JoWUA

Abstract

This paper presents a new Quantum Machine Learning (QML)-Enhanced Multiagent Reinforcement Learning (MARL) framework, designed for dynamic crop resource allocation in smart agriculture using IoT. The framework is based on multi-agent systems to enable collaborative operation among agents responsible for controlling irrigation, fertilization, pesticide injection, and energy use. Quantum machine learning, as a consequence, allows for exploration and exploitation tradeoffs in decision-making to be both more efficient (when compared to classical benchmarks) and better able to support optimal behavior in high-dimensional or complex agricultural systems. It promotes ongoing agent learning and adaptation to actual IoT sensor readings in real time, which may vary across states such as soil conditions, weather, and so on. In the following, we would like to compare and contrast our work on cooperative learning with single-agent RL models along a few dimensions, e.g., scalability (better), robustness & adaptive ability. The simulation results indicate that the crop yield can reach 1250kg/ha, and our method performs better than other existing methods by 14-32%. The WUE reached 85%, an increase of 13–42% and the energy consumption was 420kWh/season, a decrease of 8–12%. When TD loss was 0.05 and the reward variance was 0.04, stable synchronous training by learning with fast policy optimization was able to converge rapidly in 350 episodes. The score for multi-agent coordination in adaptation and militarization was around 0.9, suggesting adaptability in the face of difficult circumstances. The framework enhances crop yield prediction, sustainable resource management, and operational efficiency, all in an agile, highly adaptable, scalable, and energy-efficient manner. "QML-MARL makes use of quantum scalable multiagent coordination and ensures intelligent data-driven sustainable precision agriculture methodologically superior to state-of-the-art models."