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

International Journal of Electrical and Electronics Engineering

© 2023 by SSRG - IJEEE Journal

Volume 10 Issue 10

Year of Publication : 2023

Authors : P.K. Rajani, Guruprasad Deshpande, Mangesh Goswami, Jayesh Kolhe, Vishal Khandagale, Milind Mujumdar, Bhupendra Bahadur Singh

10.14445/23488379/IJEEE-V10I10P108

How to Cite?

P.K. Rajani, Guruprasad Deshpande, Mangesh Goswami, Jayesh Kolhe, Vishal Khandagale, Milind Mujumdar, Bhupendra Bahadur Singh, "IoT-Based Low-Cost Soil Moisture and Soil Temperature Monitoring System," SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 10, pp. 66-74, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I10P108

Abstract:

Soil Moisture (SM) is a finite amount of water molecules within the pore spaces and 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. Precisely monitoring and quantifying high-resolution surface and subsurface soil moisture observations are essential. 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 sensor assembly 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 analyzing soil moisture and temperature. The soil moisture profile obtained through the sensor deployed is highly correlated (r=0.9) with in-situ gravimetric observations, having Root Means 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.

Keywords:

Soil Moisture, Soil temperature, Raspberry Pi, Internet of Things (IoT), ThingSpeak platform IITM- COSMOS site.

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