Evaluating Approaches for Identifying and Classifying Plant Diseases

Abstract

Plant diseases are caused by a wide range of microorganisms, including fungi, viruses, bacteria, mycoplasmas, and nematodes. Among these, major plant-pathogenic microbes are responsible for significant crop losses, leading to both direct and indirect economic damage. In the context of global climate change and the increasing virulence of newly emerged transboundary pathogens, traditional agricultural practices, such as the repeated use of the same crop varieties, reliance on the same plant protection chemicals, and the import/export of planting materials, have become increasingly vulnerable. Early detection, accurate diagnosis, and proper identification of disease-causing microorganisms are therefore critical for enhancing annual crop productivity. This chapter highlights various methods for plant disease detection and classification, encompassing traditional techniques such as visual inspection, microscopy, nucleic acid-based methods [e.g., polymerase chain reaction (PCR) and gene sequencing], and serology-based approaches [e.g., enzyme-linked immunosorbent assay (ELISA), dot immunobinding assay (DIBA), tissue blot immunoassay (TIBA), and immunosorbent electron microscopy (ISEM)], along with their working principles, advantages, and practical applications. In addition to these conventional laboratory methods, recent innovations in plant disease detection are also explored, including spectroscopy-based techniques, advanced AI-driven approaches, nanotechnology applications, and Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas/genome editing-based diagnostics. A comparative analysis of diagnostic tools based on key criteria and their future prospects is provided. Collectively, these detection and classification methods offer accurate, on-site results, enabling farmers to adopt effective disease management strategies.