Precision Irrigation Model For Agriculture Using Intelligent Iot

Abstract

The critical importance of water management arises from its scarcity, affecting not only domestic and industrial sectors but also agriculture, which demands a significant proportion of new water for irrigation purposes. Various methods of irrigation have been recognized, including flood, spray, and drip irrigation. The planning of irrigation requires careful consideration of several agricultural factors, such as temperature, humidity, and soil moisture, which are commonly observed.Sensor data will trigger the irrigation system to start watering the agricultural land. The collected data and images are sent to IoT platform like Blynk, and the IoT application acts as the intermediary between the host and the devices. It manages connections and authorizations between smartphones and microcontroller, while also continuously monitoring the board to collect data at regular intervals. The sensor information was collected by Arduino UNO, and the acquired data was presented in a Graphical User Interface (GUI) to provide agricultural field data.The suggested approach for determining irrigation needs for each crop is by utilizing machine learning. The system is highly recommended, and the proposed system aims to achieve several primary objectives. The first objective is to develop a soil condition monitoring system that comprises a sensor array and a wireless communication module for transmitting soil conditions from weather stations. Secondly, the use of low-cost IoT allows for the monitoring of weather conditions through weather stations, while soil conditions are gathered through various soil sensor nodes. Thirdly, the system focuses on identifying the most effective ML technique for forecasting irrigation needs, even in unpredictable climatic conditions. Finally, the IoT is utilized to remotely monitor and store soil and weather data from weather stations with optimal computational cost.