Open Access

Plant Parasitic Nematode Identification in Complex Samples with Deep Learning

Sahil Agarwal

Sahil Agarwal

Department of Electrical & Computer Engineering, University of FloridaGainesville,
Search for this author on
Sciendo|Google Scholar
Agarwal, Sahil
, 
Zachary C. Curran

Zachary C. Curran

Department of Computer & Information Science and Engineering, University of FloridaGainesville,
Search for this author on
Sciendo|Google Scholar
Curran, Zachary C.
, 
Guohao Yu

Guohao Yu

Department of Electrical & Computer Engineering, University of FloridaGainesville,
Search for this author on
Sciendo|Google Scholar
Yu, Guohao
, 
Shova Mishra

Shova Mishra

Department of Entomology and Nematology, University of FloridaGainesville,
Search for this author on
Sciendo|Google Scholar
Mishra, Shova
, 
Anil Baniya

Anil Baniya

Department of Entomology and Nematology, University of FloridaGainesville,
Search for this author on
Sciendo|Google Scholar
Baniya, Anil
, 
Mesfin Bogale

Mesfin Bogale

Department of Entomology and Nematology, University of FloridaGainesville,
Search for this author on
Sciendo|Google Scholar
Bogale, Mesfin
, 
Kody Hughes

Kody Hughes

Department of Entomology and Nematology, University of FloridaGainesville,
Search for this author on
Sciendo|Google Scholar
Hughes, Kody
, 
Oscar Salichs

Oscar Salichs

Department of Entomology and Nematology, University of FloridaGainesville,
Search for this author on
Sciendo|Google Scholar
Salichs, Oscar
, 
Alina Zare

Alina Zare

Department of Electrical & Computer Engineering, University of FloridaGainesville,
Search for this author on
Sciendo|Google Scholar
Zare, Alina
, 
Zhe Jiang

Zhe Jiang

Department of Computer & Information Science and Engineering, University of FloridaGainesville,
Search for this author on
Sciendo|Google Scholar
Jiang, Zhe
 and 
Peter DiGennaro

Peter DiGennaro

Department of Entomology and Nematology, University of FloridaGainesville,
Search for this author on
Sciendo|Google Scholar
DiGennaro, Peter
  
Oct 16, 2023
Plant Parasitic Nematode Identification in Complex Samples with Deep Learning's Cover Image
About this article
Previous Article
Next Article
Cite
Download Cover
Article Category: Research Note
Published Online: Oct 16, 2023
Received: Jul 07, 2023
DOI: https://doi.org/10.2478/jofnem-2023-0045
Keywords
© 2023 Sahil Agarwal et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1:

Representative sample image of nematodes extracted from soil samples sent to NDL. Colored boxes indicate annotations of plant parasitic nematode heads.
Representative sample image of nematodes extracted from soil samples sent to NDL. Colored boxes indicate annotations of plant parasitic nematode heads.

Model and corresponding maximum mAP_

Model Max MAP_0.5
YOLOv5x Baseline 0.787
CenterNet HourGlass104 512×512 0.6929
EfficientDet D1 640×640 0.6489
SSD ResNet50 V1 FPN 640×640 0.6439

Modified YOLOv5 models with precision and recall_

Model Max MAP_0.5 Max Precision Max Recall
YOLOv5x Baseline 0.787 0.836 0.772
YOLOv5x-Bi-FPN 0.791 0.851 0.753
YOLO-v5x Densenet 0.769 0.831 0.782
YOLOv5x-Deeper 0.779 0.831 0.774
Language:
English
Publication timeframe:
1 times per year
Journal Subjects:
Life Sciences, Life Sciences, other
Journal RSS Feed