A review of model predictive control in precision agriculture

Abstract

Precision agriculture, driven by advanced technologies and data-driven decision-making, has emerged as a transformative approach to address global food demand, resource constraints, and sustainability challenges. In this context, Model Predictive Control (MPC) has garnered significant attention as a powerful control strategy capable of optimizing farming processes through predictive and anticipatory control actions. This review comprehensively explores the fundamentals and applications of MPC in precision agriculture. The review begins with an overview of MPC's principles, formulation, and optimization techniques, emphasizing its predictive and adaptable nature. Subsequently, it delves into the diverse applications of MPC in precision agriculture, including crop growth and yield optimization, pest and disease management, and autonomous machinery and robotics. The integration of MPC with precision agriculture machinery and its role in autonomous farming systems are also explored. Success stories and case studies highlight real-world applications of MPC, showcasing its positive impact on crop yields, resource utilization, and economic viability. Additionally, demonstrated benefits such as water conservation, reduced chemical usage, and improved produce quality attest to the significance of MPC in sustainable farming practices. While MPC offers numerous advantages, the review also discusses challenges, such as computational complexity, model uncertainty, and sensor reliability. The review concludes by underscoring MPC's potential in driving precision agriculture towards a more sustainable, efficient, and technologically advanced future.

Keywords

• Model predictive control
• System identification
• Internet of Things (IoT)
• Machine learning integration