IoT with Evolutionary Algorithm Based Deep Learning for Smart Irrigation System

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