OnlineFirst Articles
EUPHRESCO III-Special Issue on Plant Health Research Priorities-REVIEW

Integrating digital technologies and artificial intelligence in plant protection: diagnosis, prognosis, surveillance, and management of plant pests and diseases

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  • Spyridoula DIMITROPOULOU ,
  • Fotis DELIGIANNIS,
  • Aleksandar DUJAKOVIC,
  • Francesco VUOLO,
  • Spyros FOUNTAS,
  • Dimitrios I. TSITSIGIANNIS
Spyridoula DIMITROPOULOU
Department of Crop Science, Agricultural University of Athens, Athens 11855, Greece
Fotis DELIGIANNIS
Department of Natural Resources Development and Agricultural Engineering, Agricultural University of Athens, Athens 11855, Greece
Aleksandar DUJAKOVIC
BOKU University, Institute of Geomatics, Department of Ecosystem Management, Climate and Biodiversity, Peter‑Jordan‑Straße 82, Vienna 1190, Austria
Francesco VUOLO
BOKU University, Institute of Geomatics, Department of Ecosystem Management, Climate and Biodiversity, Peter‑Jordan‑Straße 82, Vienna 1190, Austria
Spyros FOUNTAS
Department of Natural Resources Development and Agricultural Engineering, Agricultural University of Athens, Athens 11855, Greece
Dimitrios I. TSITSIGIANNIS
Department of Crop Science, Agricultural University of Athens, Athens 11855, Greece
DOI: https://doi.org/10.36253/phyto-17431
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Published 2026-10-27

Keywords

  • plant health,
  • digital plant pathology,
  • decision support systems,
  • AI-driven disease detection,
  • remote sensing

How to Cite

[1]
S. DIMITROPOULOU, F. DELIGIANNIS, A. DUJAKOVIC, F. VUOLO, S. FOUNTAS, and D. I. TSITSIGIANNIS, “Integrating digital technologies and artificial intelligence in plant protection: diagnosis, prognosis, surveillance, and management of plant pests and diseases”, Phytopathol. Mediterr., Oct. 2026.

Copyright (c) 2026 Spyridoula DIMITROPOULOU , Fotis DELIGIANNIS, Aleksandar DUJAKOVIC, Francesco VUOLO, Spyros FOUNTAS, Dimitrios I. TSITSIGIANNIS

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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Abstract

As world population continues to grow, sustainable food production has become an increasingly urgent priority. At the same time, emergence and spread of invasive plant pests and diseases is causing new outbreaks that threaten plant health, reduce agricultural productivity, and undermine environmental sustainability. Conventional diagnostic approaches, based primarily on visual inspections and laboratory analyses, remain fundamental in plant pathology; however, these are often time-consuming, rely heavily on expert knowledge, have limited sensitivity at early stages, and are constrained by low scalability and throughput. In addition, their inherent subjectivity makes them prone to error, frequently leading to delayed plant protection interventions and suboptimal disease management. Precision agriculture has emerged as a transformative paradigm that integrates advanced sensing, data analytics, automation technologies, and artificial intelligence, to support early detection, continuous monitoring, and site-specific management of plant biotic and abiotic stressors. The present review synthesizes recent advances in plant pest and disease monitoring, covering current biochemical and molecular diagnostic methods and precision agriculture technologies that are organized into two complementary domains: hardware technologies, including sensing systems, positioning and communication networks, application platforms; and software technologies, including data management, machine and deep learning, modelling, and decision-support systems. Current evidence indicates that integration of spectral imaging, environmental sensing, and artificial intelligence can substantially improve accuracy, timeliness, and scalability of pest and disease detection, while supporting targeted use and reduction of agrochemical inputs. Despite rapid technological progress, however, major challenges remain, including data interoperability, model robustness under heterogeneous field conditions, economic feasibility, regulatory alignment, and farmer adoption. This review highlights current molecular and digital diagnostic capabilities, identifies key research gaps, and outlines future directions for developing resilient, cost-effective, and sustainable precision agriculture systems for plant pest and disease management. Convergence of molecular diagnostics, smart sensing technologies, and artificial intelligence, is redefining plant pathology, enabling transition from reactive disease control toward proactive, data-driven disease management.

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