Abstract: Rainfall in India is very unpredictable and is characterised by monsoon gaps. Rainfall prediction is very crucial for irrigation management to enhance farm productivity.. This article presents a portable rainfall prediction device which can be carried to fields. In the field by sensing the current atmospheric parameters like temperature, humidity, atmospheric pressure along with the current status of the sky to know the types of clouds present and gives the chances of rainfall. It is a novel approach in terms of portability of the device and it will give the prediction based on current information at a particular location by combining the predictions from the model of image processing of the clouds using deep learning and the currently sensed weather parameters are processed using machine learning without using WIFI or internet connection by providing Edge analytics where the data processing, rainfall prediction, and decision making is carried out locally on the device without any backend servers or cloud platform which will be very useful for the people like farmers who don’t have accessibility to internet in villages. The farmers can decide before every irrigation schedule, based on the prediction to what extent the crops can be irrigated. If chances of rain are very low 90% irrigation can be carried out, If chances of rain are predicted as low to medium then 40 to 60% irrigation can be done and if the prediction says medium to heavy rainfall then no irrigation is recommended.

Geeta Ambildhuke and Barnali Gupta Banik, “IoT based Portable Weather Station for Irrigation Management using Real-Time Parameters” International Journal of Advanced Computer Science and Applications(IJACSA), 13(5), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130533

@article{Ambildhuke2022,
title = {IoT based Portable Weather Station for Irrigation Management using Real-Time Parameters},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130533},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130533},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {5},
author = {Geeta Ambildhuke and Barnali Gupta Banik}
}