Machine Learning and Soil Humidity Sensing: Signal Strength Approach

• URL: http://arxiv.org/abs/2011.08273v1
• Date: Mon, 16 Nov 2020 21:00:36 GMT
• Title: Machine Learning and Soil Humidity Sensing: Signal Strength Approach
• Authors: Lea Duji\'c Rodi\'c, Tomislav \v{Z}upanovi\'c, Toni Perkovi\'c, and Petar \v{S}oli\'c (Corresponding Author, University of Split, Croatia), Joel J. P. C. Rodrigues (Federal University of Piau\'i (UFPI), Teresina - PI, Brazil and Instituto de Telecomunica\c{c}\~oes, Portugal)
• Abstract summary: Existing solutions are based on data received from the power hungry/expensive sensors that are transmitting the sensed data over the wireless channel. This work explores a concept of a novel, low-power, LoRa-based, cost-effective system which achieves humidity sensing using Deep learning techniques.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The IoT vision of ubiquitous and pervasive computing gives rise to future smart irrigation systems comprising physical and digital world. Smart irrigation ecosystem combined with Machine Learning can provide solutions that successfully solve the soil humidity sensing task in order to ensure optimal water usage. Existing solutions are based on data received from the power hungry/expensive sensors that are transmitting the sensed data over the wireless channel. Over time, the systems become difficult to maintain, especially in remote areas due to the battery replacement issues with large number of devices. Therefore, a novel solution must provide an alternative, cost and energy effective device that has unique advantage over the existing solutions. This work explores a concept of a novel, low-power, LoRa-based, cost-effective system which achieves humidity sensing using Deep learning techniques that can be employed to sense soil humidity with the high accuracy simply by measuring signal strength of the given underground beacon device.

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