Plant Parasitic Nematode Identification in Complex Samples with Deep Learning

• Published in: Journal of nematology Vol. 55; no. 1; pp. 20230045 - 915
• Main Authors: Agarwal, Sahil, Curran, Zachary C., Yu, Guohao, Mishra, Shova, Baniya, Anil, Bogale, Mesfin, Hughes, Kody, Salichs, Oscar, Zare, Alina, Jiang, Zhe, DiGennaro, Peter
• Format: Journal Article
• Language: English
• Published: Poland Sciendo 01.02.2023
• Subjects: deep learning; detection; diagnosis; identification; method; Research Note; technique; deep learning; diagnosis; technique; detection; identification; method
• ISSN: 0022-300X; 2640-396X; 2640-396X
• DOI: 10.2478/jofnem-2023-0045

Plant parasitic nematodes are significant contributors to yield loss worldwide, causing devastating losses to every crop species, in every climate. Mitigating these losses requires swift and informed management strategies, centered on identification and quantification of field populations. Current plant parasitic nematode identification methods rely heavily on manual analyses of microscope images by a highly trained nematologist. This mode is not only expensive and time consuming, but often excludes the possibility of widely sharing and disseminating results to inform regional trends and potential emergent issues. This work presents a new public dataset containing annotated images of plant parasitic nematodes from heterologous soil extractions. This dataset serves to propagate new automated methodologies or speedier plant parasitic nematode identification using multiple deep learning object detection models and offers a path towards widely shared tools, results, and meta-analyses.