A deep learning framework for species-level identification of root-knot nematodes using perineal pattern images

The use of machine learning is an emerging approach in precision agriculture, including pest identification, disease diagnostics, and soil health monitoring. Root-knot nematodes (Meloidogyne spp.) are a key group of soil-dwelling metazoans that cause plant diseases and lead to significant yield losses in major crops. Identification of these nematodes is essential for pest management. Manual identification of large samples is time-consuming, labour-intensive, and subject to inter- and intra-rater variations, which may affect the consistency and reliability of results. Moreover, the number of skilled taxonomists is on the decline. Automating the identification process can enhance accuracy and consistency while significantly reducing the time required for sample identification. In this study, a deep-learning architecture was developed using convolutional neural networks (CNNs) to identify three major root-knot species: Meloidogyne graminicola, M. incognita, and M. javanica, which are among the most economically damaging plant-parasitic nematodes, causing significant yield losses in major crops worldwide. The algorithm, based on AlexNet and VGG16 architectures, achieved an accuracy of ~ 95%. In contrast, manual annotation by three independent annotators yielded moderate agreement (Kappa = 0.56), underscoring the challenges of inter-rater variability in large-scale nematode identification. Integrated Gradients analysis revealed that the model used taxonomically relevant features of the perineal pattern images during classification. The model also showed stable training behaviour across cross-validation folds, with minimal signs of overfitting. The machine learning model demonstrated improved reliability and precision in nematode identification. By addressing the challenge of accurate taxonomic classification, especially for non-experts, this approach offers a new paradigm for rapid and consistent identification of nematodes, essential for large-scale deployment of diagnostics and precision management of plant-parasitic nematodes.