Analytical advances in citrus disease detection: Conventional molecular assays and emerging analytical technologies

Citrus diseases caused by fungal, bacterial, and viral pathogens continue to threaten global citrus production, creating an urgent need for analytical technologies that enable accurate, rapid, and field-deployable disease diagnosis. This review critically examines the evolution of citrus disease diagnostics, encompassing conventional molecular and immunological methods together with emerging electrochemical, spectroscopic, imaging, and artificial intelligence (AI)-assisted analytical approaches. Particular emphasis is placed on analytical performance, including sensitivity, selectivity, reproducibility, matrix effects, analytical robustness, and practical applicability, as well as on the contribution of nanomaterials and advanced sensing interfaces to improving analytical capability. Conventional techniques, including polymerase chain reaction (PCR), quantitative PCR (qPCR), enzyme-linked immunosorbent assay (ELISA), and lateral flow assays (LFA), are discussed alongside recent advances in electrochemical biosensors, Raman spectroscopy, surface-enhanced Raman spectroscopy (SERS), laser-induced breakdown spectroscopy (LIBS), hyperspectral imaging (HSI), fluorescence-based methods, and remote sensing technologies. The review further examines the growing role of chemometrics, machine learning, and deep learning in extracting diagnostic information from complex multidimensional datasets. It discusses their integration with Internet of Things (IoT) architectures and precision agriculture for continuous disease surveillance and decision support. Beyond analytical performance, critical challenges associated with technology readiness, standardization, scalability, commercialization, regulatory acceptance, external validation, and field implementation are comprehensively discussed. Overall, the evidence indicates that future citrus disease diagnostics will rely on interoperable and complementary analytical workflows that integrate molecular specificity with electrochemical, spectroscopic, imaging, and computational technologies to enable early disease detection, precision phytosanitary management, and robust decision-making under real agricultural conditions.