Comparing RGB - based vegetation indices from UAV imageries to estimate hops canopy area
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41310%2F20%3A84838" target="_blank" >RIV/60460709:41310/20:84838 - isvavai.cz</a>
Výsledek na webu
<a href="https://dspace.emu.ee/xmlui/handle/10492/6091" target="_blank" >https://dspace.emu.ee/xmlui/handle/10492/6091</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.15159/AR.20.169" target="_blank" >10.15159/AR.20.169</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Comparing RGB - based vegetation indices from UAV imageries to estimate hops canopy area
Popis výsledku v původním jazyce
Remote estimation of hops plants in hop gardens is imperative in field of precision agriculture, because of precise imaging of hop garden structure. Monitoring of hop plant volume and area can help to predict the condition and yield of hops. In this study, two unmanned aerial vehicles (UAV) - eBee X senseFly UAV equipped with Red Green Blue (RGB) S.O.D.A. camera and Vertical Take-Off Landing (VTOL) UAV FireFly6 Pro by BirdsEyeView Aerobotics equipped with MicaSense RedEdge MX camera were used to acquire images of hop garden at phenology stage maturity of cones (24 th July) before harvest. Seven commonly used RGB vegetation indices (VI) were derived from these RGB and multispectral (MS) images after photogrammetric pre-processing and orthophoto mosaic extraction using Pix4Dmapper software. Vegetation Indices as the Green Percentage Index (G%), Excess of Green Index (ExGreen), Green Leaf Index (GLI), Visible Atmospherically Resistant Index (VARI), Red Green Blue Vegetation Index (RGBVI), Nor
Název v anglickém jazyce
Comparing RGB - based vegetation indices from UAV imageries to estimate hops canopy area
Popis výsledku anglicky
Remote estimation of hops plants in hop gardens is imperative in field of precision agriculture, because of precise imaging of hop garden structure. Monitoring of hop plant volume and area can help to predict the condition and yield of hops. In this study, two unmanned aerial vehicles (UAV) - eBee X senseFly UAV equipped with Red Green Blue (RGB) S.O.D.A. camera and Vertical Take-Off Landing (VTOL) UAV FireFly6 Pro by BirdsEyeView Aerobotics equipped with MicaSense RedEdge MX camera were used to acquire images of hop garden at phenology stage maturity of cones (24 th July) before harvest. Seven commonly used RGB vegetation indices (VI) were derived from these RGB and multispectral (MS) images after photogrammetric pre-processing and orthophoto mosaic extraction using Pix4Dmapper software. Vegetation Indices as the Green Percentage Index (G%), Excess of Green Index (ExGreen), Green Leaf Index (GLI), Visible Atmospherically Resistant Index (VARI), Red Green Blue Vegetation Index (RGBVI), Nor
Klasifikace
Druh
J<sub>SC</sub> - Článek v periodiku v databázi SCOPUS
CEP obor
—
OECD FORD obor
40101 - Agriculture
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2020
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Agronomy Research
ISSN
1406-894X
e-ISSN
—
Svazek periodika
18
Číslo periodika v rámci svazku
4
Stát vydavatele periodika
CZ - Česká republika
Počet stran výsledku
10
Strana od-do
2592-2601
Kód UT WoS článku
—
EID výsledku v databázi Scopus
2-s2.0-85098115020