Integration of Markov Chain Predictive Model with Wireless Sensor Network for Optimized Irrigation in Precision Agriculture
Year: 2024
Type: article
Abstract: 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 model’s 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.
Author: Chongzhen Ma
Institution: Queen Mary University of London
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W4405333715