Deep learning-assisted milkfish fingerling counting:

Simple item page
creativework.keywordsimage preprocessing, milkfish fingerlings counter, segmentation, YOLOv8
dc.contributor.authorSibayan, Christian Jose
dc.contributor.authorAbando, Benjie R.
dc.contributor.authorAcosta, Justin P.
dc.contributor.authorMadrelijos, Johnico D.
dc.contributor.authorMarzan, Charlie S.
dc.date.accessioned2026-08-20T06:46:07Z
dc.date.available2026-08-20T06:46:07Z
dc.date.issued2024
dc.descriptionFull text.
dc.description.abstractThe manual counting of milkfish fingerlings within the fish industry in the Philippines poses significant challenges due to its time-consuming nature, labor intensiveness, and susceptibility to errors. To address these issues, this study presents a deep learning approach for the real-time counting of milkfish fingerlings to enhance both the speed and accuracy of fingerling counts. The dataset was meticulously curated from six milkfish fingerlings farms, with gathered videos converted into image frames and subjected to polygon annotation. Furthermore, various augmentation techniques were applied to these images to enhance training efficacy before integration into a deep neural network. Notably, implementing the YOLOv8n-seg object detector with segmentation and counting yielded a remarkable accuracy rate of up to 90.64% and 93.34%, respectively. This innovative methodology promises significant advancements in the automation and efficiency of milkfish fingerling counting processes within the fish industry, paving the way for improved productivity and accuracy in fishery management practices. © 2024 ACM.
dc.identifier.citationSibayan, C. J., Abando, B. R., Madrelijos, J. D., Acosta, J. P., & Marzan, C. S. (2024).Deep learning-assisted milkfish fingerling counting: Towards automation in fish industry practices. ACM International Conference Proceeding Series, 232-236. https://doi.org/10.1145/3674558.3674591
dc.identifier.urihttps://lakasa.dmmmsu.edu.ph/handle/123456789/2407
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.sdgSDG 2
dc.subjectComputer vision
dc.subjectDeep learning
dc.subjectYOLOv8
dc.subjectImage segmentation
dc.subjectImage preprocessing
dc.subjectObject detection
dc.subjectAquaculture
dc.subjectMilkfish
dc.subjectFingerlings counting
dc.subject.ddc006.4
dc.subject.lcshComputer vision
dc.subject.lcshImage processing--Computer programs
dc.subject.lcshNeural networks (Computer science)
dc.subject.lcshObject recognition (Computer vision)
dc.subject.lcshAquaculture
dc.subject.lcshComputer vision
dc.subject.lcshImage processing--Computer programs
dc.subject.lcshNeural networks (Computer science)
dc.subject.lcshObject recognition (Computer vision)
dc.subject.lcshAquaculture
dc.subject.lcshMilkfish
dc.titleDeep learning-assisted milkfish fingerling counting:
dc.title.alternativeTowards automation in fish industry practices
dc.typeArticle
oaire.citation.endPage236
oaire.citation.startPage232
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
PUB-JAPE-MLUC-2024-SibayanCJ-FT.pdf
Size:
2.68 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed to upon submission
Description:
Download
Collections
Journal articles published externally