Assessment of Preference Behavior of Layer Hens under Different Light Colors and Temperature Environments in...

Area of Science:

• Animal Behavior
• Computer Vision
• Agricultural Technology

Background:

• Chicken behavior is influenced by environmental conditions, particularly lighting.
• Conventional video analysis for chicken behavior is labor-intensive and limited by low light and low contrast.
• Existing methods struggle with low-quality video footage common in production settings.

Purpose of the Study:

• To develop an automated system for monitoring chicken behavior using computer vision in low-light conditions.
• To accurately detect and track chickens in low-quality video footage.
• To analyze chicken light preference and environmental interactions.

Main Methods:

• Utilized YOLO v4 architecture for advanced object detection to locate chickens in low-quality videos.
• Developed an automated system with cameras monitoring three environments with varying illumination.
• Processed over 648 hours of footage for automated bird counting and location tracking.

Main Results:

• Achieved 99.9% mean average precision for chicken detection, with 98.8% accuracy confirmed by manual inspection.
• Automated system successfully monitored bird presence and counted individuals across different light environments.
• Behavioral analysis indicated chickens prefer white light and avoid green light, except during heat stress.

Conclusions:

• Computer vision, specifically YOLO v4, is effective for monitoring chickens in low-light, low-resolution conditions.
• The automated system provides an efficient and accurate method for behavioral analysis in poultry.
• Findings offer insights into poultry welfare and environmental enrichment through light manipulation.