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Developing an automatic warning system for anomalous chicken dispersion and movement using deep learning and machine

Bo-Lin Chen1, Ting-Hui Cheng1, Yi-Che Huang1

  • 1Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan.

Poultry Science
|September 28, 2023
PubMed
Summary
This summary is machine-generated.

This study developed an automated system to monitor chicken flock dispersion and movement, improving farm health surveillance. The system uses AI to detect anomalies, saving labor and reducing disease spread risks.

Keywords:
Convolutional neural network (CNN)Embedded systemSimple online and real-time tracking (SORT)Taiwanese native chickens (TNCs)You only look once (YOLO)

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Area of Science:

  • Agricultural technology
  • Animal behavior analysis
  • Computer vision in agriculture

Background:

  • Traditional chicken flock monitoring relies on manual patrols, which are labor-intensive and increase pathogen risks.
  • Chicken dispersion and movement are key indicators of flock health and welfare.
  • Taiwanese native chickens (TNCs) exhibit high physical activity when healthy, making movement analysis crucial.

Purpose of the Study:

  • To develop an automatic warning system for detecting anomalous dispersion and movement patterns in commercial chicken farms.
  • To reduce labor costs and minimize the risk of pathogen introduction associated with manual farm surveillance.
  • To provide farmers with timely alerts for potential health risks and environmental hazards affecting chicken flocks.

Main Methods:

  • Utilized embedded systems to capture overhead video footage of approximately 20,000 TNCs over 10 weeks.
  • Employed a You Only Look Once-version 7 tiny (YOLOv7-tiny) object detection model for accurate chicken identification.
  • Calculated flock dispersion using the nearest neighbor index (NNI) and movement using the simple online and real-time tracking (SORT) algorithm.
  • Established normal dispersion and movement ranges using autoregressive integrated moving average (ARIMA) and SARIMAX models for anomaly detection.

Main Results:

  • The YOLOv7-tiny model achieved 98.2% precision in chicken detection.
  • The SORT algorithm demonstrated a 95.3% multiple object tracking accuracy.
  • ARIMA and SARIMAX models showed low forecasting errors (3.71% and 13.39% MAPE, respectively) for dispersion and movement.
  • The system successfully identified deviations from normal dispersion and movement patterns.

Conclusions:

  • The proposed automated system effectively monitors chicken flock behavior, offering a significant improvement over manual methods.
  • This technology enhances farm biosecurity by reducing human traffic and potential pathogen introduction.
  • The system provides a valuable tool for early detection of health issues and environmental concerns in large-scale chicken farming.