United KingdomFood and restaurants2024 – ongoing2 Sisters Food Group
Machine-learning poultry growth and welfare monitoring
2 Sisters used imaging and machine learning to measure 40,000 birds by weight every hour
Problem
Growers needed earlier signs of health and environmental problems in poultry sheds. Routine observation could miss changes in individual birds or differences between shed zones.
Approach
2 Sisters used cameras and machine learning to track bird growth and behaviour across shed zones. Its agriculture director reported hourly weight measurements covering 40,000 birds. Staff used the monitoring information to anticipate problems and adjust management or environmental conditions.
Results
Takeaway
The monitoring supported husbandry decisions by detecting changes within sheds. No quantified reduction in disease or production costs was disclosed.
Categories
- Size
- Large enterprise
- Industry
- Food and restaurants
- Business function
- Production and quality
- Innovation type
- Process innovation
- AI technology
- Computer visionPredictive analytics and ML
- Adoption
- Vendor solution
- Evidence
- Company's own claim
Sources
Source summaries
Meeting notes record agriculture director Nick Davies describing operational camera and machine-learning monitoring at 2 Sisters. He reported hourly weight measurements of 40,000 birds and explained how the information supported management changes.
Full details are in the original sources.
