsko modulacijo
Aktivizem blagovnih znamk: priložnost ali tveganje?
Similar works from other repositories:
Komunikacija med zaposlenimi v podjetju
Sodobni načini digitalnega komuniciranja s strankami
Preverjanje kakovosti storitev v gostinskem lokalu
Dejavniki uspešne komunikacije na delovnem mestu
Komunikacija s starši pri delu v vrtcu
Hover the mouse pointer over a document title to show the abstract or click on the title to get all document metadata.
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
Comments
Leave comment
You must
log in
to leave a comment.
Comments (0)
0 - 0 / 0
There are no comments!
Back