<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

Plant Parasitic Nematode Identification in Complex Samples with Deep Learning

Department of Electrical & Computer Engineering

Department of Computer & Information Science and Engineering

Journal of Nematology

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Plant Parasitic Nematode Identification in Complex Samples with Deep Learning"}

Plant parasitic nematodes are significant contributors to yield loss worldwide, causing devastating losses to every crop species, in every climate. Mitigating...

Plant Parasitic Nematode Identification in Complex Samples with Deep Learning

Article Category: Research Note

Data publikacji: 16 paź 2023

Zakres stron: -

Otrzymano: 07 lip 2023

DOI: https://doi.org/10.2478/jofnem-2023-0045

Słowa kluczowe
detection, diagnosis, identification, deep learning, method, technique

© 2023 Sahil Agarwal et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Plant Parasitic Nematode Identification in Complex Samples with Deep Learning

Article Category: Research Note

Data publikacji: 16 paź 2023

Zakres stron: -

Otrzymano: 07 lip 2023

DOI: https://doi.org/10.2478/jofnem-2023-0045

Słowa kluczowedetection, diagnosis, identification, deep learning, method, technique

© 2023 Sahil Agarwal et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
detection, diagnosis, identification, deep learning, method, technique