Open Access

ARTICLE

IoT with Evolutionary Algorithm Based Deep Learning for Smart Irrigation System

P. Suresh^1,*, R. H. Aswathy¹, Sridevi Arumugam², Amani Abdulrahman Albraikan³, Fahd N. Al-Wesabi⁴, Anwer Mustafa Hilal⁵, Mohammad Alamgeer⁶

1 Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, 641407, India
2 Department of Electrical Sciences, Adithya Institute of Technology, Coimbatore, 641107, India
3 Department of Computer Science, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Saudi Arabia
4 Department of Computer Science, King Khalid University, Muhayel Aseer, Saudi Arabia & Faculty of Computer and IT, Sana'a University, Sana'a, Yemen
5 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
6 Department of Information Systems, King Khalid University, Muhayel Aseer, Saudi Arabia

* Corresponding Author: P. Suresh. Email:

Computers, Materials & Continua 2022, 71(1), 1713-1728. https://doi.org/10.32604/cmc.2022.021789

Received 14 July 2021; Accepted 16 August 2021; Issue published 03 November 2021

Abstract

In India, water wastage in agricultural fields becomes a challenging issue and it is needed to minimize the loss of water in the irrigation process. Since the conventional irrigation system needs massive quantity of water utilization, a smart irrigation system can be designed with the help of recent technologies such as machine learning (ML) and the Internet of Things (IoT). With this motivation, this paper designs a novel IoT enabled deep learning enabled smart irrigation system (IoTDL-SIS) technique. The goal of the IoTDL-SIS technique focuses on the design of smart irrigation techniques for effectual water utilization with less human interventions. The proposed IoTDL-SIS technique involves distinct sensors namely soil moisture, temperature, air temperature, and humidity for data acquisition purposes. The sensor data are transmitted to the Arduino module which then transmits the sensor data to the cloud server for further process. The cloud server performs the data analysis process using three distinct processes namely regression, clustering, and binary classification. Firstly, deep support vector machine (DSVM) based regression is employed was utilized for predicting the soil and environmental parameters in advances such as atmospheric pressure, precipitation, solar radiation, and wind speed. Secondly, these estimated outcomes are fed into the clustering technique to minimize the predicted error. Thirdly, Artificial Immune Optimization Algorithm (AIOA) with deep belief network (DBN) model receives the clustering data with the estimated weather data as input and performs classification process. A detailed experimental results analysis demonstrated the promising performance of the presented technique over the other recent state of art techniques with the higher accuracy of 0.971.

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Keywords

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