Bayesian Algorithm-Based Automation System for Control of Agricultural Field Parameters

Agriculture is the most significant economic sector in our nation. Agriculture must support water conservation efforts and environmental concerns due to the rapidly rising demand for water and its supply. However, the regular operations of natural farming will be impacted by the development of wireless technologies. The sensor used in wireless networks to track soil parameters like temperature, humidity, and PH. In our proposed machine learning system, the LoRa and ESP32 Wi-fi modules are used to control the irrigation and fertiliser process. It also includes an Environment Monitoring Aggregator node, which has sensors to monitor air temperature, humidity, etc. NPK sensor used to assess the soil's chemical makeup for fertiliser management. The solenoid valve opens the specific fertiliser tank to release fertiliser into the soil based on the data gathered. Along with UV traps for pest control, this system uses current and voltage sensors to keep an eye on the motor. The obtained results of our proposed system show that the fertiliser system operates effectively and that fertiliser can be added automatically as needed.