Authors :
Aman Kumar Singh; Pankaj Kankani; Sakshi Bharti; Samiya Goyal
Volume/Issue :
Volume 7 - 2022, Issue 10 - October
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3Wnv0eT
DOI :
https://doi.org/10.5281/zenodo.7270766
Abstract :
The given paper proposes a solution to the
damage caused to the wheat grain via a sensor enabled
wireless IOT network. The prototype uses Arduino UNO
board and NODEMCU ESP8266 for obtaining and
transferring data. The data of various parameters (like
humidity, temperature, moisture, CO2 gas and pH) are
collected and is then sent to the Google’s Firebase Cloud
for data storage and management. From there, it is then
sent to the ARIMA Machine Learning Model for
prediction of relative humidity for the next five days
which helps us to analyse the microbial activity inside
the silo. Additionally, an ultrasonic sensor is used to
detect the level of wheat grain in silo so that illegal
activities like corruption can be prohibited. All the data
collected, and results obtained is presented on an online
dashboard for easy visualization via graphs and
appropriate conclusions. The simulation of a real–time
monitoring system along with the proposed shelf life as
researched through different papers, journals and
publications is also displayed on the online dashboard.
Keywords :
Arduino, NodeMCU ESP8266, Firebase, Machine Learning, Data Analytics, ARIMA Model, Grading & Classification, Web Display, Data Visualisation.
The given paper proposes a solution to the
damage caused to the wheat grain via a sensor enabled
wireless IOT network. The prototype uses Arduino UNO
board and NODEMCU ESP8266 for obtaining and
transferring data. The data of various parameters (like
humidity, temperature, moisture, CO2 gas and pH) are
collected and is then sent to the Google’s Firebase Cloud
for data storage and management. From there, it is then
sent to the ARIMA Machine Learning Model for
prediction of relative humidity for the next five days
which helps us to analyse the microbial activity inside
the silo. Additionally, an ultrasonic sensor is used to
detect the level of wheat grain in silo so that illegal
activities like corruption can be prohibited. All the data
collected, and results obtained is presented on an online
dashboard for easy visualization via graphs and
appropriate conclusions. The simulation of a real–time
monitoring system along with the proposed shelf life as
researched through different papers, journals and
publications is also displayed on the online dashboard.
Keywords :
Arduino, NodeMCU ESP8266, Firebase, Machine Learning, Data Analytics, ARIMA Model, Grading & Classification, Web Display, Data Visualisation.
