Academic Journal
Microscopic detection of nematodes in entomopathogenic nematode-enriched samples using a lightweight deep learning model.
| Title: | Microscopic detection of nematodes in entomopathogenic nematode-enriched samples using a lightweight deep learning model. |
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| Authors: | Erdinç A; Department of Biosystems Engineering, Bursa Uludağ University, Bursa, Türkiye., Erdoğan H; Department of Biosystems Engineering, Bursa Uludağ University, Bursa, Türkiye. Electronic address: hilalerdogan@uludag.edu.tr. |
| Source: | Journal of invertebrate pathology [J Invertebr Pathol] 2026 Jul; Vol. 217, pp. 108594. Date of Electronic Publication: 2026 Mar 06. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Academic Press Country of Publication: United States NLM ID: 0014067 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1096-0805 (Electronic) Linking ISSN: 00222011 NLM ISO Abbreviation: J Invertebr Pathol Subsets: MEDLINE |
| Imprint Name(s): | Publication: New York, NY : Academic Press Original Publication: New York. |
| MeSH Terms: | Image Processing, Computer-Assisted*/methods , Microscopy*/methods , Rhabditida*/isolation & purification , Deep Learning* , Detection Algorithms*, Animals ; Nematoda |
| Abstract: | Automated detection of entomopathogenic nematodes (EPNs) is increasingly important in biological control research, where manual microscopic counting remains labour-intensive and prone to variability. Building upon existing computer vision approaches but differing in architectural design and computational efficiency, this study presents LightDetectorMS, an ultra-lightweight, anchor-free object detection framework optimized for microscopic imagery of laboratory-isolated Steinernema feltiae infective juveniles. The model was evaluated using five-fold cross-validation to assess reliability and generalizability. LightDetectorMS achieved a mean mAP@0.5 of 0.9119 (±0.0242) and a mean mAP@0.5:0.95 of 0.8207 (±0.0353), with precision and recall of 0.9184 (±0.0227) and 0.9382 (±0.0452), respectively, demonstrating stable performance across folds. The coefficient of variation remained below 5% for all metrics, supporting statistical consistency. The architecture contains only 62,991 parameters (0.46 MB) and operates at 152.5 FPS (6.56 ms per frame), enabling real-time processing even in dense microscopic fields with overlapping individuals. Comparative analysis with manual expert counting (n = 50) revealed a mean human counting time of 43.08 ± 3.00 s per sample, corresponding to 0.483 ± 0.03 nematodes per second. In contrast, LightDetectorMS processes equivalent workloads several thousand times faster while maintaining high detection reliability. These findings indicate that LightDetectorMS provides a computationally efficient and statistically robust solution for semi-automated quantification of EPNs in controlled laboratory environments, supporting large-scale biological monitoring and production workflows. (Copyright © 2026 Elsevier Inc. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Lightweight deep learning models; Microscopic imaging; Object detection; YOLOv12; YOLOv5 |
| Entry Date(s): | Date Created: 20260309 Date Completed: 20260605 Latest Revision: 20260609 |
| Update Code: | 20260610 |
| DOI: | 10.1016/j.jip.2026.108594 |
| PMID: | 41796953 |
| Database: | MEDLINE |
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| Items | – Name: Title Label: Title Group: Ti Data: Microscopic detection of nematodes in entomopathogenic nematode-enriched samples using a lightweight deep learning model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Erdinç+A%22">Erdinç A</searchLink>; Department of Biosystems Engineering, Bursa Uludağ University, Bursa, Türkiye.<br /><searchLink fieldCode="AU" term="%22Erdoğan+H%22">Erdoğan H</searchLink>; Department of Biosystems Engineering, Bursa Uludağ University, Bursa, Türkiye. Electronic address: hilalerdogan@uludag.edu.tr. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%220014067%22">Journal of invertebrate pathology</searchLink> [J Invertebr Pathol] 2026 Jul; Vol. 217, pp. 108594. <i>Date of Electronic Publication: </i>2026 Mar 06. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article – Name: Language Label: Language Group: Lang Data: English – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Academic+Press%22">Academic Press </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>0014067 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1096-0805 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2200222011%22">00222011 </searchLink><i>NLM ISO Abbreviation: </i>J Invertebr Pathol <i>Subsets: </i>MEDLINE – Name: PublisherInfo Label: Imprint Name(s) Group: PubInfo Data: <i>Publication</i>: New York, NY : Academic Press<br /><i>Original Publication</i>: New York. – Name: SubjectMESH Label: MeSH Terms Group: Su Data: <searchLink fieldCode="MM" term="%22Image+Processing%2C+Computer-Assisted%22">Image Processing, Computer-Assisted*</searchLink>/<searchLink fieldCode="MM" term="%22Image+Processing%2C+Computer-Assisted+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Microscopy%22">Microscopy*</searchLink>/<searchLink fieldCode="MM" term="%22Microscopy+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Rhabditida%22">Rhabditida*</searchLink>/<searchLink fieldCode="MM" term="%22Rhabditida+isolation+%26+purification%22">isolation & purification</searchLink> <br /><searchLink fieldCode="MM" term="%22Deep+Learning%22">Deep Learning*</searchLink> <br /><searchLink fieldCode="MM" term="%22Detection+Algorithms%22">Detection Algorithms*</searchLink><br /><searchLink fieldCode="MH" term="%22Animals%22">Animals</searchLink> ; <searchLink fieldCode="MH" term="%22Nematoda%22">Nematoda</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Automated detection of entomopathogenic nematodes (EPNs) is increasingly important in biological control research, where manual microscopic counting remains labour-intensive and prone to variability. Building upon existing computer vision approaches but differing in architectural design and computational efficiency, this study presents LightDetectorMS, an ultra-lightweight, anchor-free object detection framework optimized for microscopic imagery of laboratory-isolated Steinernema feltiae infective juveniles. The model was evaluated using five-fold cross-validation to assess reliability and generalizability. LightDetectorMS achieved a mean mAP@0.5 of 0.9119 (±0.0242) and a mean mAP@0.5:0.95 of 0.8207 (±0.0353), with precision and recall of 0.9184 (±0.0227) and 0.9382 (±0.0452), respectively, demonstrating stable performance across folds. The coefficient of variation remained below 5% for all metrics, supporting statistical consistency. The architecture contains only 62,991 parameters (0.46 MB) and operates at 152.5 FPS (6.56 ms per frame), enabling real-time processing even in dense microscopic fields with overlapping individuals. Comparative analysis with manual expert counting (n = 50) revealed a mean human counting time of 43.08 ± 3.00 s per sample, corresponding to 0.483 ± 0.03 nematodes per second. In contrast, LightDetectorMS processes equivalent workloads several thousand times faster while maintaining high detection reliability. These findings indicate that LightDetectorMS provides a computationally efficient and statistically robust solution for semi-automated quantification of EPNs in controlled laboratory environments, supporting large-scale biological monitoring and production workflows.<br /> (Copyright © 2026 Elsevier Inc. All rights reserved.) – Name: Abstract Label: Competing Interests Group: Ab Data: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. – Name: SubjectMinor Label: Contributed Indexing Group: Data: <i>Keywords: </i>Lightweight deep learning models; Microscopic imaging; Object detection; YOLOv12; YOLOv5 – Name: DateEntry Label: Entry Date(s) Group: Date Data: <i>Date Created: </i>20260309 <i>Date Completed: </i>20260605 <i>Latest Revision: </i>20260609 – Name: DateUpdate Label: Update Code Group: Date Data: 20260610 – Name: DOI Label: DOI Group: ID Data: 10.1016/j.jip.2026.108594 – Name: AN Label: PMID Group: ID Data: 41796953 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.jip.2026.108594 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: 108594 Subjects: – SubjectFull: Animals Type: general – SubjectFull: Nematoda Type: general – SubjectFull: Image Processing, Computer-Assisted methods Type: general – SubjectFull: Microscopy methods Type: general – SubjectFull: Rhabditida isolation & purification Type: general – SubjectFull: Deep Learning Type: general – SubjectFull: Detection Algorithms Type: general Titles: – TitleFull: Microscopic detection of nematodes in entomopathogenic nematode-enriched samples using a lightweight deep learning model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Erdinç A – PersonEntity: Name: NameFull: Erdoğan H IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: 2026 Jul Type: published Y: 2026 Identifiers: – Type: issn-electronic Value: 1096-0805 Numbering: – Type: volume Value: 217 Titles: – TitleFull: Journal of invertebrate pathology Type: main |
| ResultId | 1 |