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Precision Farming Multimodal Technologies Using Optical Sensors for the Detection of Citrus Tristeza Virus Endemics

Author

Listed:
  • Athanasios V. Argyriou

    (Plant Pathology Laboratory, Institute of Olive Tree, Subtropical Crops and Viticulture, ELGO-DIMITRA, Karamanlis Ave. 167, 73134 Chania, Greece
    Laboratory of Geophysical-Satellite Remote Sensing and Archaeoenvironment, Institute for Mediterranean Studies, Foundation for Research and Technology-Hellas (FORTH), 74100 Rethymno, Greece)

  • Nikolaos Tektonidis

    (Plant Pathology Laboratory, Institute of Olive Tree, Subtropical Crops and Viticulture, ELGO-DIMITRA, Karamanlis Ave. 167, 73134 Chania, Greece)

  • Evangelos Alevizos

    (Institute des Substances et Organismes de la Mer (ISOMer), Nantes Universite, UR 2160, F-44000 Nantes, France)

  • Konstantinos P. Ferentinos

    (Department of Agricultural Engineering, Soil & Water Resources Institute, ELGO-DIMITRA, 61 Dimokratias Av., 13561 Athens, Greece)

  • Nektarios N. Kourgialas

    (Water Recourses-Irrigation & Environmental Geoinformatics Laboratory, Institute of Olive Tree, Subtropical Crops and Viticulture, ELGO-DIMITRA, Karamanlis Ave. 167, 73134 Chania, Greece)

  • Matthaios M. Mathioudakis

    (Plant Pathology Laboratory, Institute of Olive Tree, Subtropical Crops and Viticulture, ELGO-DIMITRA, Karamanlis Ave. 167, 73134 Chania, Greece)

Abstract

Citrus trees and their fruits have significant nutritional value and contain antioxidants that are important components of the Mediterranean diet. However, pathogenic diseases pose a threat to citriculture by reducing crop yield and quality. Therefore, there is a need for novel technologies to maintain healthy citrus crops and enable early and accurate detection of the related pathogens, such as the citrus tristeza virus (CTV). Remote sensing offers a non-destructive, cost effective and efficient method for assessing plant health dynamics. It can provide insights into chlorophyll content, water stress and disease presence. This study provides new insights by integrating a combination of remote sensing approaches (FCCs, NDVI, PCA), optical and proximal techniques with in situ field data collection as well as various serological/molecular technologies to detect CTV effectively and evaluate its temporal epidemiology pattern. In addition, the integration of the adopted techniques in case studies of known fields being infected by CTV provides the basis for remote sensing procedures, such as random forest machine learning algorithm, to become powerful in verifying and identifying new CTV-infected fields in a broader extent coverage area, reaching 89.7% accuracy assessment. Thus, it offers decision-makers a robust approach that contributes to CTV epidemiology monitoring and can aid in the development of effective and sustainable disease management strategies.

Suggested Citation

  • Athanasios V. Argyriou & Nikolaos Tektonidis & Evangelos Alevizos & Konstantinos P. Ferentinos & Nektarios N. Kourgialas & Matthaios M. Mathioudakis, 2024. "Precision Farming Multimodal Technologies Using Optical Sensors for the Detection of Citrus Tristeza Virus Endemics," Sustainability, MDPI, vol. 16(13), pages 1-26, July.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:13:p:5748-:d:1429679
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