Framework For Artificial Intelligence Based Irrigation Planning Using Linear Optimization Methods and Models with Discrete Algorithm

This paper proposes a unique framework for optimum irrigation planning by making use of discrete algorithms, empirical linkages, and artificial intelligence (AI). It highlights the need of efficient irrigation in order to keep agriculture going while tackling the serious challenges of a growing global population and climate change. Using a combination of AI, discrete algorithms, and linear optimization approaches, the framework provides a data-driven strategy that is both intelligent and effective. Important aspects include predictive modelling, monitoring in real time, and making decisions based on data, all of which place a premium on effectiveness and precision. The use of artificial intelligence (AI) in irrigation not only maximizes financial profits but also contributes to the advancement of environmentally sustainable practices by reducing water waste. It is feasible, via comparative study, to find AI-based models with competitive prediction accuracy (NNARX and GEP), discrete algorithms with the capability of optimization (PSO and FA), and empirical relations with rule-based approximations that are easier to understand. This technique makes it feasible to conduct irrigation planning that is effective, data-driven, and sustainable; agriculture and resource management will both profit from the use of this approach.