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Artificial Intelligence Answers The Call For Quail Information

Artificial Intelligence
Artificial Intelligence :

The technique can be laborious, expensive, and time-consuming when states desire to estimate quail numbers. Hours must be spent in the field waiting for calls. Or leaving a recording equipment in the field to capture any sounds heard, only to listen to the audio for hours at a time later. Repeat this procedure until there is sufficient data to begin estimating the population.

But a brand-new model created by University of Georgia researchers promises to speed up this procedure. The method enables wildlife managers to quickly acquire the information they require by utilising artificial intelligence to search through terabytes of recordings for quail sounds.

Working on the project with the Georgia Department of Natural Resources for around five years is James Martin, an associate professor at the UGA Warnell School of Forestry and Natural Resources.

“The model is very accurate, picking up between 80% and 100% of all calls even in the noisiest recordings. So, you could take a recording, put it through our model and it will tell you how many quail calls that the recorder heard”, “This new model allows you to analyze terabytes of data in seconds, and what that will allow us to do is scale up monitoring, so you can literally put hundreds of these devices out and cover a lot more area and do so with a lot less effort than in the past.”

James Martin, an associate professor at the UGA Warnell School of Forestry and Natural Resources

The model was developed over the course of around five years by Martin, postdoctoral researcher Victoria Nolan, and numerous other major contributors, who collaborated with a code writer. It also fits into a bigger trend in wildlife research, where computer algorithms are now helping with tasks that used to take humans countless hours to perform.

Computers are becoming more adept at distinguishing particular sounds or characteristics in images and sound recordings, for instance. It means hours previously spent on chores like listening to music or looking at game camera footage can now be completed by a computer, freeing up crucial time to concentrate on other areas of a project for researchers like Martin.

State and federal agencies that are looking for data on their quail populations but have a tight budget for any one project may find the new tool to be a useful resource as well. Therefore, Martin continued, “I think this is something states might seize on in terms of replacing their current surveillance with acoustic recording equipment.”

The Journal of Remote Sensing in Ecology and Conservation has reported on the effectiveness of the software.

Martin claimed that the software becomes increasingly “smarter” as it is used more frequently and exposed to sounds from many different locations. Quail currently have a wide variety of calls. However, he said that when the programme is exposed to a range of sounds other than quail, it becomes more adept at differentiating the right calls from the background noise of the nearby grasses and trees.

The software will become more discriminating over time. Therefore, he continued, “you must constantly providing it with training data, and when you switch geographies, you encounter new sounds for which you did not train the model. It always comes down to adaptation.

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