Welfare & Management

Detecting broiler chickens at different ages with advanced deep learning models

To read more content about AviNews March 2024


  • About 20,000-30,000 birds are raised in commercial broiler houses in the US today, and it has caused growing public concerns about animal welfare.
  • Daily evaluation of broiler well-being and growth, which is labor intensive and subject to human errors, is conducted manually. Therefore, there is a need for an automatic tool to detect and analyze the behaviors of chickens and predict their welfare status.
  • Deep learning technology has powerful feature representation capabilities, fast processing speed, and can resolve problems associated with external interferences.
  • Thus, deep learning algorithms are appropriate models for developing an automatic, efficient, intelligent tool for precision animal farming.
  • However, the size of the chicken and the sheer numbers raised in a single house pose challenges in applying deep learning techniques in monitoring individual chickens.
  • In the current study, we integrated the convolutional block attention module (CBAM) into YOLOv5 to enhance the algorithm’s ability to extract image features.


  • This study was conducted in an experimental broiler house at the Poultry Research Center of the University of Georgia, Athens, USA.
  • High-definition cameras (PRO-1080MSFB, Swann Communications, Santa Fe Springs, CA) were mounted on the ceiling (2.5 m above floor) to capture video (15 frame/s, 1440 pixels × 1080 pixels).
  • Two different litter types (fresh pine shavings and reused litter previously used to raise three flocks of broilers) were selected as application scenes for broiler detection. For the two litter scenes, 70 images were selected from d2, d9, d16, and d23, respectively, for 560 images.


Figure 1. Examples of broiler images from different scenes. a. Broiler images from reused litter floor .b. Broiler images from fresh pine shavings floor.c. Broiler images from multiple pens floor.

In addition, to evaluate the detection performance of the model under multiple pens scenes, the image samples shown in Fig.1c were constructed, in which 70 images were selected for d16 and d23.

  • Finally, 700 images were obtained and randomly divided into training and testing sets at a ratio of 5:2.
  • Figure 1 shows examples of broiler images from different scenes.


Figures 2 and 3 show the detection results of broilers with YOLOv5 and YOLOv5-CBAM on fresh pine shaving and reused litter floors, respectively.

The precision, recall, F1, and [email protected] of YOLOv5- CBAM were 97.3%, 92.3%, 94.7%, and 96.5%, which was higher than that of YOLOv5 (96.6%, 92.1%, 94.3% and 96.3%), Faster R-CNN (79.7%, 95.4%, 86.8% and 90.6%) and SSD (60.8%, 94.0%, 73.8% and 88.5%).

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The results show that the overall performance of the proposed YOLOv5-CBAM was the best.



Fig. 3. Detection results using YOLOv5 and YOLOv5-CBAM in reused litter. (a) Birds at day 2. (b) Birds at day 9. (c) Birds at day 16. (d) Birds at day 23.


Further reading: Guo, Y., S. E. Aggrey, X. Yang, A. Oladeinde, Y. Qiao, L. Chai. (2023) Detecting broiler chickens on litter floor with the YOLOv5- CBAM deep learning model. Artificial Intelligence in Agriculture,9: 36-45. https://doi.org/10.1016/j.aiia.2023.08.002.




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