Machine Learning and Soil Humidity Sensing: Signal Strength Approach

TL;DR

This paper proposes a low-power, cost-effective soil humidity sensing system using LoRa technology and deep learning, which accurately estimates soil moisture by analyzing signal strength, reducing reliance on expensive sensors.

Contribution

It introduces a novel soil humidity sensing method leveraging signal strength and deep learning, offering an energy-efficient alternative to traditional sensor-based solutions.

Findings

Achieves high accuracy in soil humidity estimation

Reduces energy consumption compared to traditional sensors

Provides a cost-effective solution for remote irrigation systems

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.