An Energy-Efficient Method to Monitor Soil Moisture

University of Maine, Orno, ME

WiSe-Net could be used to make measuring more efficient across the broad forest ecosystems of Maine and beyond. (Image: University of Maine)

Monitoring and measuring forest ecosystems is a complex challenge because of an existing combination of software, collection systems, and computing environments that require increasing amounts of energy to power. The University of Maine’s Wireless Sensor Networks (WiSe-Net) laboratory has developed a novel method of using artificial intelligence (AI) and machine learning to make monitoring soil moisture more energy and cost efficient — one that could be used to make measuring more efficient across the broad forest ecosystems of Maine and beyond.

Soil moisture is an important variable in forested and agricultural ecosystems alike, particularly under the recent drought conditions of past Maine summers. Despite the robust soil moisture monitoring networks and large, freely available databases, the cost of commercial soil moisture sensors and the power that they use to run can be prohibitive for researchers, foresters, farmers, and others tracking the health of the land.

Along with researchers at the University of New Hampshire and University of Vermont, UMaine’s WiSe-Net designed a wireless sensor network that uses AI to learn how to be more power efficient in monitoring soil moisture and processing the data.

“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, Professor of Electrical and Computer engineering at the University of Maine.

The software learns over time how to make the best use of available network resources, which helps produce power-efficient systems at a lower cost for large-scale monitoring compared to the existing industry standards.

Although the system designed by the researchers focuses on soil moisture, the same methodology could be extended to other types of sensors, like ambient temperature, snow depth and more, as well as scaling up the networks with more sensor nodes.

For more information, contact Sam Schipani, This email address is being protected from spambots. You need JavaScript enabled to view it.; 207-581-3743.