Characterisation of fungal diseases on winter wheat crop using proximal and remote multispectral imaging

Bebronne, Romain; Michez, Adrien; Leemans, Vincent; Dumont, Benjamin; Vermeulen, Philippe; Mercatoris, Benoît

doi:10.3920/978-90-8686-888-9_31

Request a copy

Paper published in a book (Scientific congresses and symposiums)

Characterisation of fungal diseases on winter wheat crop using proximal and remote multispectral imaging

Bebronne, Romain; Michez, Adrien; Leemans, Vincent et al.

2019 • In Precision agriculture ’19

Peer reviewed

Permalink
https://hdl.handle.net/2268/239119

DOI
10.3920/978-90-8686-888-9_31

Files (1)Send to Details Statistics Bibliography Similar publications

Files

Full Text

ECPA - Révisé - Sans modif.docx

Author preprint (701.34 kB)

Request a copy

This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned. Nothing from this publication may be translated, reproduced, stored in a computerised system or published in any form or in any manner, including electronic, mechanical, reprographic or photographic, without prior written permission from the publisher: Wageningen Academic Publishers P.O. Box 220 6700 AE Wageningen The Netherlands www.WageningenAcademic.com copyright@ WageningenAcademic.com. The individual contributions in this publication and any liabilities arising from them remain the responsibility of the authors.The publisher is not responsible for possible damages, which could be a result of content derived from this publication.

All documents in ORBi are protected by a user license.

Send to

RIS BibTex APA Chicago Permalink X Linkedin

Details

Keywords :

Wheat; Fungal diseases; unmanned aerial vehicle sensing; multispectral; proximal sensing

Abstract :

[en] Winter wheat fungal diseases, responsible for high yield losses, can be assessed by computer vision to increase phenotyping performance. This study aims to compare multispectral imagery based on remote and proximal sensing for disease detection. Wavelength selection was achieved by ANOVA and stepwise regression. Prediction of disease severity was performed by means of an artificial neural network based on proximal sensing data. If septoria requires proximal measurements, stripe and brown rusts can be detected from UAVs and early detection from the ground. Prediction results obtained gave R² of 0.55 and 0.57 for septoria tritici blotch and stripe rust respectively.

Research Center/Unit :

Terra Research and Teaching Centre

Disciplines :

Agriculture & agronomy

Author, co-author :

Bebronne, Romain ; Université de Liège - ULiège > Ingénierie des biosystèmes (Biose) > Biosystems Dynamics and Exchanges

Michez, Adrien ; Université de Liège - ULiège > Ingénierie des biosystèmes (Biose) > Gestion des ressources forestières et des milieux naturels

Leemans, Vincent ; Université de Liège - ULiège > Ingénierie des biosystèmes (Biose) > Biosystems Dynamics and Exchanges

Dumont, Benjamin ; Université de Liège - ULiège > Agronomie, Bio-ingénierie et Chimie (AgroBioChem) > Ingénierie des productions végétales et valorisation

Vermeulen, Philippe; Centre wallon de recherches agronomiques > Valorisation des produits agricoles > Qualité des produits

Mercatoris, Benoît ; Université de Liège - ULiège > Ingénierie des biosystèmes (Biose) > Biosystems Dynamics and Exchanges

Language :

English

Title :

Characterisation of fungal diseases on winter wheat crop using proximal and remote multispectral imaging

Publication date :

08 July 2019

Event name :

The 12th European Conference on Precision Agriculture

Event organizer :

Montpellier SupAgro

Event place :

Montpellier, France

Event date :

du 8 juillet 2019 au 11 juillet 2019

Audience :

International

Main work title :

Precision agriculture ’19

Publisher :

Wageningen Academic Publishers, Wageningen, Netherlands

ISBN/EAN :

978-90-8686-337-2

Pages :

255-261

Peer reviewed :

Peer reviewed

Additional URL :

https://www.wageningenacademic.com/doi/abs/10.3920/978-90-8686-888-9_31

Name of the research project :

Project D31-1385

Funders :

SPW DG03-DGARNE - Service Public de Wallonie. Direction Générale Opérationnelle Agriculture, Ressources naturelles et Environnement

Available on ORBi :

since 03 September 2019

Statistics

Number of views

119 (20 by ULiège)

Number of downloads

3 (2 by ULiège)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenCitations

OpenAlex citations

Bibliography

Ashourloo, D., Mobasheri, M. R., & Huete, A., 2014. Developing two spectral disease indices for detection of wheat leaf rust (Puccinia triticina). Remote Sensing 6 4723-4740.
Bahat, A., Gelernter, I., Brown, M. B. & Eyal, Z., 1980. Factors affecting the vertical progression of Septoria leaf blotch in short-statured wheats. Phytopathology 70 (3) 179-184.
Baraldi, A. & Parmiggiani, F., 1995. An investigation of the textural characteristics associated with Gray Level Co-occurrence Matrix statistical parameters. IEEE Transactions on Geoscience and Remote Sensing 33 (2) 293-304.
Bodson B., De Proft M. & Watillon B., 2017. Livre Blanc Céréales. Edition février 2017 (White paper cereals. February edition 2017).
Bravo, C., Moshou, D., West, J., McCartney, A., & Ramon, H., 2003. Early disease detection in wheat fields using spectral reflectance. Biosystems Engineering. 84 (2), 137-145.
Devadas, R., Lamb, D. W., Simpfendorfer, S., & Backhouse, D., 2009. Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves. Precision Agriculture 10 459-470.
Fiorani, F., & Schurr, U., 2013. Future Scenarios for Plant Phenotyping. Annual Review of Plant Biology 64 267-291.
Hornik K., Stinchcombe M. & White H., 1989. Multilayer feedforward networks are universal approximators. Neural networks 2 (5) 359-366.
Huang, W., Lamb, D. W., Niu, Z., Zhang, Y., Liu, L., & Wang, J., 2007. Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precision Agriculture 8 (4-5) 187-197.
Krishna, G., Sahoo, R. N., Pargal, S., Gupta, V. K., Sinha, P., Bhagat, S. et al., 2014. Assessing wheat yellow rust disease through hyperspectral remote sensing. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Hyderabad, India, 40 1413-1416
Kuska, M., Wahabzada, M., Leucker, M., Dehne, H. W., Kersting, K., Oerke, E. C. et al., 2015. Hyperspectral phenotyping on the microscopic scale: Towards automated characterization of plant-pathogen interactions. Plant Methods 11 (1) 1-14.
Li, L., Zhang, Q., & Huang, D., 2014. A review of imaging techniques for plant phenotyping. Sensors 14 (11) 20078-20111.
Moshou, D., Bravo, C., West, J., Wahlen, S., McCartney, A., & Ramon, H., 2004. Automatic detection of 'yellow rust' in wheat using reflectance measurements and neural networks. Computers and Electronics in Agriculture 44 (3) 173-188.
Nebiker, S., Lack, N., Abächerli, M., & Läderach, S., 2016. Light-weight multispectral uav sensors and their capabilities for predicting grain yield and detecting plant diseases. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Pragues, Czech Republic, 41 963-970.
Odilbekov, F., Armoniené, R., Henriksson, T., & Chawade, A., 2018. Proximal Phenotyping and Machine Learning Methods to Identify Septoria Tritici Blotch Disease Symptoms in Wheat. Frontiers in Plant Science 91-11.
Peterson, R. F., Campbell, A. B., & Hannah, A. E., 1948. A diagrammatic scale for estimating rust intensity on leaves and stems of cereals. Canadian Journal of Research 26 (5) 496-500.
Service Public de Wallonie, 2018. Programme wallon de réduction des pesticides 2018-2022 (Walloon program of pesticides reduction 2018-2022). http://diantonio.wallonie.be/files/PWRP_II.pdf. (last accessed August 2018)
Yu, K., Anderegg, J., Mikaberidze, A., Karisto, P., Mascher, F., McDonald, B. A. et al., 2018. Hyperspectral Canopy Sensing of Wheat Septoria Tritici Blotch Disease. Frontiers in Plant Science 9 1-17.
Zadoks, J. C., Chang, T. T., Konzak, C. F., & Fryer, J. D., 1974. A decimal code for the growth stages of cereals. Weed Research 14 415-421.