Monitoring and measuring forest ecosystems is a complex challenge due to an existing combination of software, collection systems and computing environments that increasingly require more energy to be powered. The University of Maine’s Wireless Sensor Networks (WiSe-Net) lab has developed a new method of using artificial intelligence and machine learning to make soil moisture monitoring more energy and cost efficient. forest ecosystems of Maine and beyond.
Soil moisture is an important variable in forested and agricultural ecosystems, especially under the recent droughts of recent Maine summers. Despite robust soil moisture monitoring networks and large, freely available databases, the cost of commercial soil moisture sensors and the power they use to operate can be prohibitive to researchers, forest rangers, farmers and others monitoring the health of the land.
Together with researchers from the University of New Hampshire and the University of Vermont, UMaine’s WiSe-Net designed a wireless sensor network that uses artificial intelligence to learn how to be more energy efficient when monitoring soil moisture and processing the data. The research was funded by a grant from the National Science Foundation.
“AI can learn from the environment, predict the quality of the wireless connection and the incoming solar energy to make efficient use of limited energy and make a robust, low-cost network run longer and more reliably,” said Ali Abedi, principal investigator of the study. recent study and professor of electrical and computer engineering at the University of Maine.
The software learns over time how to best use the available network resources, helping to produce energy efficient systems at a lower cost for large scale monitoring compared to existing industry standards.
WiSe-Net also worked with Aaron Weiskittel, director of the Center for Research on Sustainable Forests, to ensure that all hardware and software research is informed by science and aligned with research needs.
“Soil moisture is a primary driver for tree growth, but it changes rapidly, both daily and seasonally,” Weiskittel says. “We weren’t able to monitor effectively at scale. We used to use expensive sensors that collected at fixed intervals – say, every minute – but were not very reliable. A cheaper and more robust sensor with wireless capabilities like this opens the door for future applications for researchers and practitioners alike.”
The study was published in Springer’s on August 9, 2022 International Journal of Wireless Information Networks.
While the system designed by the researchers focuses on soil moisture, the same methodology could be extended to other types of sensors, such as ambient temperature, snow depth and more, as well as scaling the networks with more sensor nodes.
“Real-time monitoring of different variables requires different sampling rates and power levels. An AI agent can learn these and adjust the data collection and transmission rate accordingly instead of sampling and transmitting each individual data point, which is not as efficient,” Abedi says.
Materials supplied by University of Maine. Note: Content is editable for style and length.