Lightweight vision models for egg fertility detection

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creativework.keywordschicken egg fertility; computer vision; deep learning; YOLO
dc.contributor.authorFlores, Earl John
dc.date.accessioned2026-07-17T05:34:55Z
dc.date.available2026-07-17T05:34:55Z
dc.date.issued2025-12-03
dc.description.abstractThe Philippine chicken industry relies heavily on effective egg fertility detection to sustain its growth. Traditional manual candling techniques are prone to human error and inefficiency; thus, there is a need for automation. While deep learning models such as Convolutional Neural Networks (CNNs) and YOLO have shown great potential for real-time object detection, a noteworthy gap in prior research is their application to native Philippine chickens due to a lack of publicly available image datasets. The current study fills this gap by developing and optimizing a YOLOv11 model for egg fertility detection in native Philippine chickens. A grid search was implemented to tune key parameters, such as the learning rate, optimizer (SGD, Adam, RMSProp), and weight decay, to improve the detector. Overall, careful tuning increases the model's performance. The best configuration used SGD with a 0.01 learning rate and a 0.0001 weight decay. The tuned model outperformed the baseline model in terms of mAP50-95, precision, and recall, while having faster inference speed. This focused tuning makes YOLOv11 more reliable in detecting the fertility of chicken eggs.
dc.identifier.citationFlores, E. J. (2025). Lightweight vision models for egg fertility detection. Engineering, Technology & Applied Science Research, 16(1), 31618-31623. https://doi.org/10.48084/etasr.15693
dc.identifier.doihttps://doi.org/10.48084/etasr.15693
dc.identifier.issne1792-8036
dc.identifier.issn2241-4487
dc.identifier.urihttps://lakasa.dmmmsu.edu.ph/handle/123456789/2209
dc.language.isoen_US
dc.publisherEngineering, Technology & Applied Science Research
dc.rights.licenseCC-BY 4.0
dc.sdgSDG 2
dc.sdgSDG 9
dc.sdgSDG 12
dc.subjectChicken egg fertility
dc.subjectComputer vision
dc.subjectDeep learning
dc.subjectYOLO
dc.subjectYOLOv11
dc.subjectObject detection
dc.subjectHyperparameter optimization (Grid search)
dc.subjectEgg candling automation
dc.subjectNative Philippine chickens
dc.subjectHatchery automation
dc.subject.ddcPoultry Breeding and Reproduction
dc.subject.ddcEgg Incubation
dc.subject.ddcComputer Vision
dc.subject.ddcMachine Learning
dc.subject.ddcDeep Learning
dc.subject.lcshEggs--Fertility-Detection-Automation
dc.subject.lcshComputer vision in agriculture
dc.subject.lcshDeep learning (Machine learning)
dc.subject.lcshYOLO (Computer science)
dc.subject.lcshChickens—Philippines
dc.titleLightweight vision models for egg fertility detection
dc.typeArticle
local.subject.scientificnameGallus gallus domesticus
oaire.citation.endPage31623
oaire.citation.issue1
oaire.citation.startPage31618
oaire.citation.volume16
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