20.500.12556/DKUM-74521 Zaznava bolezni in fizioloških motenj pšenice na podlagi sprememb vegetacijskih indeksov Detection of diseases nad physiological disturbances of wheat based on vegetation index charges V zadnjem času se uvajajo droni oz. brezpilotna letala (uav) za spremljanje razvoja rastlin. Prednosti uporabe se kažejo v preprostem načrtovanju, nemotenem delovanju in pridobivanju posnetkov iz ptičje perspektive. S prenosom sistemov multispektralnih kamer in nizko višino leta zagotavljajo visoko ločljivost rastlin. Pšenica je kultura, ki se goji v največjem obsegu površin, zato ponuja velik interes po pridobitvi prostorskih in časovnih informacijah o rastlini. V nalogi smo prikazali določevanje različnih vegetacijskih indeksov na osnovi multispektralnih posnetkov z droni. Snemanja so bila izvedena na površinah podjetja JERUZALEM SAT d.d. v različnih obdobjih rasti pšenice. Na osnovi zajetih posnetkov smo s pomočjo programske opreme Pix4Dfields generirali različne vegetacijske indekse in poiskali vzorčne povezave s stanjem na polju. Pridobljeni podatki omogočajo selektivne nanose gnojil in fitofarmacevtskih sredstev ter tako pripomorejo k prihrankom v proizvodnji in zmanjšanim obremenitvam okolja. Recently, unmanned aerial vehicles have been introduced into agriculture to monitor plant development. The main advantages are simple planning, immediate operation and cloud-based imaging. By wearing multispectral camera system and with low altitude of the flight UAVs assure high-resolution images of plants. Wheat is a crop grown on the largest surface area and therefore trigger interesting for acquisition of spatial and temporal information. In the thesis, we have shown the determination of different vegetation indices based on multispectral drone imagery. The recordings were made on the land of JERUZALEM SAT d.d. at different stages of wheat growth. Based on the captured images, various vegetation indices were generated using Pix4Dfields software to find sample connections to the real state of the field. Obtained data allow us to selectively apply fertilizers and plant protection products and they contribute to saving in production and reduce environmental burden. dron pšenica multispektralna kamera vegetacijski indeksi daljinsko zaznavanje drone wheat multispectral camera vegetation indices remote sensing true true false Slovenski jezik Angleški jezik Diplomsko delo/naloga 2019-08-29 18:25:19 2019-09-09 08:46:39 2022-08-09 09:11:52 2019-09-13 03:13:19 2019 0 Maribor 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 URN:SI:UM:DK:UBAPYTVH VS_Fridrih_Gasper_2019.pdf VS_Fridrih_Gasper_2019.pdf 1 646B6464C8636F60E11FEBF0482D4AE7 be97aec56dcaa3b17079400a77b53f7a3f8cdf27ec303f1e4d66c350264dc5ef e0d06436-7c10-11eb-bb7a-00155d0001ca 20.500.12556/dkum/997b3905-9903-473b-8ea2-7633766cc6d7 https://dk.um.si/Dokument.php?lang=slv&id=136849 Fakulteta za kmetijstvo in biosistemske vede 0 0 0