Publication:
Entomopathogenic nematode detection and counting model developed based on A-star algorithm

dc.contributor.authorErdoğan, Hilal
dc.contributor.buuauthorERDOĞAN, HİLAL
dc.contributor.departmentZiraat Fakültesi
dc.contributor.departmentBiyosistem Mühendisliği Bölümü
dc.contributor.orcid0000-0002-0387-2600
dc.contributor.researcheridAAP-5834-2020
dc.date.accessioned2025-01-17T06:40:52Z
dc.date.available2025-01-17T06:40:52Z
dc.date.issued2024-09-13
dc.description.abstractEntomopathogenic nematodes are soil-dwelling living organisms widely employed in the biological control of agricultural insect pests, serving as a significant alternative to pesticides. In laboratory procedures, the counting process remains the most common, labor-intensive, time-consuming, and approximate aspect of studies related to entomopathogenic nematodes. In this context, a novel method has been proposed for the detection and quantification of Steinernema feltiae isolate using computer vision on microscope images. The proposed method involves two primary algorithmic steps: framing and isolation. Compared to YOLO-V5m, YOLO-V7m, and YOLOV8m, the A-star-based developed network demonstrates significantly improved detection accuracy compared to other networks. The novel method is particularly effective in facilitating the detection of overlapping nematodes. The developed algorithm excludes processes that increase space and time complexity, such as the weight document, which contains the learned parameters of the deep learning model, model integration, and prediction time, resulting in more efficient operation. The results indicate the feasibility of the proposed method for detecting and counting entomopathogenic nematodes.
dc.identifier.doi10.1016/j.jip.2024.108196
dc.identifier.eissn1096-0805
dc.identifier.issn0022-2011
dc.identifier.scopus2-s2.0-85203659653
dc.identifier.urihttps://doi.org/10.1016/j.jip.2024.108196
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0022201124001393
dc.identifier.urihttps://hdl.handle.net/11452/49534
dc.identifier.volume207
dc.identifier.wos001316665800001
dc.indexed.wosWOS.SCI
dc.language.isoen
dc.publisherElsevier Science
dc.relation.journalJournal of Invertebrate Pathology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectHeterorhabditis-bacteriophora poinar
dc.subjectSteinernema-carpocapsae
dc.subjectTemperature
dc.subjectPathogens
dc.subjectViability
dc.subjectFitness
dc.subjectArtificial intelligence
dc.subjectNematode segmentation
dc.subjectSteinernema feltiae
dc.subjectYolo-v5
dc.subjectYolo-v7
dc.subjectYolo-v8
dc.subjectScience & technology
dc.subjectLife sciences & biomedicine
dc.subjectZoology
dc.titleEntomopathogenic nematode detection and counting model developed based on A-star algorithm
dc.typeArticle
dspace.entity.typePublication
local.contributor.departmentZiraat Fakültesi/Biyosistem Mühendisliği Bölümü
local.indexed.atWOS
local.indexed.atScopus
relation.isAuthorOfPublication8a30d083-11ef-49d4-b80e-55752fc324f2
relation.isAuthorOfPublication.latestForDiscovery8a30d083-11ef-49d4-b80e-55752fc324f2

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