An insight into remote sensing solutions for monitoring the Xylella fastidiosa bacterium that is threatening olive trees: a literature review
Journal
Proceedings of SPIE - The International Society for Optical Engineering
Date Issued
September 13, 2024
Abstract
Olive (Olea europaea L.) is a traditional crop of great socio-economic importance for Mediterranean countries,
covering approximately 8,6 million hectares and providing over 90% of the world’s production of olive oil.
However, emerging plant pathogens threaten olive and olive oil production in the Mediterranean. Recently, olive
quick decline syndrome (OQDS), an insect-borne disease caused by the bacterial pathogen Xylella fastidiosa (Xf),
has led to the death of millions of olive trees in Italy, endangering global olive oil production. Xf colonizes the
xylem vessels of the host tree being transmitted by sap feeding insects, mainly Philaenus spumarius (Hemiptera:
Aphrophoridae). Infected trees develop symptoms that resemble symptoms from water stress due to plant vessel
blockage, resulting to leaf scorching, twig, and branch dieback, and leading to tree death within a few years. To
safeguard productivity and profitability of crop production, early disease detection is imperative. Remote Sensing
(RS) technology offers a promising solution to challenges posed by labor-intensive, error-prone conventional field
monitoring methods of plant diseases, offering insights regarding their timely spatial and temporal spread, as well
their impact at early-infection stages. RS platforms, such as airborne (e.g. UAVs) and spaceborne (satellite
sensors) have been utilized to monitor Xf incidence and severity. Machine-learning techniques are applied to
multispectral and hyperspectral data aiming to identify affected orchards by the implicated causal agents, while
specific band combinations and indices e.g. NDVI, ARVI, OSAVI have been found promising for OQDS
monitoring. Summarizing, the present review examines the use of RS in Xf monitoring over the past 20 years,
evaluates the effectiveness of various RS methods, identifies their benefits and limitations, and discusses future
trends to enhance detection efficiency, to support effective management decisions.
covering approximately 8,6 million hectares and providing over 90% of the world’s production of olive oil.
However, emerging plant pathogens threaten olive and olive oil production in the Mediterranean. Recently, olive
quick decline syndrome (OQDS), an insect-borne disease caused by the bacterial pathogen Xylella fastidiosa (Xf),
has led to the death of millions of olive trees in Italy, endangering global olive oil production. Xf colonizes the
xylem vessels of the host tree being transmitted by sap feeding insects, mainly Philaenus spumarius (Hemiptera:
Aphrophoridae). Infected trees develop symptoms that resemble symptoms from water stress due to plant vessel
blockage, resulting to leaf scorching, twig, and branch dieback, and leading to tree death within a few years. To
safeguard productivity and profitability of crop production, early disease detection is imperative. Remote Sensing
(RS) technology offers a promising solution to challenges posed by labor-intensive, error-prone conventional field
monitoring methods of plant diseases, offering insights regarding their timely spatial and temporal spread, as well
their impact at early-infection stages. RS platforms, such as airborne (e.g. UAVs) and spaceborne (satellite
sensors) have been utilized to monitor Xf incidence and severity. Machine-learning techniques are applied to
multispectral and hyperspectral data aiming to identify affected orchards by the implicated causal agents, while
specific band combinations and indices e.g. NDVI, ARVI, OSAVI have been found promising for OQDS
monitoring. Summarizing, the present review examines the use of RS in Xf monitoring over the past 20 years,
evaluates the effectiveness of various RS methods, identifies their benefits and limitations, and discusses future
trends to enhance detection efficiency, to support effective management decisions.
Funding
The authors acknowledge the ‘EXCELSIOR’: ERATOSTHENES: Excellence Research Centre for Earth
Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project
(www.excelsior2020.eu). The ‘EXCELSIOR’ project has received funding from the European Union’s Horizon
2020 research and innovation programme under Grant Agreement No 857510, from the Government of the
Republic of Cyprus through the Directorate General for the European Programmes, Coordination and
Development and the Cyprus University of Technology.
The present work was carried out in the framework of CERBERUS project that has received funding from the
Horizon Europe HORIZON-CL6-2023-GOVERNANCE-01-16- HORIZON Research and Innovation Actions,
under Grant Agreement No 101134878.
Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project
(www.excelsior2020.eu). The ‘EXCELSIOR’ project has received funding from the European Union’s Horizon
2020 research and innovation programme under Grant Agreement No 857510, from the Government of the
Republic of Cyprus through the Directorate General for the European Programmes, Coordination and
Development and the Cyprus University of Technology.
The present work was carried out in the framework of CERBERUS project that has received funding from the
Horizon Europe HORIZON-CL6-2023-GOVERNANCE-01-16- HORIZON Research and Innovation Actions,
under Grant Agreement No 101134878.
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