Article (Scientific journals)
In-field proximal sensing of septoria tritici blotch, stripe rust and brown rust in winter wheat by means of reflectance and textural features from multispectral imagery
Bebronne, Romain; Carlier, Alexis; Meurs, Rémy et al.
2020In Biosystems Engineering, 197, p. 257-269
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Keywords :
Artificial neural networks; Fungal diseases; Multispectral; Partial least squares regression; Proximal sensing; Winter wheat; Crops; Infrared devices; Least squares approximations; Remote sensing; Acquisition systems; Multi-spectral cameras; Multi-spectral imagery; Partial least squares regressions (PLSR); Septoria tritici blotch; Supervised classification; Visible and near infrared; Winter wheat (Triticum aestivum L.); Plants (botany)
Abstract :
[en] During its growth, winter wheat (Triticum aestivum L.) can be impacted by multiple stresses involving fungal diseases that are responsible for high yield losses. Enhancing the breeding and the identification of resistant cultivars could be achieved by collecting automated and reliable information at the plant level. This study aims to estimate the severity of stripe rust (SR), brown rust (BR) and septoria tritici blotch (STB) in natural conditions and to highlight wavebands of interest, based on images acquired through a multispectral camera embedded on a ground-based platform. The severity of the three diseases has been assessed visually in an agronomic trial involving five wheat cultivars with or without fungicide treatment. An acquisition system using multispectral imagery covering the visible and near-infrared range has been set up at the canopy level. Based on spectral and textural features, estimations of area under disease progress curve (AUDPC) were performed by means of artificial neural networks (ANN) and partial least squares regression (PLSR). Supervised classification was also implemented by means of ANN. The ANN performed better at estimating disease severity with R2 of 0.72, 0.57 and 0.65 for STB, SR and BR respectively. Discrimination in two classes below or above 100 AUDPC reached an accuracy of 81% (κ = 0.60) for STB. This study, which combined the effect of date, cultivar and multiple disease infections, managed to highlight a few wavebands for each disease and took a step further in the development of a machine vision-based approach for the characterisation of fungal diseases in natural conditions. © 2020
Disciplines :
Agriculture & agronomy
Author, co-author :
Bebronne, Romain ;  Biosystems Dynamics and Exchanges, TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, Passage des Déportés, 2, Gembloux, 5030, Belgium
Carlier, Alexis  ;  Université de Liège - ULiège > Département GxABT > Biosystems Dynamics and Exchanges
Meurs, Rémy ;  Plant Sciences, TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, Passage des Déportés, 2, Gembloux, 5030, Belgium
Leemans, Vincent ;  Université de Liège - ULiège > Département GxABT > Biosystems Dynamics and Exchanges
Vermeulen, Philippe;  Walloon Agricultural Research Centre, Knowledge and Valorization of Agricultural Products Department, Quality and Authentication of Agricultural Products, Chaussée de Namur, 24, Gembloux, 5030, Belgium
Dumont, Benjamin  ;  Université de Liège - ULiège > Département GxABT > Phytotechnie tempérée
Mercatoris, Benoît  ;  Université de Liège - ULiège > Département GxABT > Biosystems Dynamics and Exchanges
Language :
English
Title :
In-field proximal sensing of septoria tritici blotch, stripe rust and brown rust in winter wheat by means of reflectance and textural features from multispectral imagery
Publication date :
2020
Journal title :
Biosystems Engineering
ISSN :
1537-5110
eISSN :
1537-5129
Publisher :
Academic Press
Volume :
197
Pages :
257-269
Peer reviewed :
Peer Reviewed verified by ORBi
Name of the research project :
PHENWHEAT D31-1385
Funders :
DGA - Région wallonne. Direction générale de l'Agriculture [BE]
Available on ORBi :
since 07 September 2020

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