Smart Farming: An IOT-Enabled Precision Agriculture System for Soil Prediction

Because of the scarcity of water, water management is of the utmost importance for the residential, industrial, and agricultural sectors, all of which need a significant amount of extra water for irrigation. Among the many methods of irrigation, flood, spray, and drip are some of the most used strategies. When it comes to planning irrigation, it is necessary to take into consideration conventional agricultural factors such as temperature, humidity, and soil moisture. The irrigation system is activated by sensor data, which allows fields to be irrigated. By acting as mediators between the host and the devices, Internet of Things platforms such as Blynk are able to collect data and photographs. It monitors the board in order to collect data on a regular basis and maintains the connections and authorizations between the microcontroller and the smartphone. In order to provide data on agricultural fields, Arduino UNO collected sensor data and showed it in a graphical user interface. It is strongly suggested that agricultural irrigation requirements can be determined via the use of machine learning. The method that has been proposed comes highly recommended and has a number of primary objectives. The first step is to build a soil condition monitoring system that includes a sensor array and a wireless communication module. This system will be used to convey the soil conditions at the weather station. Second, the Internet of Things makes it possible for weather stations to monitor the weather and soil sensor nodes to measure the state of the soil. Third, the system determines the most effective machine learning strategy for predicting irrigation with changeable weather. The Internet of Things is able to remotely monitor and store data from weather stations, including soil and weather, at the lowest possible computational cost.