Visual Monitoring of Broiler Behaviour, Health and Welfare using Artificial Intelligent Image Machine Learning.

This project will monitor specific behaviours in the flock to improve our knowledge of intensively raised broiler chicken behaviours in relation to modern management techniques allowing for improved health, welfare and performance. 

24/7 monitoring of poultry environments with quantified visual observations aim to record which behaviours are being displayed by ratio at specified times and known conditions. This potentially enables early disease detection reducing mortality.

The environment required for optimum chicken growth is tightly controlled. Various elements are measured and managed constantly throughout the day. Small changes in these environmental elements cause significant changes to the chickens’ state of wellbeing. 

Observations of behaviours are a proven indicator of animal health and welfare. Chickens can suffer from a myriad of illnesses and ailments, these almost always display some sort of behavioural change.

Currently, farmers use environmental measures and visual observation to see any change in the birds. This relies on quality observations from the stockman. Reliance on human intervention is not a guaranteed method of effective management though.

Using camera systems and AI technology to analyse the images, selected broiler behaviours being displayed by how many birds at what time of day and in what conditions will be identified to assess overall wellbeing.  

A collaborative project involving Applied PoultryV7 Ltd, and the RVC.