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Title:
Selektivno zaznavanje in odstranjevanje plevela
Authors:
ID
Kenda, Urban
(Author)
ID
Uran, Suzana
(Mentor)
More about this mentor...
ID
Bratina, Božidar
(Comentor)
ID
Rakun, Jurij
(Comentor)
ID
Belšak, Aleš
(Mentor)
More about this mentor...
Files:
UN_Kenda_Urban_2020.pdf
(3,75 MB)
MD5: 71EDC8BB3558589BE2F57F2CB0C243A9
PID:
20.500.12556/dkum/a0f65b96-98e9-489b-9c30-9bce57e53c66
Language:
Slovenian
Work type:
Bachelor thesis/paper
Typology:
2.11 - Undergraduate Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V diplomskem delu smo spoznali robota Farmbeast, različne pristope k škropljenju, osnovne principe iz področja strojnega vida in uporabo robotskega operacijskega sistema. Na podlagi novopridobljenega znanja smo ustvarili sistem, ki je s strojnim vidom sposoben ločevati plevel med ozko- in širokolistnim plevelom. Rezultat prepoznave pa predstavlja vhodni podatek za novo razvito orodje, s katerim je omogočeno škropljenje z dvema različnima fitofarmacevtskima pripravkoma, glede na vrsto plevela. Za prepoznavo sta bila razvita dva različna algoritma, ki omogočata ločevanje plevela in sta bila testirana na 30 vzorcih. Test je pokazal, da prvi način v 93,3 % uspešno loči ozkolisten plevel in je 53,3 % uspešen pri ločevanju širokolistnega plevela, drugi način pa obe vrsti plevela loči 93,3 % uspešno.
Keywords:
plevel
,
škropljenje
,
strojni vid
Place of publishing:
Maribor
Publisher:
[U. Kenda]
Year of publishing:
2020
PID:
20.500.12556/DKUM-77279
UDC:
621.865.8(043.2)
COBISS.SI-ID:
38393347
NUK URN:
URN:SI:UM:DK:IKODU7ER
Publication date in DKUM:
03.11.2020
Views:
990
Downloads:
147
Metadata:
Categories:
KTFMB - FERI
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:
KENDA, Urban, 2020,
Selektivno zaznavanje in odstranjevanje plevela
[online]. Bachelor’s thesis. Maribor : U. Kenda. [Accessed 22 January 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=77279
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Licences
License:
CC BY 4.0, Creative Commons Attribution 4.0 International
Link:
http://creativecommons.org/licenses/by/4.0/
Description:
This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:
25.08.2020
Secondary language
Language:
English
Title:
Selective detection and removal of weed
Abstract:
In this work we learn about robot Farmbeast, different approaches to spraying, basic principles in the field of machine vision and the use of a robotic operating system. Based on new knowledge, we created a system which is able to separate weeds into narrow and wide-leaf sort with machine vision. The result of defining weed sort is used as input data for newly developed tool, which is capable of spraying two different phytopharmaceutical preparations, depending of the type of weed. For defining weed, two different weed separating algorithms were developed and tested on 30 samples. The test showed that first method successfully separates the narrow sort in 93,3 % and is 53,3 % successful in separating wide sort weeds. The second algorithm both of sorts separate correctly in 93,3 %.
Keywords:
weeds
,
spraying
,
machine visions
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