IoT- Based Low-Cost Soil Moisture and Soil Temperature Monitoring System

TL;DR

This paper presents a low-cost IoT-based system using Raspberry Pi for real-time monitoring of soil moisture and temperature, demonstrating high correlation with traditional methods and enabling efficient data visualization.

Contribution

The paper introduces a novel low-cost IoT system for soil monitoring that integrates sensors, wireless communication, and mobile visualization, with validated accuracy against standard methods.

Findings

Soil moisture sensor data correlates highly (r=0.9) with gravimetric observations.

The system achieves an RMSE of about 3.1% for soil moisture measurements.

Temperature readings closely match in-situ temperature data.

Abstract

Soil moisture (SM) is referred to as a finite amount of water molecules within the pore spaces and it is a crucial parameter of Hydro-Meteorological processes. The behaviour of soil moisture water changes spatially and temporally in response to topography, soil characteristics, and climate[1]. Soil moisture is overseen by various hydro-meteorological factors that vary vertically with depth, laterally across terrestrial shapes, and temporarily in feedback to the climate. The precise monitoring and quantification of high-resolution surface and subsurface soil moisture observations are very important [13]. This paper highlights the outcomes of the fieldwork carried out at IITM, Pune, wherein we have developed a soil moisture and temperature measurement system using Raspberry Pi and the Internet of things (IoT). The development is classified into three stages, the first stage includes the assembly of the sensor with the microprocessor. The deployment of the low-cost system, data generation, and communication through a wireless sensor network is part of the second stage. Finally, the third stage includes real-time data visualization using a mobile application and data server for analysing soil moisture and temperature. The soil moisture profile obtained through the sensor deployed is highly correlated (r=.9) with in-situ gravimetric observations, having a root mean square error (RMSE) of about 3.1%. Similarly, the temperature observations are well-matched with the in-situ standard temperature observation. Here we present the preliminary results and compare the accuracy with the state-of-the-art sensors.