UAV RGB Imagery as an Early-Warning Tool of Wheat Rust Pathogen-Induced Physiological Changes [* Cross-Reference *]

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Moussa El Jarroudi; Louis Kouadio; Jonathan Peereman; Marco Beyer, 2026, "UAV RGB Imagery as an Early-Warning Tool of Wheat Rust Pathogen-Induced Physiological Changes [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.IKZKFZ, LIST Open Repository, V1

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Description	Remote sensing of crop diseases has traditionally focused on detecting visible symptoms, often limiting intervention to advanced stages of epidemic development. This study investigates whether high-resolution unmanned aerial vehicles (UAV)-based red–green–blue (RGB) imagery can reveal earlier physiological destabilization preceding visible symptoms of wheat stripe rust and wheat leaf rust. UAV imagery was acquired at four winter wheat-growing sites in Luxembourg during the 2018/2019 season. Temporal dynamics of green–red spectral slopes were analyzed and compared with ground-based disease severity observations to identify potential pre-symptomatic spectral signals. A consistent flattening of the green–red spectral slope was detected prior to a rapid increase in visually assessed severity for both diseases. However, the length of this pre-symptomatic window varied between the two diseases: it lasted 7 to 14 days for wheat stripe rust and 5 to 10 days for wheat leaf rust. Likewise, the reduction in spectral slope magnitude was slightly greater for wheat stripe rust (65–80%) than for wheat leaf rust (60–75%), indicating that the temporal lead time and intensity of the spectral response were disease-dependent. During the pre-symptomatic phase, the spectral dynamics reflected latent physiological changes rather than visible disease severity. Strong correlations emerged only after the epidemic transition. These findings demonstrate that UAV-based RGB imagery could capture a distinct pre-symptomatic phase of stripe rust and leaf rust epidemics in winter wheat. Interpreting RGB spectral dynamics as early-warning indicators rather than merely as static severity proxies can guide proactive disease monitoring and precision agriculture. (2026-06-01) *This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.3390/rs18111769 for the original and latest version of the publication* (2026-07-01)
Subject	Agricultural Sciences; Arts and Humanities; Earth and Environmental Sciences; Medicine, Health and Life Sciences; Physics
Keyword	Stripe rust, RGB color model, Rust (programming language), Limiting, Winter wheat, Spectral bands, Flattening
License/Data Use Agreement	CC BY 4.0


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Persistent Identifier	perma:LIST.IKZKFZ
Publication Date	2026-07-06
Title	UAV RGB Imagery as an Early-Warning Tool of Wheat Rust Pathogen-Induced Physiological Changes [* Cross-Reference *]
Other Identifier	https://doi.org/10.3390/rs18111769 OpenAlex ID: https://openalex.org/W7163010969
Author	Moussa El Jarroudi (University of Liège) Louis Kouadio (University of Southern Queensland, Africa Rice Center) - ORCID: https://orcid.org/0000-0001-9669-7807 Jonathan Peereman (University of Liège) - ORCID: https://orcid.org/0000-0002-5128-3536 Marco Beyer (Luxembourg Institute of Science and Technology) - ORCID: https://orcid.org/0000-0002-9415-4718
Point of Contact	Use email button above to contact. LIST QDKM (LIST)
Description	Remote sensing of crop diseases has traditionally focused on detecting visible symptoms, often limiting intervention to advanced stages of epidemic development. This study investigates whether high-resolution unmanned aerial vehicles (UAV)-based red–green–blue (RGB) imagery can reveal earlier physiological destabilization preceding visible symptoms of wheat stripe rust and wheat leaf rust. UAV imagery was acquired at four winter wheat-growing sites in Luxembourg during the 2018/2019 season. Temporal dynamics of green–red spectral slopes were analyzed and compared with ground-based disease severity observations to identify potential pre-symptomatic spectral signals. A consistent flattening of the green–red spectral slope was detected prior to a rapid increase in visually assessed severity for both diseases. However, the length of this pre-symptomatic window varied between the two diseases: it lasted 7 to 14 days for wheat stripe rust and 5 to 10 days for wheat leaf rust. Likewise, the reduction in spectral slope magnitude was slightly greater for wheat stripe rust (65–80%) than for wheat leaf rust (60–75%), indicating that the temporal lead time and intensity of the spectral response were disease-dependent. During the pre-symptomatic phase, the spectral dynamics reflected latent physiological changes rather than visible disease severity. Strong correlations emerged only after the epidemic transition. These findings demonstrate that UAV-based RGB imagery could capture a distinct pre-symptomatic phase of stripe rust and leaf rust epidemics in winter wheat. Interpreting RGB spectral dynamics as early-warning indicators rather than merely as static severity proxies can guide proactive disease monitoring and precision agriculture. (2026-06-01) *This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.3390/rs18111769 for the original and latest version of the publication* (2026-07-01)
Subject	Agricultural Sciences; Arts and Humanities; Earth and Environmental Sciences; Medicine, Health and Life Sciences; Physics
Keyword	Stripe rust RGB color model Rust (programming language) Limiting Winter wheat Spectral bands Flattening
Topic Classification	Remote Sensing in Agriculture Wheat and Barley Genetics and Pathology Smart Agriculture and AI
Deposit Date	2026-06-01
Data Type	Article
Data Source	Remote Sensing

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