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Behavior recognition of cage-free multi-broilers based on spatiotemporal feature learning.

Yilei Hu¹, Jiaqi Xiong¹, Jinyang Xu¹

¹College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, P. R. China; Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, P. R. China.

Poultry Science

|September 26, 2024

Summary

This summary is machine-generated.

This study introduces an automated system for recognizing multiple broiler behaviors in videos, improving poultry welfare and disease detection. The Broiler Behavior Recognition System (BBRS) uses spatiotemporal learning for accurate, real-time analysis.

Keywords:

behavior recognition broiler computer vision end-to-end spatiotemporal feature

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Area of Science:

Agricultural Science
Computer Vision
Animal Behavior

Background:

Poultry behavior is crucial for health, welfare, and production.
Existing multi-object behavior recognition methods often ignore temporal video features, leading to inaccuracies.
Timely behavioral data can enhance welfare and reduce disease spread in broilers.

Purpose of the Study:

To develop an end-to-end system for recognizing simultaneous behaviors of multiple cage-free broilers in videos.
To leverage spatiotemporal feature learning for improved accuracy in broiler behavior recognition.
To provide an efficient tool for automated, accurate analysis of broiler behavior.

Main Methods:

Proposed the Broiler Behavior Recognition System (BBRS), integrating an improved YOLOv8s detector, Bytetrack tracker, and a 3D-ResNet50-TSAM model.
Enhanced YOLOv8s with MPDIoU for multi-broiler identification and used Bytetrack for tracking individual broilers.
Employed a 3D-ResNet50-TSAM model with a temporal-spatial attention module to learn spatiotemporal features from video sequences.

Main Results:

The improved YOLOv8s detector achieved an mAP@0.5 of 99.50%.
The Bytetrack tracker obtained a mean MOTA of 93.89% under varying occlusion levels.
The 3D-ResNet50-TSAM model demonstrated high performance with Accuracy, Precision, Recall, and F1-score above 97.65%.

Conclusions:

The BBRS effectively recognizes multiple simultaneous broiler behaviors using spatiotemporal feature learning.
The system shows robust performance even with incomplete video sequences (26 frames), achieving 93.98% accuracy.
This research offers a valuable tool for automated and accurate monitoring of cage-free broiler behavior.