Hybrid Autofluorescence and Optoacoustic Microscopy for the Label-Free, Early and Rapid Detection of Pathogenic Infections in Vegetative Tissues

Author:

Tserevelakis George J.1ORCID,Theocharis Andreas2,Spyropoulou Stavroula1,Trantas Emmanouil23ORCID,Goumas Dimitrios23,Ververidis Filippos23ORCID,Zacharakis Giannis1

Affiliation:

1. Foundation for Research and Technology Hellas, Institute of Electronic Structure and Laser, N. Plastira 100, GR-70013 Heraklion, Crete, Greece

2. Department of Agriculture, School of Agricultural Sciences, Hellenic Mediterranean University, Estavromenos, GR-71410 Heraklion, Crete, Greece

3. Institute of Agri-Food and Life Sciences, University Research Centre, Hellenic Mediterranean University, GR-71410 Heraklion, Crete, Greece

Abstract

Agriculture plays a pivotal role in food security and food security is challenged by pests and pathogens. Due to these challenges, the yields and quality of agricultural production are reduced and, in response, restrictions in the trade of plant products are applied. Governments have collaborated to establish robust phytosanitary measures, promote disease surveillance, and invest in research and development to mitigate the impact on food security. Classic as well as modernized tools for disease diagnosis and pathogen surveillance do exist, but most of these are time-consuming, laborious, or are less sensitive. To that end, we propose the innovative application of a hybrid imaging approach through the combination of confocal fluorescence and optoacoustic imaging microscopy. This has allowed us to non-destructively detect the physiological changes that occur in plant tissues as a result of a pathogen-induced interaction well before visual symptoms occur. When broccoli leaves were artificially infected with Xanthomonas campestris pv. campestris (Xcc), eventually causing an economically important bacterial disease, the induced optical absorption alterations could be detected at very early stages of infection. Therefore, this innovative microscopy approach was positively utilized to detect the disease caused by a plant pathogen, showing that it can also be employed to detect quarantine pathogens such as Xylella fastidiosa.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging

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