| Issue |
E3S Web Conf.
Volume 387, 2023
International Conference on Smart Engineering for Renewable Energy Technologies (ICSERET-2023)
|
|
|---|---|---|
| Article Number | 05012 | |
| Number of page(s) | 7 | |
| Section | Information Secutity | |
| DOI | https://doi.org/10.1051/e3sconf/202338705012 | |
| Published online | 15 May 2023 | |
Design and Implementation of a Smart Solar Irrigation System Using IoT and Machine Learning
1 Assistant Professor of Mathematics, National College (Autonomous/Affiliated to Bharathidasan University) Tirchy 620001
2 New Prince Shri Bhavani College Of Engineering and Technology, Chennai, India
3 Professor, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai - 127
4 Assistant Professor, Prince Dr. K. Vasudevan College of Engineering and Technology, Chennai - 127
5 Departmet of Mathematics, Mohamed Sathak Engineering College, Kilakarai, Tamil Nadu, India
† Corresponding author: srisenthil2011@gmil.com
anithamaths2010@gmail.com
Water scarcity is a major challenge in the agriculture industry, and traditional irrigation methods are often wasteful and inefficient. To address this challenge, a smart solar irrigation system that uses loT and Artificial Neural Network (ANN) algorithms can optimize water usage for agriculture. The system can provide automated irrigation, improve crop yields, and reduce water consumption. This paper proposes a design and implementation methodology of a smart solar irrigation system using loT and ANN algorithms. The system includes solar panels, a water pump, a water storage tank, sensors, loT devices, and ANN algorithms. The system is designed to automate the irrigation process by controlling the water pump based on the data collected from the sensors.
Key words: Smart irrigation / IoT / Machine Learning / Solar Energy / Water Scarcity / Crop Yield / Automation
© The Authors, published by EDP Sciences, 2023
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.