Bridging field diagnostics and genomics: Contemporary and emerging tools for potato virus detection

Abstract

Potato crops are severely affected by a wide range of viral pathogens, particularly aphid-transmitted viruses such as potato virus Y (PVY), potato leafroll virus (PLRV), potato virus A (PVA), potato virus S (PVS), and potato virus M (PVM), together with the mechanically and vegetatively transmitted potato virus X (PVX). These viruses cause substantial yield losses, reduce tuber quality, and threaten global seed certification systems. Their epidemiology is strongly influenced by vector dynamics, mixed infections, and environmental factors, including climate change. Accurate and early detection is therefore essential for effective disease management. This narrative review synthesizes literature retrieved from Scopus, Web of Science, ScienceDirect, PubMed, and Google Scholar, with searches conducted up to 31 August 2026, drawing on approximately 200 primary and review sources retained after eligibility screening. Traditional diagnostic methods, including visual inspection, serological assays (ELISA), and conventional RT-PCR, remain widely used but suffer from limitations in sensitivity, specificity, and field applicability. Recent advances, such as isothermal amplification techniques (LAMP and RPA), next-generation sequencing (NGS), CRISPR-based diagnostics, and nanotechnology-enabled biosensors, offer improved sensitivity, multiplexing capacity, and potential for rapid on-site detection, although most remain confirmatory or research-stage tools rather than validated replacements for established assays. This review provides a comprehensive overview of major potato viruses and critically evaluates both conventional and emerging diagnostic technologies, highlighting their role in the development of sustainable virus management strategies. Principal limitations of the reviewed evidence base include inconsistent validation of emerging assays against field-representative samples, limited interlaboratory reproducibility data, and uneven reporting of quantitative performance metrics across studies.