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This paper introduces a computer vision pipeline using YOLOv11-seg to automatically identify plant-parasitic nematodes at the genus level from microscopic images. The study addresses the need for faster and more efficient nematode identification compared to traditional methods like morphology and molecular markers. The trained model achieved high accuracy in identifying key PPN genera (RKN, RLN, SRN) with F1-scores >0.92 and AUC >0.93 on the test set, demonstrating the potential of AI for automated plant pathogen detection.
AI can now identify plant-parasitic nematodes with high accuracy, potentially revolutionizing agricultural diagnostics.
Early and accurate identification and quantification of plant-parasitic nematodes (PPN) is crucial for their effective control. Although valuable, the current techniques for identifying PPN, such as morphology and molecular marker-based methods, can be time and resource-intensive. This study aims 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 (RLN; Pratylenchus spp.), root-knot (RKN; Meloidogyne spp.), and stubby root (SRN; Paratrichodorus and Trichodorus spp.), additional plant-parasitic nematodes (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 the 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, SRN; F1-scores >0.92; AUC >0.93 in the test set). While the NON-PARASITIC showed strong performance (F1-score > 0.846 and AUC >0.91), the PPN-OTHERS group performed poorly (test accuracy: 43.9%), frequently misclassified as RLN and NON-PARASITIC nematodes. The results highlight the potential of artificial intelligence-based tools in identifying PPN, paving the way for the long-term goal of developing automated detection and quantification systems for plant pathogens.
