Saturday, December 23, 2017

Artificial Intelligence being used to study chicken communication

Researchers are using machine learning technology to understand and analyze patterns in chicken speech. The scientists hope to better inform farmers about the health and happiness of their poultry.

New technologies continue to revolutionize the business of farming — from self-driving tractors to IoT sensors monitoring crops. However, scientists at the University of Georgia University and Georgia Institute of Technology are taking a different approach to monitoring the health of farm animals.
Over the last five years, both engineers and poultry scientists have been studying chicken "language" at the two institutions hoping to be able to better use the information gleaned from their studies to help poultry farmers such as Mitchell.
Chickens are chatty creatures
Kevin Mitchell of Wilcox Farms, with properties in both Washington State and Oregon, notes that chickens make sounds that exhibit "patterns of speech" that reveal a considerable amount of information about their well-being.
Mitchell says chickens usually make the most noise in the morning with many clucks, chortles, and caws. When he hears these sounds, Mitchell claims he knows they are reasonably healthy and happy.
In the evening when they’re preparing to roost Mitchell claims, the chickens are much more mellow, cooing softly.
When a hen lays an egg it issues a series of clucks, like drumbeats that culminate in a loud "buck-caw" as heard on one of the appended videos.
When chickens detect an aerial predator approaching they produce a short, high-pitched shriek as also heard on one of the appended videos.
The repetitive clucking often heard when you approach chickens is in fact an alarm call as well indicating a predator may be approaching on the ground.
Studies at the University of Georgia and Georgia Institute of Technology
Georgia Tech research engineer Wayne Daley and his colleagues studied the effects on 6 to 12 broiler chickens of moderately stressful conditions and recorded their vocalizations. The conditions included increased ammonia levels in the air, minor infections and higher temperatures.
The resulting audio data was fed into an AI learning program that enabled it to learn the difference between contented and distressed birds through their vocalizations.
What the AI has learned
The program can now detect when birds are uncomfortable due to heat stress on the basis of the sounds they are making.
It can also detect when the birds have a respiratory infection because of the sound they make when mucus clogs their airways.
Daley notes: “A lot of poultry farmers we have worked with say they can hear when something is wrong with a flock, but they can’t tell us exactly how they know that. There’s a lot of subtlety. We’re learning that there are changes in the frequency of the sounds and the levels—the amplitude or loudness—that the machines can pick up on.”
Chicken "language" may be more complex than previously thought
Carolynn Smith a biologist at Macquarie University in Australia, a leading expert on chicken vocalizations, together with other scientists have discovered that chicken communications appear to be more complicated and nuanced than previously thought.
For example, roosters do not automatically shriek every time they see an aerial predator. After all, the shriek makes them more vulnerable in that the predator can hear it. If there are females around they usually do sound the alarm. However, if they are alone or surrounded by other males they often stay quiet. They also shriek more often when there is cover nearby where they can be safe.
Technology in commercial chicken farms a challenge for AI
One of the problems facing researchers is that large scale commercial chicken farms are very noisy as they have large fans meant to circulate air as well as large heater fans.
Given the background noise it becomes difficult for the AI software to simply concentrate on the sounds of the chickens. The challenge will be to have the software filter out the loud background noises.
When this is solved, the AI software can simply be added to the existing advanced technology which includes the ability to monitor and modify lighting, temperature, and ventilation. The farmer can also activate automatic feeding systems from their phones or computers. The audio AI data would just be one more tool to monitor the conditions of the fowl.


Previously published in Digital Journal

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