Hybrid Caps Based Deep Conventional Neural Network for IoT Based Smart Water Irrigation for the Agriculture Field

Abstract

These present studies based on the wireless sensor network in smart agriculture have considered main objectives of this research. The purpose of this work was to develop an IoT based networks in smart agriculture using various types of sensors and applications. However, sensors have restricted processing, limited memory resources, less energy and transmission that may negatively influence yield from agriculture. Therefore, the current study conducted to develop an effective water management approach in the WSN based on IoT in smart agriculture to reduce water use in crops. Smart agriculture methods differ entirely from traditional agricultural methods; many famous people currently manage advanced technology or devices.Such as smart tractors, hydroponics, field-based drones and sensors to achieve higher productivity. In the agriculture field, DCNN technology used to anticipate accurate water levels. In order to measure its performance, the stimulation analysis for the suggested framework conducted. To improve this research work by using the proposed techniques of clustering techniques, and PSO optimization using in this research work. In addition, we are using the WSN to achieving more effective to develop this research work.