Enhancing Poultry Welfare with Chicken Pose Detection on Matrice

Chicken Pose Detection

Understanding animal behavior is critical in modern poultry farming. Monitoring specific chicken poses—such as eating, drinking, moving, and resting—provides valuable insights into health, welfare, and environmental conditions.

Matrice makes it easy to deploy AI models for pose detection with no code. Using YOLOv8, YOLOv9 and Matrice’s powerful interface, we built a system to automatically detect and classify chicken behaviors in real time.

This blog explains how we built the chicken pose detection system using Matrice:

  1. Dataset Preparation

  2. Dataset Annotation

  3. Model Training

  4. Model Inference

  5. Model Deployment

Dataset Preparation

The dataset contains annotated images of chickens in different poses like:

  • Eat/Drink

  • Moving

  • Resting

  • Aggressive/Alert

  • Total Images: 1,711

  • Training Set: 1,346 images

  • Validation Set: 188 images

  • Test Set: 177 images

Each image is annotated with bounding boxes and pose labels.

Chicken-pose-dataset-summary Dataset-Preview

Model Training

We used YOLOv8 for its real-time detection capability and high performance on livestock datasets.

  • Batch Size: 16

  • Epochs: 50

  • Learning Rate: 0.001

  • Optimizer: Auto

  • Momentum: 0.95

  • Weight Decay: 0.0005

The model achieved high accuracy in detecting fine-grained behavior patterns.

Chicken-pose-training Chicken-inference-preview

Model Inference

The trained model supports fast inference and can be exported in formats like:

  • PyTorch (.pt)

  • ONNX

  • TensorRT

  • OpenVINO

This enables flexible deployment in both cloud and edge devices such as smart cameras in poultry farms.

Model Deployment

Matrice enables seamless deployment with its no-code AI builder. Farmers or technicians can:

  • Monitor chicken behavior in real time

  • Receive alerts for abnormal patterns

  • Visualize data on an interactive dashboard

Key Applications:

  • Welfare monitoring in broiler or layer farms

  • Early detection of stress or illness

  • Smart feeding and lighting systems

Conclusion

With chicken pose detection using AI, poultry farms can improve animal health, reduce mortality, and optimize operations. Powered by Matrice, deploying such models becomes effortless—bringing precision livestock farming to reality.

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