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Machine Learning and Soil Humidity Sensing: Signal Strength Approach

Published:29 October 2021Publication History
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Abstract

The Internet-of-Things vision of ubiquitous and pervasive computing gives rise to future smart irrigation systems comprising the physical and digital worlds. A 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 a 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 the concept of a novel, low-power, LoRa-based, cost-effective system that achieves humidity sensing using Deep Learning techniques that can be employed to sense soil humidity with high accuracy simply by measuring the signal strength of the given underground beacon device.

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  1. Machine Learning and Soil Humidity Sensing: Signal Strength Approach

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              • Published in

                cover image ACM Transactions on Internet Technology
                ACM Transactions on Internet Technology  Volume 22, Issue 2
                May 2022
                582 pages
                ISSN:1533-5399
                EISSN:1557-6051
                DOI:10.1145/3490674
                • Editor:
                • Ling Liu
                Issue’s Table of Contents

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                Publication History

                • Published: 29 October 2021
                • Revised: 1 July 2020
                • Accepted: 1 July 2020
                • Received: 1 May 2020
                Published in toit Volume 22, Issue 2

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