Correlation of yield and vegetation indices from unmanned aerial vehicle multispectral imagery in Thailand rice production systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F25%3A00635882" target="_blank" >RIV/86652079:_____/25:00635882 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/62156489:43210/25:43927007

Výsledek na webu

<a href="https://acsess.onlinelibrary.wiley.com/doi/10.1002/agg2.70107" target="_blank" >https://acsess.onlinelibrary.wiley.com/doi/10.1002/agg2.70107</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/agg2.70107" target="_blank" >10.1002/agg2.70107</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Correlation of yield and vegetation indices from unmanned aerial vehicle multispectral imagery in Thailand rice production systems

Popis výsledku v původním jazyce

Unmanned aerial vehicles (UAVs) equipped with cameras are used for collecting vegetation indices (VIs) for health monitoring of field crops such as rice (Oryza sativa). This study evaluated the relationship of VIs derived from multispectral UAV images at different buffer zones around the sampling points with rice biomass and grain yield from 20 nonirrigated and irrigated fields in Sakhon Nakhon, Thailand, in 2021. Varying nitrogen (N) rates (21.8-98.7 kg ha-1) were applied in splits, at 14 days after transplanting and panicle initiation (PI) stage. One week after PI, multispectral images were captured by a DJI Phantom 4 Multispectral UAV before taking biomass samples at four 1-m2 sampling points for the three plots in each field. At harvest, whole plant samples were collected from nearby these sampling points for grain yield estimation. For each sampling point at buffer zones 5, 10, and 20 m, the average of four VIs (normalized difference vegetation index [NDVI], green NDVI [GNDVI], normalized difference red-edge [NDRE], and optimized soil adjusted vegetation index [OSAVI]) was computed. Correlation analysis showed NDRE had the highest correlation with grain yield in nonirrigated systems (r = 0.575-0.613) and pooled data (r = 0.523-0.539). NDVI moderately correlated with biomass (r = 0.224-0.233). Images within the 10-m buffer zone produced NDRE values most strongly linked to yield, both unadjusted and normalized by planting to sensing days and growing degree days. There is a potential to use NDRE as predictor of rice biomass yield in rice systems.

Název v anglickém jazyce

Correlation of yield and vegetation indices from unmanned aerial vehicle multispectral imagery in Thailand rice production systems

Popis výsledku anglicky

Unmanned aerial vehicles (UAVs) equipped with cameras are used for collecting vegetation indices (VIs) for health monitoring of field crops such as rice (Oryza sativa). This study evaluated the relationship of VIs derived from multispectral UAV images at different buffer zones around the sampling points with rice biomass and grain yield from 20 nonirrigated and irrigated fields in Sakhon Nakhon, Thailand, in 2021. Varying nitrogen (N) rates (21.8-98.7 kg ha-1) were applied in splits, at 14 days after transplanting and panicle initiation (PI) stage. One week after PI, multispectral images were captured by a DJI Phantom 4 Multispectral UAV before taking biomass samples at four 1-m2 sampling points for the three plots in each field. At harvest, whole plant samples were collected from nearby these sampling points for grain yield estimation. For each sampling point at buffer zones 5, 10, and 20 m, the average of four VIs (normalized difference vegetation index [NDVI], green NDVI [GNDVI], normalized difference red-edge [NDRE], and optimized soil adjusted vegetation index [OSAVI]) was computed. Correlation analysis showed NDRE had the highest correlation with grain yield in nonirrigated systems (r = 0.575-0.613) and pooled data (r = 0.523-0.539). NDVI moderately correlated with biomass (r = 0.224-0.233). Images within the 10-m buffer zone produced NDRE values most strongly linked to yield, both unadjusted and normalized by planting to sensing days and growing degree days. There is a potential to use NDRE as predictor of rice biomass yield in rice systems.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40101 - Agriculture

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Agrosystems Geosciences & Environment

ISSN

2639-6696

e-ISSN

2639-6696

Svazek periodika

8

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

e70107

Kód UT WoS článku

001482449900001

EID výsledku v databázi Scopus

2-s2.0-105004643937