Agricultural Data from Acoustic Monitoring (ADAM)

The motivation for this project is the critical need to improve general farming practices in Rwanda and elsewhere in East Africa. There are many possible approaches to improving farm practices, but blindly applying practices without knowing whether they will be useful can be very inefficient. Successful farmers understand the problems they face by simple observation. One-way farmers can understand the status of a crop or herd is by listening. However, just as with visual observation, a farmer cannot always listen everywhere on their farm. This project proposes to build passive acoustic listening networks for use on farms to give farmers "ears" throughout their lands and in their buildings, 24 hours per day. The focus of this project then is to build low-cost acoustic sensing networks and to create machine learning techniques for processing acoustic data into information.