Quantum-Enhanced Artificial Intelligence Models for Autonomous Multi-Agent Crop Monitoring Systems

The adoption of artificial intelligence (AI), Internet of Things (IoT), remote sensing, robotics, and autonomous
systems has advanced precision agriculture through intelligent crop monitoring and data-driven management.
However, conventional AI may face challenges in complex optimization, multi-agent coordination, adaptive
decision-making, and computational scalability. This study presents a Quantum-Enhanced Artificial Intelligence
(Q-AI) framework for autonomous multi-agent crop monitoring. The framework integrates classical and quantum
machine learning, quantum-enhanced optimization, and a simulated multi-agent environment for crop disease
detection, agricultural data analysis, monitoring, task allocation, and decision-making. PlantVillage crop disease
data and structured agricultural data containing environmental and soil variables were used. The architecture
comprises data acquisition, multi-agent coordination, classical AI processing, quantum enhancement, and
intelligent decision layers. Performance was assessed using accuracy, precision, recall, F1-score, monitoring
coverage, task allocation efficiency, resource utilization, and decision latency. The framework demonstrates the
potential of hybrid quantum-classical approaches to improve predictive and operational performance in
autonomous agricultural monitoring and provides a foundation for further empirical research in quantum
enabled precision agriculture.

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