Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 148 tok/s
Gemini 2.5 Pro 44 tok/s Pro
GPT-5 Medium 23 tok/s Pro
GPT-5 High 30 tok/s Pro
GPT-4o 86 tok/s Pro
Kimi K2 197 tok/s Pro
GPT OSS 120B 458 tok/s Pro
Claude Sonnet 4.5 38 tok/s Pro
2000 character limit reached

IoT- Based Low-Cost Soil Moisture and Soil Temperature Monitoring System (2206.07488v1)

Published 23 May 2022 in eess.SP, cs.SY, eess.SY, physics.ao-ph, and physics.ins-det

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.

Citations (1)

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube