TY - GEN
T1 - Analysis of Microenvironment Data using Low-Cost Portable Data Logger Based on a Microcontroller
AU - Bhadola, P.
AU - Kunakhonnuruk, B.
AU - Kongbangkerd, A.
AU - Gupta, Y. M.
N1 - Publisher Copyright:
© The Electrochemical Society
PY - 2022
Y1 - 2022
N2 - Understanding the ecological and species diversity of natural places also requires the data of natural environmental factors but due to environmental and geographical conditions, monitoring the micro-environment can be difficult. In this context, we propose, a portable and cost-effective weather station based on the micro-controller Raspberry Pi Pico mounted with a digital temperature and humidity DHT11 sensor and a portable battery for monitoring and studying the effect of micro-climates on the forest, plant nursery, and farmland. The developed prototype is tested by collecting the temperature and humidity data in the control and random environment. A seasonal decomposition using moving averages on time series is done determining the trend, seasonal, and noise components. We perform the multi-fractal detrended fluctuation analysis on the collected data and estimated the Hurst exponent to check for the trends and patterns and their variation for different environments.
AB - Understanding the ecological and species diversity of natural places also requires the data of natural environmental factors but due to environmental and geographical conditions, monitoring the micro-environment can be difficult. In this context, we propose, a portable and cost-effective weather station based on the micro-controller Raspberry Pi Pico mounted with a digital temperature and humidity DHT11 sensor and a portable battery for monitoring and studying the effect of micro-climates on the forest, plant nursery, and farmland. The developed prototype is tested by collecting the temperature and humidity data in the control and random environment. A seasonal decomposition using moving averages on time series is done determining the trend, seasonal, and noise components. We perform the multi-fractal detrended fluctuation analysis on the collected data and estimated the Hurst exponent to check for the trends and patterns and their variation for different environments.
UR - https://www.scopus.com/pages/publications/85130554291
U2 - 10.1149/10701.15099ecst
DO - 10.1149/10701.15099ecst
M3 - Conference contribution
AN - SCOPUS:85130554291
T3 - ECS Transactions
SP - 15099
EP - 15109
BT - ECS Transactions
PB - Institute of Physics
T2 - 1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021
Y2 - 29 November 2021 through 30 November 2021
ER -