Artificial Intelligence-Based Tools for Automated Genus-Level Identification of Plant-Parasitic Nematodes

Early and accurate identification and quantification of plant-parasitic nematodes (PPNs) is crucial for their effective control. Although valuable, the current techniques for identifying PPNs, such as morphology and molecular marker-based methods, can be time- and resource-intensive. This study aimed to develop and validate cutting-edge computer vision tools for automated, accurate, and reproducible PPN detection. To achieve this goal, we captured microscopic images of the three economically important PPN genera associated with potato crop: root-lesion nematode (RLN; Pratylenchus spp.), root-knot nematode (RKN; Meloidogyne spp.), and stubby root nematode (SRN; Paratrichodorus and Trichodorus spp.), as well as additional PPNs (PPN-OTHERS) and non-parasitic (NON-PARASITIC) nematodes, for a total of five groups. The captured images (total instances = 8,654) were preprocessed, annotated, and randomly split into three datasets: 75% for training, 15% for validation, and 10% for testing. An object segmentation algorithm, YOLOv11-seg, which predicts each pixel in an image, was trained and evaluated on previously unseen images. The model achieved high accuracy in validation (92.4%) and test (88.6%) datasets, with strong performance for key PPN genera (RKN, RLN, and SRN; F1 scores > 0.92; AUC > 0.93 in the test set). Whereas the NON-PARASITIC group showed strong performance (F1 score > 0.846 and AUC > 0.91), the PPN-OTHERS group performed poorly (test accuracy: 43.9%), frequently misclassified as RLNs and NON-PARASITIC nematodes. The results highlight the potential of artificial intelligence-based tools for identifying PPNs, paving the way for the long-term goal of developing automated detection and quantification systems for plant pathogens.