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JACIII Vol.26 No.6 pp. 930-936
doi: 10.20965/jaciii.2022.p0930
(2022)

Paper:

Detection of Japanese Quails (Coturnix japonica) in Poultry Farms Using YOLOv5 and Detectron2 Faster R-CNN

Ivan Roy S. Evangelista*,†, Lenmar T. Catajay**, Maria Gemel B. Palconit*, Mary Grace Ann C. Bautista*, Ronnie S. Concepcion II***, Edwin Sybingco*, Argel A. Bandala*, and Elmer P. Dadios***

*Department of Electronics and Computer Engineering, De La Salle University
2401 Taft Avenue, Malate, Manila 1004, Philippines

**Computer Engineering Department, Sultan Kudarat State University
E.J.C. Montilla, Isulan, Sultan Kudarat 9805, Philippines

***Department of Manufacturing and Management Engineering, De La Salle University
2401 Taft Avenue, Malate, Manila 1004, Philippines

Corresponding author

Received:
April 10, 2022
Accepted:
June 15, 2022
Published:
November 20, 2022
Keywords:
deep learning, Detectron2, precision livestock farming, smart poultry farming, YOLOv5
Abstract
Detection of Japanese Quails (<i>Coturnix japonica</i>) in Poultry Farms Using YOLOv5 and Detectron2 Faster R-CNN

Vision-based monitoring of Quails

Poultry, like quails, is sensitive to stressful environments. Too much stress can adversely affect birds’ health, causing meat quality, egg production, and reproduction to degrade. Posture and behavioral activities can be indicators of poultry wellness and health condition. Animal welfare is one of the aims of precision livestock farming. Computer vision, with its real-time, non-invasive, and accurate monitoring capability, and its ability to obtain a myriad of information, is best for livestock monitoring. This paper introduces a quail detection mechanism based on computer vision and deep learning using YOLOv5 and Detectron2 (Faster R-CNN) models. An RGB camera installed 3 ft above the quail cages was used for video recording. The annotation was done in MATLAB video labeler using the temporal interpolator algorithm. 898 ground truth images were extracted from the annotated videos. Augmentation of images by change of orientation, noise addition, manipulating hue, saturation, and brightness was performed in Roboflow. Training, validation, and testing of the models were done in Google Colab. The YOLOv5 and Detectron2 reached average precision (AP) of 85.07 and 67.15, respectively. Both models performed satisfactorily in detecting quails in different backgrounds and lighting conditions.

Cite this article as:
I. Evangelista, L. Catajay, M. Palconit, M. Bautista, R. II, E. Sybingco, A. Bandala, and E. Dadios, “Detection of Japanese Quails (Coturnix japonica) in Poultry Farms Using YOLOv5 and Detectron2 Faster R-CNN,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.6, pp. 930-936, 2022.
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Last updated on Dec. 01, 2022