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All Journal Journal of Advanced in Information and Industrial Technology (JAIIT)
Suhari, Ahmad Riznandi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Implementation of the YOLOv8n Model for Automatic Owl Detection in Swiftlet Farming Buildings Putra, Iqbal Kurniawan Asmar; Apriska Prameswari; Fikri, Muhammad Ainul; Suhari, Ahmad Riznandi
Journal of Advances in Information and Industrial Technology Vol. 7 No. 2 (2025): Nov
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v7i2.733

Abstract

Object detection based on digital images is a rapidly developing field in the application of intelligent systems. This study aims to create an automatic owl detection system utilizing the YOLOv8 deep learning model as a pest mitigation measure in the swiftlet farming industry. Owls are known to enter swiftlet houses at night and prey on the birds, causing economic losses. Owl image datasets were obtained from the Roboflow platform and annotated in YOLO format. The model was trained using the YOLOv8-nano architecture with a 640×640 pixel input resolution. The evaluation results showed that the model achieved a mAP@0.5 of 96.82% and mAP@0.5:0.95 of 70.5%, with a precision of 97.2% and a recall of 93.38%. These results indicate that the YOLOv8 model performs well and has the potential to be implemented as an automatic monitoring system in swiftlet farming environments.
Co-Authors Apriska Prameswari Fikri, Muhammad Ainul Putra, Iqbal Kurniawan Asmar