A recent study by researchers at the University of Maine suggests that artificial intelligence could be a cost-effective and energy-efficient tool to monitor and manage Maine’s forests.
Researchers from the university, in collaboration with the University of Vermont, used the University of Maine’s Wireless Sensor Networks laboratory to devise a way that artificial intelligence and machine learning could be used to assist soil moisture monitoring practices.
Monitoring soil conditions can typically be a time-consuming job, with soil conditions changing on a daily, weekly and monthly basis. Forest management practices have relied on expensive monitoring systems in the past, but the systems were not adequate to assess soil quality on a large scale, according to Aaron Weiskittel, the director of the Center for Research on Sustainable Forests.
However, soil monitoring is key to maintaining healthy forests, as well as anticipating the impact of weather events, such as droughts or sustained periods of heavy rain.
The software that the university researchers tested can learn to react to environmental and network conditions, and report only the data points that are most necessary to generating meaningful information about forest health. This allows the network to use less energy and collect more pertinent data than older systems, according to researchers.
This ability to react and create more efficient data-monitoring conditions can significantly reduce the cost of using a monitoring system across large areas, and can increase sustainability practices.
“AI can learn from the environment, predict the wireless link quality and incoming solar energy to efficiently use limited energy and make a robust low cost network run longer and more reliably,” said Ali Abedi, principal investigator of the recent study and professor of electrical and computer engineering at the University of Maine.
The researchers also determined that the artificial intelligence systems could be adapted for use to monitor other key environmental markers, such as ambient temperature and snowfall.