Quantum-Optimized IoT System for Precision Irrigation

This paper presents a Quantum-Optimized Decision Support System (QoDSS) for IoT-driven precision irrigation, addressing critical challenges in water conservation and real-time agricultural monitoring. We propose a hybrid quantum-classical architecture combining soil moisture sensors with the Quantum Approximate Optimization Algorithm (QAOA) to optimize irrigation control. The system dynamically adjusts valve and pump operations based on real-time field data, achieving 61% higher decision accuracy (reducing error from 158% to 62%) compared to classical methods, 58% water savings (310L/day), and complete prevention of waterlogging scenarios (tested up to 95% moisture levels). Field experiments demonstrate reliable operation with 2.1s average decision latency, validated across extreme moisture conditions (10% drought to 95% saturation). Key innovations include: (1) A novel QUBO formulation for moisture optimization (α = 2.3, β = 1.7, γ = 3.0), (2) Edge-quantum integration (RPi4 + IBMQ 5-qubit backend), and (3) Automated anomaly correction. This work provides a practical framework for quantum-IoT solutions in agriculture, demonstrating 38% energy reduction and 95% system reliability in real-world conditions.