[en] 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.
Disciplines :
Agriculture & agronomy
Author, co-author :
Heidarian Dehkordi, Ramin ; Université de Liège - ULiège > DER Sc. et gest. de l'environnement (Arlon Campus Environ.) > Eau, Environnement, Développement
El Jarroudi, Moussa ; Université de Liège - ULiège > DER Sc. et gest. de l'environnement (Arlon Campus Environ.) > Eau, Environnement, Développement
Kouadio, Louis
Meersmans, Jeroen ; Université de Liège - ULiège > Département GxABT > Analyse des risques environnementaux
Beyer, Marco
Language :
English
Title :
Monitoring Wheat Leaf Rust and Stripe Rust in Winter Wheat Using High-Resolution UAV-Based Red-Green-Blue Imagery
Publication date :
11 November 2020
Journal title :
Remote Sensing
eISSN :
2072-4292
Publisher :
Multidisciplinary Digital Publishing Institute (MDPI), Basel, Switzerland
Special issue title :
Remote and Proximal Sensing for Precision Agriculture and Viticulture
(2020) World Food Situation—FAO Cereal Supply and Demand Brief (Release Date: 03/09/2020), , FAO. Food and Agriculture Organization of the United Nations (FAO): Rome, Italy
World Agricultural Production. Circular Series WAP 9–20. Spetember 2020, , USDA. United States Department of Agriculture (USDA) Foreign Agricultural Service, Gobal Market Analysis: Washington, DC, USA, 2020
Kolmer, J.A., Tracking wheat rust on a continental scale (2005) Curr. Opin. Plant Biol, 8, pp. 441-449
Hovmøller, M.S., Walter, S., Justesen, A.F., Escalating threat of wheat rusts (2010) Science, 329, p. 369
El Jarroudi, M., Kouadio, L., Giraud, F., Delfosse, P., Tychon, B., Brown rust disease control in winter wheat: II. Exploring the optimization of fungicide sprays through a decision support system (2014) Environ. Sci. Pollut. Res, 21, pp. 4809-4818
Beddow, J.M., Pardey, P.G., Chai, Y., Hurley, T.M., Kriticos, D.J., Braun, H.-J., Park, R.F., Yonow, T., Research investment implications of shifts in the global geography of wheat stripe rust (2015) Nat. Plants, 1, p. 15132
El Jarroudi, M., Kouadio, L., Beyer, M., Junk, J., Hoffmann, L., Tychon, B., Maraite, H., Delfosse, P., Economics of a decision–support system for managing the main fungal diseases of winter wheat in the Grand-Duchy of Luxembourg (2015) Field Crops Res, 172, pp. 32-41
Ali, S., Rodriguez-Algaba, J., Thach, T., Sørensen, C.K., Hansen, J.G., Lassen, P., Nazari, K., Hovmøller, M.S., Yellow rust epidemics worldwide were caused by pathogen races from divergent genetic lineages (2017) Front. Plant Sci, 8, p. 1057
Huerta-Espino, J., Singh, R.P., German, S., McCallum, B.D., Park, R.F., Chen, W., Bhardwaj, S.C., Goyeau, H., Global status of wheat leaf rust caused by Puccinia triticina (2011) Euphytica, 179, pp. 143-160
Freier, B., Boller, E.F., Integrated Pest Management in Europe—History, Policy, Achievements and Implementation (2009) Integrated Pest Management: Dissemination and Impact, pp. 435-454. , Peshin, R.
Dhawan, A.K., Eds.
Springer: Dordrecht, The Netherlands
Mohanty, S.P., Hughes, D.P., Salathé, M., Using Deep Learning for Image-based plant disease detection (2016) Front. Plant Sci, 7
Bock, C.H., Poole, G.H., Parker, P.E., Gottwald, T.R., Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging (2010) Crit. Rev. Plant Sci, 29, pp. 59-107
El Jarroudi, M., Kouadio, A.L., Mackels, C., Tychon, B., Delfosse, P., Bock, C.H., A comparison between visual estimates and image analysis measurements to determine Septoria leaf blotch severity in winter wheat (2014) Plant Pathol, pp. 355-364
Moshou, D., Bravo, C., West, J., Wahlen, S., McCartney, A., Ramon, H., Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networks (2004) Comp. Electron. Agric, 44, pp. 173-188
Zhang, C., Kovacs, J.M., The application of small unmanned aerial systems for precision agriculture: A review (2012) Precision Agric, 13, pp. 693-712
Bohnenkamp, D., Behmann, J., Mahlein, A.-K., In-field detection of yellow rust in wheat on the ground canopy and UAV scale (2019) Remote Sens, 11, p. 2495
Boulent, J., Foucher, S., Théau, J., St-Charles, P.-L., Convolutional Neural Networks for the automatic identification of plant diseases (2019) Front. Plant Sci, 10
Dang, L.M., Wang, H., Li, Y., Min, K., Kwak, J.T., Lee, O.N., Park, H., Moon, H., Fusarium wilt of radish detection using RGB and near infrared images from Unmanned Aerial Vehicles (2020) Remote Sens, 12, p. 2863
Singh, A., Jones, S., Ganapathysubramanian, B., Sarkar, S., Mueller, D., Sandhu, K., Nagasubramanian, K., Challenges and opportunities in machine-augmented plant stress phenotyping (2020) Trends Plant Sci
Franke, J., Menz, G., Oerke, E.-C., Rascher, U., Comparison of multi-and hyperspectral imaging data of leaf rust infected wheat plants (2005) Remote Sensing for Agriculture, Ecosystems, and Hydrology VII, 5976. , SPIE: Bellingham, WA, USA
Su, J., Liu, C., Coombes, M., Hu, X., Wang, C., Xu, X., Li, Q., Chen, W.-H., Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery (2018) Comp. Electron. Agric, 155, pp. 157-166
Moshou, D., Bravo, C., Wahlen, S., West, J., McCartney, A., De Baerdemaeker, J., Ramon, H., Simultaneous identification of plant stresses and diseases in arable crops using proximal optical sensing and selforganising maps (2006) Precision Agric, 7, pp. 149-164
Huang, W., Lamb, D.W., Niu, Z., Zhang, Y., Liu, L., Wang, J., Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging (2007) Precision Agric, 8, pp. 187-197
Moshou, D., Bravo, C., Oberti, R., West, J., Bodria, L., McCartney, A., Ramon, H., Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps (2005) RealTime Imaging, 11, pp. 75-83
Sankaran, S., Mishra, A., Maja, J.M., Ehsani, R., Visible-near infrared spectroscopy for detection of Huanglongbing in citrus orchards (2011) Comp. Electron. Agric, 77, pp. 127-134
Bravo, C., Moshou, D., West, J., McCartney, A., Ramon, H., Early disease detection in wheat fields using spectral reflectance (2003) Biosyst. Eng, 84, pp. 137-145
Whetton, R.L., Waine, T.W., Mouazen, A.M., Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 2: On-line field measurement (2018) Biosyst. Eng, 167, pp. 144-158
Mahlein, A.K., Rumpf, T., Welke, P., Dehne, H.W., Plümer, L., Steiner, U., Oerke, E.C., Development of spectral indices for detecting and identifying plant diseases (2013) Remote Sens. Environ, 128, pp. 21-30
Das, P.K., Laxman, B., Rao, S.V.C.K., Seshasai, M.V.R., Dadhwal, V.K., Monitoring of bacterial leaf blight in rice using ground-based hyperspectral and LISS IV satellite data in Kurnool, Andhra Pradesh, India (2015) Int. J. Pest Manag, 61, pp. 359-368
Heidarian Dehkordi, R., Burgeon, V., Fouche, J., Placencia Gomez, E., Cornelis, J.-T., Nguyen, F., Denis, A., Meersmans, J., Using UAV collected RGB and multispectral images to evaluate winter wheat performance across a site characterized by century-old biochar patches in Belgium (2020) Remote Sens, 12, p. 2504
Heidarian Dehkordi, R., Denis, A., Fouche, J., Burgeon, V., Cornelis, J.T., Tychon, B., Placencia Gomez, E., Meersmans, J., Remotely-sensed assessment of the impact of century-old biochar on chicory crop growth using high-resolution UAV-based imagery (2020) Int. J. Appl. Earth. Obs. Geoinf, 91, p. 102147
Dam, D., Pallez-Barthel, M., El Jarroudi, M., Eickermann, M., Beyer, M., The debate on a loss of biodiversity: Can we derive evidence from the monitoring of major plant pests and diseases in major crops? (2020) J. Plant Dis. Prot
James, C.A., An illustrated series of assessment keys for plant diseases, their preparation and usage (1971) Can. Plant Dis. Surv, 51, pp. 39-65
Tomerlin, J.R., Howell, A., DISTRAIN: A computer program for training people to estimate disease severity on cereal leaves (1988) Plant Dis, 72, pp. 455-459
Deutscher Landwirtschaftsverlag GmbH.: Hannover, Germany
Hou, Y.-C., Visual cryptography for color images (2003) Pattern Recognit, 36, pp. 1619-1629
Liu, M., Yu, T., Gu, X., Sun, Z., Yang, J., Zhang, Z., Mi, X., Li, J., The impact of spatial resolution on the classification of vegetation types in highly fragmented planting areas based on unmanned aerial vehicle hyperspectral images (2020) Remote Sens, 12, p. 146
Lovell, D.J., Parker, S.R., Hunter, T., Royle, D.J., Coker, R.R., Influence of crop growth and structure on the risk of epidemics by Mycosphaerella graminicola (Septoria tritici) in winter wheat (1997) Plant Pathol, 46, pp. 126-138
Lovell, D.J., Parker, S.R., Hunter, T., Welham, S.J., Nichols, A.R., Position of inoculum in the canopy affects the risk of septoria tritici blotch epidemics in winter wheat (2004) Plant Pathol, 53, pp. 11-21
Kendall, M.G., Partial rank correlation (1942) Biometrika, 32, pp. 277-283
R: A Language and Environment for Statistical Computing, , R Core Team. R Foundation for Statistical Computing, Vienna, Austria
El Jarroudi, M., Kouadio, L., Bertrand, M., Curnel, Y., Giraud, F., Delfosse, P., Hoffmann, L., Tychon, B., Integrating the impact of wheat fungal diseases in the Belgian crop yield forecasting system (BCYFS) (2012) Eur. J. Agron, 40, pp. 8-17
El Jarroudi, M., Kouadio, L., Bock, C.H., El Jarroudi, M., Junk, J., Pasquali, M., Maraite, H., Delfosse, P., A threshold-based weather model for predicting stripe rust infection in winter wheat (2017) Plant Dis, 101, pp. 693-703
Junk, J., Kouadio, L., Delfosse, P., El Jarroudi, M., Effects of regional climate change on brown rust disease in winter wheat (2016) Clim. Chang, 135, pp. 439-451
El Jarroudi, M., Kouadio, L., Delfosse, P., Tychon, B., Brown rust disease control in winter wheat: I. Exploring an approach for disease progression based on night weather conditions (2014) Environ. Sci. Pollut. Res, 21, pp. 4797-4808
Rodríguez-Moreno, V.M., Jiménez-Lagunes, A., Estrada-Avalos, J., Mauricio-Ruvalcaba, J.E., Padilla Ramírez, J.S., Weather-data-based model: An approach for forecasting leaf and stripe rust on winter wheat (2020) Meteorol. Appl, 27, p. e1896
Gooding, M.J., Dimmock, J.P.R.E., France, J., Jones, S.A., Green leaf area decline of wheat flag leaves: The influence of fungicides and relationships with mean grain weight and grain yield (2000) Ann. Appl. Biol, 136, pp. 77-84
Adams, M.L., Philpot, W.D., Norvell, W.A., Yellowness index: An application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation (1999) Int. J. Remote Sens, 20, pp. 3663-3675