Proceedings Article | 13 September 2024
KEYWORDS: Diseases and disorders, Remote sensing, Satellites, Unmanned aerial vehicles, Pathogens, Vegetation, MODIS, Crop monitoring, Satellite imaging, Image segmentation
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 feeder insects, mainly Philaenus spumarius. Infected trees develop symptoms that resemble 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.
