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

Zhaoan Wang; Wonseok Jang; Bowen Ruan; Jun Wang; Shaoping Xiao

doi:10.48550/arxiv.2505.10803

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

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Open access

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

Zhaoan Wang, Wonseok Jang, Bowen Ruan, Jun Wang and Shaoping Xiao

ArXiV.org

Cornell University

05/15/2025

DOI: 10.48550/arxiv.2505.10803

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https://doi.org/10.48550/arxiv.2505.10803View

Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

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.

Computer Science - Artificial Intelligence

Details

Title: Subtitle: Developing and Integrating Trust Modeling into Multi-Objective Reinforcement Learning for Intelligent Agricultural Management
Creators: Zhaoan Wang - University of Iowa
Wonseok Jang - University of Iowa
Bowen Ruan - University of Iowa
Jun Wang - University of Iowa
Shaoping Xiao - University of Iowa, Iowa Technology Institute
Resource Type: Preprint
Publication Details: ArXiV.org
DOI: 10.48550/arxiv.2505.10803
ISSN: 2331-8422
Publisher: Cornell University; Ithaca, New York
Language: English
Date posted: 05/15/2025
Academic Unit: Electrical and Computer Engineering; Civil and Environmental Engineering; Marketing; Iowa Technology Institute; Physics and Astronomy; Chemical and Biochemical Engineering; Mechanical Engineering
Record Identifier: 9984824322702771

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