Paddy rice traits estimation under varying management strategies using UAV technology

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43410%2F25%3A43926539" target="_blank" >RIV/62156489:43410/25:43926539 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1002/agg2.70047" target="_blank" >https://doi.org/10.1002/agg2.70047</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/agg2.70047" target="_blank" >10.1002/agg2.70047</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Paddy rice traits estimation under varying management strategies using UAV technology

Popis výsledku v původním jazyce

Timely crop monitoring and yield prediction are essential in guiding management decision making. The aim of the study was to estimate the agronomic traits of paddy rice (Oryza sativa L.) using unmanned aerial vehicle (UAV)-multispectral imaging. A randomized complete block design field experiment with a split-split plot arrangement was set up in the Ruzizi plain, Democratic Republic of Congo (DRC). Spectral imaging data were collected at rice tillering and panicle initiation stages. Predictive analysis of rice agronomic traits was performed using linear and decision tree-based machine learning techniques. Paddy rice trait predictions were critically sensitive to the timing of image acquisition but not largely affected by the model. The most accurate predictions were made at rice panicle initiation stage, with R2 values of 0.62, 0.65, and 0.75 for yield, aboveground biomass, and plant nitrogen (N) uptake, respectively. The visible atmospherically resistant index (VARI), modified chlorophyll absorption in reflective index, and ratio vegetation index, along with near infrared and green bands, played a critical role in predicting paddy rice N uptake and yield. The same spectral features associated with crop height and canopy data were essential for predicting paddy rice aboveground biomass. UAV-multispectral data were able to assess agricultural intensification strategies at field/landscape scale irrespective of soil types, watering regimes, and cultivars. Special consideration should be attributed to VARI, as it enables economical prediction of paddy rice traits. The UAV technologies are therefore reliable tools for monitoring rice production and can be applied in agricultural extension in the DRC.

Název v anglickém jazyce

Paddy rice traits estimation under varying management strategies using UAV technology

Popis výsledku anglicky

Timely crop monitoring and yield prediction are essential in guiding management decision making. The aim of the study was to estimate the agronomic traits of paddy rice (Oryza sativa L.) using unmanned aerial vehicle (UAV)-multispectral imaging. A randomized complete block design field experiment with a split-split plot arrangement was set up in the Ruzizi plain, Democratic Republic of Congo (DRC). Spectral imaging data were collected at rice tillering and panicle initiation stages. Predictive analysis of rice agronomic traits was performed using linear and decision tree-based machine learning techniques. Paddy rice trait predictions were critically sensitive to the timing of image acquisition but not largely affected by the model. The most accurate predictions were made at rice panicle initiation stage, with R2 values of 0.62, 0.65, and 0.75 for yield, aboveground biomass, and plant nitrogen (N) uptake, respectively. The visible atmospherically resistant index (VARI), modified chlorophyll absorption in reflective index, and ratio vegetation index, along with near infrared and green bands, played a critical role in predicting paddy rice N uptake and yield. The same spectral features associated with crop height and canopy data were essential for predicting paddy rice aboveground biomass. UAV-multispectral data were able to assess agricultural intensification strategies at field/landscape scale irrespective of soil types, watering regimes, and cultivars. Special consideration should be attributed to VARI, as it enables economical prediction of paddy rice traits. The UAV technologies are therefore reliable tools for monitoring rice production and can be applied in agricultural extension in the DRC.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40101 - Agriculture

Projekt

—

Návaznosti

O - Projekt operacniho programu

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Agrosystems, Geosciences &amp; Environment

ISSN

2639-6696

e-ISSN

2639-6696

Svazek periodika

8

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

"e70047"

Kód UT WoS článku

001409509800001

EID výsledku v databázi Scopus

2-s2.0-85216643623