Skip to main content

Research Repository

Advanced Search

Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant traits

Camino, C; Calderón, R; Parnell, SR; Dierkes, H; Chemin, Y; Román-Écija, M; Montes-Borrego, M; Landa, BB; Navas-Cortes, JA; Zarco-Tejada, PJ; Beck, PSA

Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant traits Thumbnail


Authors

C Camino

R Calderón

SR Parnell

H Dierkes

Y Chemin

M Román-Écija

M Montes-Borrego

BB Landa

JA Navas-Cortes

PJ Zarco-Tejada

PSA Beck



Abstract

The early detection of Xylella fastidiosa (Xf) infections is critical to the management of this dangerous plan pathogen across the world. Recent studies with remote sensing (RS) sensors at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, further work is needed to integrate remote sensing in the management of plant disease epidemics. Here, we research how the spectral changes picked up by different sets of RS plant traits (i.e., pigments, structural or leaf protein content), can help capture the spatial dynamics of Xf spread. We coupled a spatial spread model with the probability of Xf-infection predicted by a RS-driven support vector machine (RS-SVM) model. Furthermore, we analyzed which RS plant traits contribute most to the output of the prediction models. For that, in almond orchards affected by Xf (n = 1426 trees), we conducted a field campaign simultaneously with an airborne campaign to collect high-resolution thermal images and hyperspectral images in the visible-near-infrared (VNIR, 400–850 nm) and short-wave infrared regions (SWIR, 950–1700 nm). The best performing RS-SVM model (OA = 75%; kappa = 0.50) included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator (Tc), alongside pigments and structural parameters. Leaf protein content together with NIs contributed 28% to the explanatory power of the model, followed by chlorophyll (22%), structural parameters (LAI and LIDFa), and chlorophyll indicators of photosynthetic efficiency. Coupling the RS model with an epidemic spread model increased the accuracy (OA = 80%; kappa = 0.48). In the almond trees where the presence of Xf was assayed by qPCR (n = 318 trees), the combined RS-spread model yielded an OA of 71% and kappa = 0.33, which is higher than the RS-only model and visual inspections (both OA = 64–65% and kappa = 0.26–31). Our work demonstrates how combining spatial epidemiological models and remote sensing can lead to highly accurate predictions of plant disease spatial distribution.

Citation

Camino, C., Calderón, R., Parnell, S., Dierkes, H., Chemin, Y., Román-Écija, M., …Beck, P. (2021). Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant traits. Remote Sensing of Environment, 260, 112420. https://doi.org/10.1016/j.rse.2021.112420

Journal Article Type Article
Acceptance Date Mar 29, 2021
Online Publication Date Apr 28, 2021
Publication Date Jul 1, 2021
Deposit Date May 4, 2021
Publicly Available Date May 4, 2021
Journal Remote Sensing of Environment
Print ISSN 0034-4257
Publisher Elsevier
Volume 260
Pages 112420
DOI https://doi.org/10.1016/j.rse.2021.112420
Publisher URL https://doi.org/10.1016/j.rse.2021.112420
Related Public URLs https://www.journals.elsevier.com/remote-sensing-of-environment
Additional Information Additional Information : ** Article version: VoR ** From Elsevier via Jisc Publications Router ** Licence for VoR version of this article starting on 31-03-2021: http://creativecommons.org/licenses/by/4.0/ **Journal IDs: issn 00344257 **History: issue date 31-07-2021; published_online 28-04-2021; accepted 29-03-2021
Funders : European Union;Horizon 2020;Alfonso Martin Escudero Foundation
Projects : unspecified;635646;727987
Grant Number: 635646
Grant Number: 727987

Files





Downloadable Citations