Monitoring wheat leaf rust and stripe rust in winter wheat using high-resolution UAV-based red-green-blue imagery

Article


Heidarian Dehkordi, Ramin, El Jarroudi, Moussa, Kouadio, Louis, Meersmans, Jeroen and Beyer, Marco. 2020. "Monitoring wheat leaf rust and stripe rust in winter wheat using high-resolution UAV-based red-green-blue imagery." Remote Sensing. 12 (22). https://doi.org/10.3390/rs12223696
Article Title

Monitoring wheat leaf rust and stripe rust in winter wheat using high-resolution UAV-based red-green-blue imagery

ERA Journal ID201448
Article CategoryArticle
AuthorsHeidarian Dehkordi, Ramin (Author), El Jarroudi, Moussa (Author), Kouadio, Louis (Author), Meersmans, Jeroen (Author) and Beyer, Marco (Author)
Journal TitleRemote Sensing
Journal Citation12 (22)
Article Number3696
Number of Pages18
Year2020
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN2072-4292
Digital Object Identifier (DOI)https://doi.org/10.3390/rs12223696
Web Address (URL)https://www.mdpi.com/2072-4292/12/22/3696
Abstract

During the past decade, imagery data acquired from unmanned aerial vehicles (UAVs), thanks to their high spatial, spectral, and temporal resolutions, have attracted increasing attention for discriminating healthy from diseased plants and monitoring the progress of such plant diseases in fields. Despite the well-documented usage of UAV-based hyperspectral remote sensing for discriminating healthy and diseased plant areas, employing red-green-blue (RGB) imagery for a similar purpose has yet to be fully investigated. This study aims at evaluating UAV-based RGB imagery to discriminate healthy plants from those infected by stripe and wheat leaf rusts in winter wheat (Triticum aestivum L.), with a focus on implementing an expert system to assist growers in improved disease management. RGB images were acquired at four representative wheat-producing sites in the Grand Duchy of Luxembourg. Diseased leaf areas were determined based on the digital numbers (DNs) of green and red spectral bands for wheat stripe rust (WSR), and the combination of DNs of green, red, and blue spectral bands for wheat leaf rust (WLR). WSR and WLR caused alterations in the typical reflectance spectra of wheat plants between the green and red spectral channels. Overall, good agreements between UAV-based estimates and observations were found for canopy cover, WSR, and WLR severities, with statistically significant correlations (p-value (Kendall) < 0.0001). Correlation coefficients were 0.92, 0.96, and 0.86 for WSR severity, WLR severity, and canopy cover, respectively. While the estimation of canopy cover was most often less accurate (correlation coefficients < 0.20), WSR and WLR infected leaf areas were identified satisfactorily using the RGB imagery-derived indices during the critical period (i.e., stem elongation and booting stages) for efficacious fungicide application, while disease severities were also quantified accurately over the same period. Using such a UAV-based RGB imagery method for monitoring fungal foliar diseases throughout the cropping season can help to identify any new disease outbreak and efficaciously control its spread.

Keywordsfungal foliar disease; RGB imagery; precision agriculture; disease management
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020310805. Plant pathology
401304. Photogrammetry and remote sensing
300409. Crop and pasture protection (incl. pests, diseases and weeds)
Byline AffiliationsUniversity of Liege, Belgium
Centre for Applied Climate Sciences
Luxembourg Institute of Science and Technology, Luxembourg
Institution of OriginUniversity of Southern Queensland
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Newlands, Nathaniel K., Zamar, David S., Kouadio, Louis A., Zhang, Yinsuo, Chipanshi, Aston, Potgieter, Andries, Toure, Souleymane and Hill, Harvey S. J.. 2014. "An integrated, probabilistic model for improved seasonal forecasting of agricultural crop yield under environmental uncertainty." Frontiers in Environmental Science. 2, pp. 1-21. https://doi.org/10.3389/fenvs.2014.00017
A comparison of raters and disease assessment methods for estimating disease severity for purposes of hypothesis testing
Bock, Clive, El Jarroudi, Moussa, Kouadio, Louis, Mackels, Christophe, Chiang, Kuo-Szu and Delfosse, Philippe. 2014. "A comparison of raters and disease assessment methods for estimating disease severity for purposes of hypothesis testing." Phytopathology: International Journal of the American Phytopathological Society. 104 (11), pp. 15-16.
Importance of a well-distributed frequency of measurements in the senescence monitoring of winter wheat and yield estimates
Kouadio, Louis, Djaby, Bakary, El Jarroudi, Moussa and Tychon, Bernard. 2012. "Importance of a well-distributed frequency of measurements in the senescence monitoring of winter wheat and yield estimates." Journal of Agricultural Science and Technology . 2 (12), pp. 1203-1213.
Estimating winter wheat yield through the decreasing phase of its green area [Cinetique de decroissance de la surface verte et estimation du rendement du ble d'hiver]
Kouadio, Amani Louis, Djaby, Bakary, Duveiller, Gregory, El Jarroudi, Moussa and Tychon, Bernard. 2012. "Estimating winter wheat yield through the decreasing phase of its green area [Cinetique de decroissance de la surface verte et estimation du rendement du ble d'hiver]." Biotechnology, Agronomy, Society and Environment. 16 (2), pp. 179-191.
Typology of the main fungal diseases affecting winter wheat in the Grand Duchy of Luxembourg
El Jarroudi, Moussa, Kouadio, Louis, Delfosse, Philippe, Giraud, Frederic, Junk, Jurgen, Hoffmann, Lucien, Maraite, Henri and Tychon, Bernard. 2012. "Typology of the main fungal diseases affecting winter wheat in the Grand Duchy of Luxembourg." Journal of Agricultural Science and Technology . 2 (12), pp. 1386-1399.
Spatial distribution of calibrated WOFOST parameters and their influence on the performances of a regional yield forecasting system
Djaby, Bakary, de Wit, Allard, Kouadio, Louis, El Jarroudi, Moussa and Tychon, Bernard. 2013. "Spatial distribution of calibrated WOFOST parameters and their influence on the performances of a regional yield forecasting system." Sustainable Agriculture Research. 2 (4), pp. 12-29. https://doi.org/10.5539/sar.v2n4p12
Disease severity assessment in epidemiological studies: accuracy and reliability of visual estimates of Septoria leaf blotch (SLB) in winter wheat
El Jarroudi, Moussa, Kouadio, Louis, Mackels, Christophe, Tychon, Bernard, Delfosse, Philippe and Bock, Clive H.. 2014. "Disease severity assessment in epidemiological studies: accuracy and reliability of visual estimates of Septoria leaf blotch (SLB) in winter wheat." 2014 APS-CPS Joint Meeting. Minneapolis, United States of America 09 - 13 Aug 2014 United States. American Phytopathological Society. https://doi.org/10.1094/PHYTO-104-11-S3.1