Artificial Intelligence based Bot Assisted Irrigation System

This paper elucidates the most significant approaches for self-learning schemes, to create a self-learning algorithm and an appropriate physical model for efficient vision in real-life applications. The scheme designated in this paper is comprehended with the Neural Network method to design and implement an efficient bot assisted irrigation system. Irrigation is an irreplaceable part of the agriculture sector. Adequate water supply ensures maximized crop yield and in turn food security for the population. Advanced irrigation methods can boost water utilization efficiency, attaining commercial advantage while also reducing effects on the environment. In the presented work an automated agricultural system is proposed to ease the process of farming and reduce irrigation wastage. Microcontroller assisted automaton with a self-learning approach will traverse and collect soil parameters, suitably guiding the dedicated sprinkler system. The guidance is possible through the exchange of data between the interconnected components using the online information highway. The simulator has been established in MATLAB to be as close as possible to the physical model. Simulation results show that the neural network method makes the simulated agent can steer in an unidentified atmosphere, while the physical model only handles stationary hindrances due to physical restrictions and project time scale. An artificial intelligence-based working model has been designed to control a robot wirelessly. This system helps in efficiently fulfilling the soil moisture requirements without constant supervision and makes work simpler on a huge scale where the process of automation empowers human effort and save water in many aspects.