Developing and Integrating Trust Modeling into Multi-Objective Reinforcement Learning for Intelligent Agricultural Management

• URL: http://arxiv.org/abs/2505.10803v1
• Date: Fri, 16 May 2025 02:52:16 GMT
• Title: Developing and Integrating Trust Modeling into Multi-Objective Reinforcement Learning for Intelligent Agricultural Management
• Authors: Zhaoan Wang, Wonseok Jang, Bowen Ruan, Jun Wang, Shaoping Xiao,
• Abstract summary: This study focuses on transparency, usability, and trust in RL-based farm management.<n>We employ a well-established trust framework to develop a novel mathematical model quantifying farmers' confidence in AI-based fertilization strategies.<n>Unlike prior methods, our approach embeds trust directly into policy optimization, ensuring AI recommendations are technically robust, economically feasible, context-aware, and socially acceptable.
• Score: 4.698114737827742
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Precision agriculture, enhanced by artificial intelligence (AI), offers promising tools such as remote sensing, intelligent irrigation, fertilization management, and crop simulation to improve agricultural efficiency and sustainability. Reinforcement learning (RL), in particular, has outperformed traditional methods in optimizing yields and resource management. However, widespread AI adoption is limited by gaps between algorithmic recommendations and farmers' practical experience, local knowledge, and traditional practices. To address this, our study emphasizes Human-AI Interaction (HAII), focusing on transparency, usability, and trust in RL-based farm management. We employ a well-established trust framework - comprising ability, benevolence, and integrity - to develop a novel mathematical model quantifying farmers' confidence in AI-based fertilization strategies. Surveys conducted with farmers for this research reveal critical misalignments, which are integrated into our trust model and incorporated into a multi-objective RL framework. Unlike prior methods, our approach embeds trust directly into policy optimization, ensuring AI recommendations are technically robust, economically feasible, context-aware, and socially acceptable. By aligning technical performance with human-centered trust, this research supports broader AI adoption in agriculture.

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