Incorporating Farmer Trust Into AI-Driven Agricultural Management Optimization

As climate change evolves, farmers face growing challenges in achieving production goals, underscoring the urgency for sustainable agricultural management strategies. Reinforcement learning (RL), a technique of artificial intelligence (AI), has shown promise in optimizing resource management, such as fertilization, delivering greater efficiency and sustainability than traditional approaches. However, a critical barrier to adoption persists: the misalignment between AI-generated policies and farmers' practical preferences. To address this, we propose a mathematical model that quantifies farmers' confidence in AI-driven strategies. By integrating trust evaluation directly into the RL framework, our novel approach enables iterative refinement of AI-generated policies to align with farmers' expectations. This trustaware optimization ensures that AI solutions evolve to meet real-world agricultural needs, fostering both adoption and user satisfaction. While developed for agriculture, the trust model offers broad potential applications in domains such as healthcare, autonomous systems, and education, advancing human-centered AI systems designed for practical and ethical use.