Integration of Markov Chain Predictive Model with Wireless Sensor Network for Optimized Irrigation in Precision Agriculture

With the increasing global demand for sustainable water management, agriculture, which accounts for approximately 70% of freshwater withdrawals, faces critical challenges in optimizing irrigation practices. This study explores the feasibility of integrating a Markov chain predictive model with wireless sensor networks (WSNs) to enhance irrigation management in precision agriculture. The proposed system aims to predict short-term soil moisture levels based on historical and real-time data, allowing for proactive irrigation scheduling. Through a theoretical analysis of the Markov chain models applicability to soil moisture prediction, combined with a review of WSN architectures, this study evaluates the potential benefits and limitations of this integrated approach. Our findings suggest that such a system could improve water-use efficiency and energy savings by reducing the need for reactive irrigation. While challenges remain in model accuracy and scalability, this research offers insights into a data-driven approach to irrigation, emphasizing the importance of predictive capabilities in sustainable agriculture. Future research should involve practical field trials and model refinement to assess real-world viability and performance.