The broader impact / commercial potential of this Partnerships for Innovation-Technology Translation (PFI-TT) project is to enhance the well-being and productivity of commercially raised chickens in our food supply chain. The poultry industry is highly competitive, and poultry has become one of the most efficient animal proteins to produce. Billions of birds are grown in the United States each year and most are raised by independent farmers in cooperation with larger producers. This PFI project is aimed at providing non-invasive, continuous monitoring of chicken flocks to alert the farmer to problems in a timely manner and aid the farmer in assessing the welfare and needs of his flocks. The end result is expected to be healthier and better-cared for birds as well as improved farm productivity.

The proposed project makes it possible to use machine learning to monitor audio environments, in this case chicken farms, without needing extensive labeled training data. The approaches typically used for audio machine learning require extensive labeled training sample sets and tuning. However, humans can become familiar with the sounds in a particular situation, and notice when something occurs that does not belong, without significant training. By using algorithms that learn the structure of sounds in an environment and learn the typical variation of those sounds, the methods used in the proposed project can act similarly to a human listener to notice significant changes (anomalies) in a soundscape on a farm. The anomalies are characterized by severity and can be brought to the attention of a farmer or researchers who can choose to label or ignore the new sound. Labeled sounds can then be used to automatically label future anomalies. This project will deploy listening devices into multiple commercial chicken farms, collect continuous audio recordings and metadata, and use the collected data to improve the performance and usefulness of the systems for monitoring the chickens' welfare and their environment. This listening method is non-invasive and causes no stress to the animals.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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Georgia Tech Research Corporation
United States
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