AbstractNematodes, the most diverse group of invertebrate metazoans in the phylum Nematoda, inhabit a wide range of ecological conditions but remain largely understudied, with less than 3% of species explored. They play a vital role in organic matter recycling, and impact plant and animal health. Due to the lack of distinct identification features, taxonomical knowledge about nematodes, is limited. Traditional morphological classification, based on De Man’s indices, faces limitations due to insufficient variations and a shortage of trained taxonomists. While protein-based approaches can assist in identification, their complexity and the degradation of proteins may hinder accuracy. PCR methods have expanded our understanding of nematode diversity but are often restricted by expensive equipments and a lack of trained personnel. Techniques like next-generation sequencing (NGS), and phylogenetic analysis have improved species identification but can be time-consuming and resource-intensive. In contrast to conventional techniques, isothermal amplification methods such as loop-mediated isothermal amplification (LAMP) and recombinase polymerase amplification (RPA) offer faster, more sensitive and cost-effective alternatives though limited by complex primer design and contamination risks (false positive results). Recent advancements have also highlighted the potential of deep learning algorithms as robust tools for automating nematode identification, though their accuracy depends heavily on large, well-annotated datasets and high computational resources. Moreover, remote sensing technologies have been increasingly applied to detect nematode infestations at the field level. However, it is limited by spatial resolution and environmental factors. Every approach has its own advantages and disadvantages. Thus, integrating morphological, molecular, and digital tools offers a holistic and reliable approach for nematode diagnostics. This review critically examines both traditional and contemporary approaches employed in the identification of nematodes, with a focus on emerging molecular and digital innovations.
Lingan et al. (Wed,) studied this question.