Progress in tomato anthracnose disease diagnosis: from traditional to molecular and emerging techniques

Anthracnose, caused by multiple Colletotrichum species, is among the most destructive diseases affecting fruits and vegetables worldwide. In tomato, it compromises both pre-harvest and post-harvest fruit quality. Effective management of tomato anthracnose relies heavily on early detection and precise pathogen identification. This paper reviews recent progress in diagnostic methods for tomato anthracnose, emphasizing both traditional and emerging technologies. Conventional diagnosis begins with classical mycological techniques, including isolation of the fungal pathogen from infected host tissues and morphological identification of fungal isolates under microscopy. Molecular identification using the Polymerase Chain Reaction (PCR), which remains the gold standard for identifying Colletotrichum species, offers high sensitivity and specificity by targeting unique DNA sequence variations. In recent years, field-deployable rapid diagnostic tools have gained prominence. Techniques such as Loop-mediated Isothermal Amplification (LAMP) enable rapid on-site detection. Recombinase Polymerase Amplification (RPA) can be integrated into lateral flow dipsticks (LFDs) for user-friendly application. Meanwhile, emerging diagnostic approaches include spectroscopy-based techniques (e.g., Fourier-transform infrared spectroscopy with attenuated total reflectance), sensor-based platforms (e.g., electronic noses and chip-based biosensors), and artificial intelligence-driven image classification methods (e.g., convolutional neural network models such as Densenet_Xception and TomConv). Each diagnostic method offers distinct advantages and limitations. Therefore, an integrated or polyphasic approach combining morphological, molecular, and data-driven methods provides the most reliable framework for accurate diagnosis.