Academic Journal
Microscopic detection of nematodes in entomopathogenic nematode-enriched samples using a lightweight deep learning model.
| Τίτλος: | Microscopic detection of nematodes in entomopathogenic nematode-enriched samples using a lightweight deep learning model. |
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| Συγγραφείς: | 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. |
| Πηγή: | Journal of invertebrate pathology [J Invertebr Pathol] 2026 Jul; Vol. 217, pp. 108594. Date of Electronic Publication: 2026 Mar 06. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | 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. |
| Ιατρικοί όροι (MeSH): | Image Processing, Computer-Assisted*/methods , Microscopy*/methods , Rhabditida*/isolation & purification , Deep Learning* , Detection Algorithms*, Animals ; Nematoda |
| Περίληψη: | 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 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1096-0805 |
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| DOI: | 10.1016/j.jip.2026.108594 |