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International Journal of Computer and Engineering Optimization - IJCEO

SMART AGRICULTURE IN SPRINKLER IRRIGATION USING DEEP LEARNING NETWORK


An Internet of Things (IoT) is used to provide information about agricultural areas and then take action based on user input in "smart agriculture," an emerging idea. In this paper, a novel Smart agriculture based on Solar panel for Sprinkler irrigation (SSS) system that collects and monitors the environmental temperature, soil moisture and humidity. The temperature, humidity, and soil moisture measurements are kept in the cloud for analysis. Data obtained from cloud storage is validated by MobileNet. A farmer receives an alarm message from the mobile net if the soil moisture content is less than 20%, if the temperature and humidity are less than -40°C to +80°C, and if the relative humidity is between 0% and 100%. A mobile net alarm message is sent to a farmer if the pH is lower than 5.5. Solar panels are the renewable energy source for the farm and battery. When needed, the water pumps are powered by the element that stores extra electrical energy produced by the solar panels. A farmer uses sprinkling irrigation to remotely access the field after receiving an alert message from the mobile network. A farmer can remotely access a field and irrigate a farm using sprinkler irrigation when they receive an alarm message from the mobile network. The proposed method improves the overall accuracy of 98.57%, 96.24%, 89.65%, 91.68% and 99.37% AlexNet, ResNet-50, DenseNet, GoogleNet and MobileNet respectively.