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Related Experiment Video

Updated: Jul 29, 2026

Windowing Chicken Eggs for Developmental Studies
15:01

Windowing Chicken Eggs for Developmental Studies

Published on: October 1, 2007

Enhanced Methodology and Experimental Research for Caged Chicken Counting Based on YOLOv8.

Zhenlong Wu1,2, Jikang Yang1,3, Hengyuan Zhang1,3

  • 1College of Engineering, South China Agricultural University, Guangzhou 510642, China.

Animals : an Open Access Journal From MDPI
|March 28, 2025
PubMed
Summary

Accurate chicken counting in poultry farms is improved by the You Only Look Once-Chicken Counting Algorithm (YOLO-CCA). This AI model enhances efficiency and reduces labor costs in large-scale farming operations.

Keywords:
chicken countinglayered cage farmingobject detectionpoultryprecision livestock farming

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Area of Science:

  • Agricultural Technology
  • Computer Vision
  • Artificial Intelligence

Background:

  • Manual chicken counting in large-scale poultry farms is labor-intensive, costly, and error-prone.
  • Existing deep learning models face challenges with accuracy in dense, occluded caged environments.

Purpose of the Study:

  • To develop an accurate and efficient automated chicken counting system for poultry farms.
  • To enhance existing deep learning models for improved performance in caged environments.

Main Methods:

  • Proposed the You Only Look Once-Chicken Counting Algorithm (YOLO-CCA), an enhanced YOLOv8-small model.
  • Integrated CoordAttention mechanism and Reversible Column Networks backbone into YOLOv8-small.
  • Developed a threshold-based continuous frame inspection method with cloud data storage.

Main Results:

  • YOLO-CCA achieved an F1 score of 96.7% and an average precision of 80.6%.
  • The chicken recognition rate reached 90.9% in a real-world poultry farming environment.
  • Optimized deployment on Jetson AGX Orin with TensorRT achieved 90.9 FPS detection speed.

Conclusions:

  • YOLO-CCA significantly improves chicken counting accuracy and efficiency in poultry farming.
  • The system reduces labor costs and supports the transformation towards intelligent agriculture.
  • The developed algorithm offers a robust solution for automated monitoring in challenging environments.