ITM Web Conf.
Volume 32, 2020International Conference on Automation, Computing and Communication 2020 (ICACC-2020)
|Number of page(s)||7|
|Published online||29 July 2020|
Design and Development of Solar Powered Intelligent Irrigation & Water Management System using Data Science
Department of Instrumentation Engineering, Ramrao Adik Institute of Technology, Dr. D.Y. Patil Vidyanagar, Nerul, Navi Mumbai, Maharashtra – 400706, India
E-mail: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com
India is an agricultural country. Agriculture and its allied activities act as main source of livelihood for more than 80 percent population of rural India. Available irrigation systems are not efficient and lead to wastage of water. Literature revels that there is need to develop automated irrigation system with latest technology. In this paper we are proposing a system which will detect the moisture percentage of the respective farmland and compare it with the provided set point. Based on the difference our machine learning algorithms will decide how long the water pump will remain switched on, it will then close the pump after that particular time and give out final moisture percentage reading and final water level of the water storage tank. Also, there will be a website provided which will continuously show current moisture percentage, amount of water present in the tank, how long water in the tank will last, the usage statistics and predict preferred and non- preferred crop to grow in that particular season. Plus a manual override will be provided for all systems.Usage statistics will consist of a graph showing water usage vs day. System display will show amount of water used vs date and below that the median and mode of the outcomes will also be indicated. Proposed in-house designed system has potential to provide the list of preferred and non-preferred crops.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.