A New Research On Monitoring Forests Using Artificial Intelligence

AI in Forests

Researchers have developed a revolutionary way of monitoring soil moisture that uses artificial intelligence and machine learning to save energy and money. When compared to existing industry standards, the software learns how to make the most use of available network resources over time, resulting in more power-efficient systems at a reduced cost for large-scale monitoring.

Springer’s International Journal of Wireless Information Networks published this report on August 9, 2022. The National Science Foundation provided funding for the study. This article’s main application is the accurate monitoring of forest ecosystems with great spatio-temporal resolution.

The reason for selecting AI as a solution

The existing software, collecting data, and computational infrastructures demand more electricity to power, monitoring and assessing forest ecosystems and thus makes a challenging task. The University of Maine’s Wireless Sensor Networks (WiSe-Net) laboratory has developed a novel method for using AI and machine learning to make soil moisture monitoring more energy and cost-efficient. A method that could be used to make measuring more efficient across Maine’s broad forest ecosystems.

Soil moisture is an essential component in forests and agricultural ecosystems alike, especially under the severely dry conditions of prior Maine summers. Despite powerful soil moisture monitoring networks and large, freely available databases, the cost of commercial soil moisture sensors and the power required to run them can be prohibitively expensive for researchers, foresters, farmers, and others monitoring land health.

UMaine’s WiSe-Net team collaborated with academics from the University of New Hampshire and the University of Vermont to create a wireless sensor network that utilises artificial intelligence to learn how to monitor soil moisture and process data more efficiently.

Researchers’ Explanation of this Technology

Ali Abedi is the study’s principal investigator and professor of electrical and computer engineering at the University of Maine. He says that AI could learn from the environment, predict wireless link quality and incoming solar energy to efficiently use limited energy and make a robust low-cost network run longer and more reliably.

WiSe-Net also worked with Aaron Weiskittel, director of the Center for Research on Sustainable Forests, to guarantee that all hardware and software research is science-based and adapted to the demands of the research.

Weiskittel says that soil moisture is a fundamental factor in tree development, but it varies swiftly, both daily and annually. They lacked the capacity to adequately monitor at scale. Previously, they utilised expensive sensors that gathered data at predetermined intervals – every minute, for example – but were unreliable. A less expensive and more durable sensor with wireless capabilities, such as this one, paves the way for future applications for both researchers and practitioners.

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

Abedi explains that real-time monitoring of different variables demands varying sample rates and power levels. An AI agent may learn these and adapt the data collection and transmission frequency accordingly.

This technology revolutionizes the way government keep a track on the forests. Advancing this technology may greatly help in the agricultural sector. It would be beneficial for the farmers to assess the soil moisture for the lands in the dry area.

Reference Articles

Resources provided by University of Maine. Note: Content may be edited for style and length.

Sonia Naderi – Sharing Wireless Spectrum in the Forest Ecosystems Using Artificial Intelligence and Machine LearningInternational Journal of Wireless Information Networks, 2022; 29 (3): 257 DOI: 10.1007/s10776-022-00572-9

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