Implementation of the YOLOv8n Model for Automatic Owl Detection in Swiftlet Farming Buildings
DOI:
https://doi.org/10.52435/jaiit.v7i2.733Keywords:
Deep Learning, Object Detection, Owl, YOLOv8, YOLOv8nAbstract
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 [email protected] of 96.82% and [email protected]: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.
References
S. Kurnia et al., “JEPIN (Jurnal Edukasi dan Penelitian Informatika) Perancangan Prototipe Sistem Kontrol Suhu dan Kelembaban pada Gedung Walet dengan Mikrokontroler Berbasis Mobile”.
C. Hakim et al., “Studi Budidaya Burung Walet di Wilayah Gorontalo Kawasan Teluk Tominis,” Pontianak, Jan. 2024. [Online]. Available: https://journal.ikippgriptk.ac.id/index.php/snpp/article/view/6984
Yovi Apridiansyah, A. Wijaya, Pahrizal, Rozali Toyib, and Arif Setiawan, “Pengolahan Citra Berbasis Video Proccesing dengan Metode Frame Difference untuk Deteksi Gerak,” Journal of Applied Computer Science and Technology, vol. 5, no. 1, pp. 81–89, Jun. 2024, doi: 10.52158/jacost.v5i1.790.
Y. Akkem, S. K. Biswas, and A. Varanasi, “Smart farming using artificial intelligence: A review,” Eng Appl Artif Intell, vol. 120, Apr. 2023, doi: 10.1016/j.engappai.2023.105899.
M. Mahmud, M. S. Kaiser, A. Hussain, and S. Vassanelli, “Applications of Deep Learning and Reinforcement Learning to Biological Data,” IEEE Trans Neural Netw Learn Syst, vol. 29, no. 6, pp. 2063–2079, Jun. 2018, doi: 10.1109/TNNLS.2018.2790388.
Z. Ge, S. Liu, F. Wang, Z. Li, and J. Sun, “YOLOX: Exceeding YOLO Series in 2021 V100 batch 1 Latency (ms) YOLOX-L YOLOv5-L YOLOX-DarkNet53 YOLOv5-Darknet53 EfficientDet5 COCO AP (%) Number of parameters (M) Figure 1: Speed-accuracy trade-off of accurate models (top) and Size-accuracy curve of lite models on mobile devices (bottom) for YOLOX and other state-of-the-art object detectors.” [Online]. Available: https://github.com/ultralytics/yolov3
S. Xu et al., “PP-YOLOE: An evolved version of YOLO,” Dec. 2022, [Online]. Available: http://arxiv.org/abs/2203.16250
M. Hussain, “YOLOv1 to v8: Unveiling Each Variant-A Comprehensive Review of YOLO,” IEEE Access, vol. 12, pp. 42816–42833, 2024, doi: 10.1109/ACCESS.2024.3378568.
W. Fang, L. Wang, and P. Ren, “Tinier-YOLO: A Real-Time Object Detection Method for Constrained Environments,” IEEE Access, vol. 8, pp. 1935–1944, 2020, doi: 10.1109/ACCESS.2019.2961959.
P. Jiang, D. Ergu, F. Liu, Y. Cai, and B. Ma, “A Review of Yolo Algorithm Developments,” in Procedia Computer Science, Elsevier B.V., 2021, pp. 1066–1073. doi: 10.1016/j.procs.2022.01.135.
X. Chen, C. Liu, S. Wang, and X. Deng, “LSI-YOLOv8: An Improved Rapid and High Accuracy Landslide Identification Model Based on YOLOv8 From Remote Sensing Images,” IEEE Access, vol. 12, pp. 97739–97751, 2024, doi: 10.1109/ACCESS.2024.3426040.
H. Liu, R. Hu, H. Dong, and Z. Liu, “SFC-YOLOv8: Enhanced Strip Steel Surface Defect Detection Using Spatial-Frequency Domain-Optimized YOLOv8,” IEEE Trans Instrum Meas, vol. 74, 2025, doi: 10.1109/TIM.2025.3548193.
V. V. Kukartsev, R. A. Ageev, A. S. Borodulin, A. P. Gantimurov, and I. I. Kleshko, “Deep Learning for Object Detection in Images Development and Evaluation of the YOLOv8 Model Using Ultralytics and Roboflow Libraries,” in Lecture Notes in Networks and Systems, Springer Science and Business Media Deutschland GmbH, 2024, pp. 629–637. doi: 10.1007/978-3-031-70285-3_48.
G. A. Pereira, “Fall Detection for Industrial Setups Using YOLOv8 Variants,” Aug. 2024, [Online]. Available: http://arxiv.org/abs/2408.04605
P. K. Pativada, “Real-time detection and classification of plant seeds using YOLOv8 object detection model,” 2024.
Z. Zhang et al., “An Improved YOLOv8n Used for Fish Detection in Natural Water Environments,” Animals, vol. 14, no. 14, Jul. 2024, doi: 10.3390/ani14142022.
S. Guan and S. Lv, “MCG-YOLOv8n: An Enhanced YOLOv8n Model for Segmentation of Adherent Buckwheat Seeds,” IEEE Access, vol. 12, pp. 131968–131981, 2024, doi: 10.1109/ACCESS.2024.3460174.
Y. Liu, Q. Ye, L. Sun, and Z. Wu, “SOD-YOLOv8n: Small Object Detection in Remote Sensing Images Based on YOLOv8n,” IEEE Geoscience and Remote Sensing Letters, vol. 22, 2025, doi: 10.1109/LGRS.2025.3567362.
Q. Liu et al., “DSW-YOLOv8n: A New Underwater Target Detection Algorithm Based on Improved YOLOv8n,” Electronics (Switzerland), vol. 12, no. 18, Sep. 2023, doi: 10.3390/electronics12183892.
L. Chen, G. Li, S. Zhang, W. Mao, and M. Zhang, “YOLO-SAG: An improved wildlife object detection algorithm based on YOLOv8n,” Ecol Inform, vol. 83, Nov. 2024, doi: 10.1016/j.ecoinf.2024.102791.
P. Masci and C. A. Muñoz, “An integrated development environment for the prototype verification system,” in Electronic Proceedings in Theoretical Computer Science, EPTCS, Open Publishing Association, Dec. 2019, pp. 35–49. doi: 10.4204/EPTCS.310.5.
T. S. Gunawan et al., “Development of video-based emotion recognition using deep learning with Google Colab,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 18, no. 5, pp. 2463–2471, Oct. 2020, doi: 10.12928/TELKOMNIKA.v18i5.16717.
K. R. M. Leino and V. Wüstholz, “The Dafny integrated development environment,” in Electronic Proceedings in Theoretical Computer Science, EPTCS, Open Publishing Association, Apr. 2014, pp. 3–15. doi: 10.4204/EPTCS.149.2.
P. K. Pativada, “Real-time detection and classification of plant seeds using YOLOv8 object detection model,” 2024.
Q. Liu et al., “DSW-YOLOv8n: A New Underwater Target Detection Algorithm Based on Improved YOLOv8n,” Electronics (Switzerland), vol. 12, no. 18, Sep. 2023, doi: 10.3390/electronics12183892.
Y. Fan, L. Zhang, and P. Li, “A Lightweight Model of Underwater Object Detection Based on YOLOv8n for an Edge Computing Platform,” J Mar Sci Eng, vol. 12, no. 5, May 2024, doi: 10.3390/jmse12050697.
B. Zhao, Q. Zhang, Y. Liu, Y. Cui, and B. Zhou, “Detection Method for Rice Seedling Planting Conditions Based on Image Processing and an Improved YOLOv8n Model,” Applied Sciences (Switzerland), vol. 14, no. 6, Mar. 2024, doi: 10.3390/app14062575.
M. Zhu, “Recall, Precision and Average Precision,” 2004.
L. Torgo and R. Ribeiro, “Precision and recall for regression,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009, pp. 332–346. doi: 10.1007/978-3-642-04747-3_26.
N. Ma, Y. Wu, Y. Bo, and H. Yan, “Chili Pepper Object Detection Method Based on Improved YOLOv8n,” Plants, vol. 13, no. 17, Sep. 2024, doi: 10.3390/plants13172402.
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