AI trained to identify and count chickens’ distress calls

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Farmers can now improve conditions for chickens raised on crowded commercial farms. This is possible through an AI that has been trained to identify and count chickens’ distress calls. Hundreds of thousands of chickens live in poor conditions, packed together with little ability to move around.

Alan McElligott, a researcher at the City University of Hong Kong, says despite not being kept hungry or thirsty, there are serious welfare concerns about how chickens are produced. His earlier study highlighted that the frequency and volume of a chicken’s distress call, which is sharp, short, and cheep, can predict the bird’s health and growth rate.

McElligott said such calls can be difficult to identify when there are thousands of chickens cheeping together. The researcher with his team listened to recordings made at large broiler chicken farms in southwestern China. They labeled chicken distress calls, and distinguished them from farm sounds and other chicken sounds that signify fear. With the collected data, the researchers trained several algorithms to identify distress calls from the background noise and measure their frequency and volume.

As such, the best algorithm accurately detected distress calls around 85% of the time. However, this AI hasn’t been deployed on a working chicken farm. McElligott said there’s more work to be done to understand the link between distress calls and a chicken’s well-being. In regards to the next phase of the research, the expert outlined that they need to give the chickens conditions in which they would produce fewer distress calls. This means giving the chickens more space or other enrichments, such as giving them bales of straw to climb on.

Elodie Floriane Mandel-Briefer, a scientist at the University of Copenhagen in Denmark, has developed similar tools to decode the emotions of pigs. She has based it on the sounds and facial expressions of the animal. Elodie says there’s growing evidence that animal emotions can be measured and monitored via machine learning. She believes this kind of assessment is crucial.

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