Monitoring and measuring forest ecosystems are complex challenges because software, collection systems and computing environments require increasing amounts of energy. Now, the University of Maine's Wireless Sensor Networks laboratory, or WiSe-Net, has developed a novel method of using artificial intelligence and machine learning to monitor soil moisture with less energy and cost. The method could be used to increase the efficiency of measurements in the forest ecosystems of Maine and beyond. Soil moisture is an important variable in forested and agricultural ecosystems, particularly in the recent drought conditions of Maine summers. Despite robust soil moisture monitoring networks and large, freely available databases, the cost of commercial soil moisture sensors and the power they consume can be prohibitive for researchers, foresters, farmers, and others tracking the health of the land.

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 artificial intelligence to learn how to be more power efficient in 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 wireless link quality and incoming solar energy to efficiently use limited energy and make a robust low cost network run longer and more reliably," says Ali Abedi, principal investigator of the recent study and professor of electrical and computer engineering at the University of Maine.