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

2025-05-16

AI Summary

In smart agriculture, the disconnect between AI-driven recommendations and farmers’ local knowledge undermines trust, hindering the real-world deployment of reinforcement learning (RL). Method: We propose a human–AI interactive multi-objective RL framework that mathematically models triadic trust—comprising competence, benevolence, and integrity—as quantifiable factors directly integrated into the policy gradient update. The framework synergizes trust quantification, Human-AI Interactive Interface (HAII) design, empirical farmer surveys, and behavioral feedback loops. Contribution/Results: Our approach jointly optimizes technical performance (e.g., yield, resource efficiency) and socio-technical acceptability (i.e., trustworthiness, feasibility, contextual adaptability). Experiments demonstrate significantly increased farmer adoption intention toward AI-generated fertilizer recommendations, while preserving economic viability and regional applicability—thereby enhancing the social credibility of AI-enabled agricultural decision-making.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Machine Learning: Reinforcement Learning

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Bridging gaps between AI recommendations and farmers' practical experience
• Quantifying farmers' trust in AI-based agricultural strategies
• Integrating trust into multi-objective reinforcement learning for farm management

Innovation

• Integrates trust modeling into multi-objective reinforcement learning
• Quantifies farmer confidence via ability, benevolence, integrity metrics
• Embeds trust directly into AI policy optimization