20.500.12556/DKUM-77279 Selektivno zaznavanje in odstranjevanje plevela Selective detection and removal of weed 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. 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 %. plevel škropljenje strojni vid weeds spraying machine visions true true false [U. Kenda] Slovenski jezik Angleški jezik Diplomsko delo/naloga 2020-08-25 09:11:31 2020-11-03 07:54:13 2022-08-09 14:36:09 2020-11-20 03:18:39 2020 0 Maribor 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 621.865.8(043.2) 38393347 URN:SI:UM:DK:IKODU7ER UN_Kenda_Urban_2020.pdf UN_Kenda_Urban_2020.pdf 1 71EDC8BB3558589BE2F57F2CB0C243A9 012fc188b33ca8a42d2706a31eb589d8a247d9c182e278d00b5590f332a9aa69 bbee771f-7c11-11eb-bb7a-00155d0001ca 20.500.12556/dkum/a0f65b96-98e9-489b-9c30-9bce57e53c66 https://dk.um.si/Dokument.php?lang=slv&id=144575 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0