Detecting Xylella fastidiosa in a machine learning framework using Vcmax and the leaf biochemistry quantified with airborne hyperspectral imagery

The bacterium Xylella fastidiosa (Xf) is considered one of the plant pathogens of greatest global concern threatening key agricultural crops worldwide. This paper explores the ability of parsimonious machine learning (ML) algorithms to detect Xf-infected trees operationally, when considering a proxy of photosynthetic capacity, namely the maximum carboxylation rate (Vcmax), along with carbon-based constituents (CBC, including lignin), and leaf biochemical traits and tree-crown temperature (Tc) as an indicator of transpiration rates. The ML framework proposed here reduced the uncertainties associated with the extraction of reflectance spectra and temperature from individual tree crowns using high-resolution hyperspectral and thermal images. We showed that the relative importance of Vcmax and leaf biochemical constituents (e.g., CBC) in the ML model for detection of Xf at early stages of development were intrinsically associated with the water and nutritional conditions of almond trees. Overall, the functional traits that were most consistently altered by Xf-infection were Vcmax, pigments, CBC and Tc, and, particularly in rainfed-trees, anthocyanins, and Tc. The parsimonious ML model for Xf detection yielded accuracies exceeding 90% (kappa = 0.80). This study brings progress in the development of an operational ML framework for the detection of Xf outbreaks based on plant traits related to photosynthetic capacity, plant biochemistry and structural decay parameters.

CAMINO GONZALEZ Carlos;  ARANO Keith;  BERNI Jose Antonio;  DIERKES Henrike;  TRAPERO-CASAS Jose Luis;  LEON-ROPERO Guillermo;  MONTES-BORREGO Miguel;  ROMAN Miguel;  VELASCO-AMO M. P.;  LANDA Blanca;  NAVAS-CORTES Juan A.;  BECK Pieter; 

2022-10-11

ELSEVIER SCIENCE INC

JRC128559

0034-4257 (online),   

https://www.sciencedirect.com/science/article/pii/S003442572200387X,    https://publications.jrc.ec.europa.eu/repository/handle/JRC128559,   

10.1016/j.rse.2022.113281 (online),