Adaptive Water Resource Allocation in Smart Agriculture using Time-Varying Bayesian Networks

Effective allocation of water resources is essential for sustainable agriculture, particularly in smart farming contexts where improving resource utilization can improve crop output and resilience. This paper presents an adaptive approach for water resource allocation that employs Time-Varying Bayesian Networks (TVBNs) to dynamically model water needs based on real-time environmental and crop-specific data. Our methodology dynamically modifies water distribution by integrating time-sensitive variables and probabilistic analyses, accounting for seasonal variations, soil moisture content, and stages of crop development. Experimental findings indicate that the suggested TVBN model attains a mean accuracy of 92.5% in forecasting water requirements, markedly enhancing water consumption efficiency and minimizing waste. These findings provide a scalable approach for adaptive irrigation in intelligent agricultural systems, underscoring the potential of TVBNs to improve sustainable farming methods.