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.
Highlights
Water is one of the essential factors limiting agriculture
It comprises agriculture methods which have allowed for optimizing irrigation management, by using drip irrigation system for controlled deficit irrigation approaches that are capable of maintaining crops with low irrigation volume
The sensor data are transferred to the Arduino module which transmits the sensor data to cloud server for further process
Summary
Such factors are increased in the areas where there is scarcity of water resources [1] In such areas, the significance of effectively handling irrigation is an essential factor for sustainable production. The significance of effectively handling irrigation is an essential factor for sustainable production It comprises agriculture methods which have allowed for optimizing irrigation management, by using drip irrigation system for controlled deficit irrigation approaches that are capable of maintaining crops with low irrigation volume. Soil sensor nodes had begun to be utilized for these purposes, taking into account volumetric water content and thermal sensors, water matric potential, and, currently, their integration with wireless techniques for flexible execution [7]. A detailed experimental results analysis outperformed the promising efficiency of the presented technique over the other recent state of art techniques interms of different models
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