Monitoring forest ecosystems is highly challenging, since it generally needs a combination of software, collection systems, and computing environments which require increasing amounts of energy to function properly. Now, a research team from the University of Maine’s Wireless Sensor Networks (WiSe-Net) Laboratory has developed an innovative method of using artificial intelligence and machine learning algorithms to make monitoring soil moisture more energy- and cost-efficient.
Soil moisture is an important variable in both forested and agricultural ecosystems, particularly under drought conditions, such as Maine experienced over the past summers. However, regardless of the robust soil moisture monitoring networks and the large, freely available databases, the high costs of using such technologies can be prohibited for researchers, farmers, or foresters investigating these issues.
By designing a wireless sensor network that uses artificial intelligence to learn how to be more energy efficient in monitoring soil moisture and processing the data, the scientists hope to significantly reduce the costs involved in such activities.
“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,” explained study co-author Adi Abedi, a professor of Electrical and Computer Engineering at the University of Maine.
“Soil moisture is a primary driver of tree growth, but it changes rapidly, both daily as well as seasonally,” added study co-author Aaron Weiskittel, the director of the Center for Research on Sustainable Forests at the University of Maine.
“We have lacked the ability to monitor effectively at scale. Historically, we used expensive sensors that collected at fixed intervals — every minute, for example — but were not very reliable. A cheaper and more robust sensor with wireless capabilities like this really opens the door for future applications for researchers and practitioners alike.”
Although this newly designed system focuses only on soil moisture, scientists hope that the same methodology could be extended to other types of sensors that could reliably measure features such as ambient temperature or snow depth.
The study is published in the International Journal of Wireless Information Networks.