smol: Sensing Soil Moisture using LoRa

TL;DR

smol is a low-cost, compact soil moisture sensing system that uses LoRa signal attenuation to automatically monitor soil conditions, offering an affordable alternative to expensive commercial sensors.

Contribution

This work introduces a novel low-cost soil moisture sensor leveraging LoRa signal attenuation, enabling automatic and remote environmental monitoring.

Findings

Soil moisture can be predicted from LoRa RSSI and transmission power.

The prototype works effectively in indoor and outdoor environments.

Calibration improves the accuracy of soil moisture estimation.

Abstract

Technologies for environmental and agricultural monitoring are on the rise, however, there is a lack of small, low-power, and lowcost sensing devices in the industry. One of these monitoring tools is a soil moisture sensor. Soil moisture has significant effects on crop health and yield, but commercial monitors are very expensive, require manual use, or constant attention. This calls for a simple and low-cost solution based on novel technology. In this work, we introduce smol: Sensing Soil Moisture using LoRa, a low-cost system to measure soil moisture using received signal strength indicator (RSSI) and transmission power. It is compact and can be deployed in the field to collect data automatically with little manual intervention. Our design is enabled by the phenomenon that soil moisture attenuates wireless signals, so the signal strength between a transmitter-receiver pair decreases. We exploit this physical property to determine the variation in soil moisture. We designed and tested our measurement-based prototype in both indoor and outdoor environments. With proper regression calibration, we show soil moisture can be predicted using LoRa parameters.