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:10.1016/j.atech.2026.102145

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Developing and integrating trust modeling into multi-objective reinforcement learning for intelligent agricultural management

Journal article

Open access

Peer reviewed

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

Smart agricultural technology, Vol.14, 102145

08/2026

DOI: 10.1016/j.atech.2026.102145

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Abstract

•AI-driven precision agriculture boosts efficiency and sustainability.•Reinforcement learning surpasses traditional farming methods.•Human-AI interaction improves trust in AI farm management.•Novel trust model integrates farmers’ real-world experiences.•Embedding trust into RL ensures practical AI recommendations. [Display omitted] Precision agriculture, enhanced by artificial intelligence (AI), offers promising tools like remote sensing, intelligent irrigation, fertilization management, and crop simulation to boost agricultural efficiency and sustainability. Reinforcement learning (RL), in particular, has outperformed traditional approaches in optimizing yields and managing resources. Yet, widespread AI adoption remains limited by discrepancies between algorithmic recommendations and farmers’ practical experiences, local knowledge, and traditional practices. To bridge this gap, our study emphasizes Human-AI Interaction (HAII), specifically targeting transparency, usability, and trust in RL-driven farm management. We employ a well-established trust framework—consisting of ability, benevolence, and integrity—to construct a novel mathematical model quantifying farmers’ confidence in AI-based fertilization strategies. Farmer surveys conducted specifically for this research highlight critical misalignments, and these insights are incorporated into our trust model, subsequently integrated into a multi-objective RL framework. Unlike previous methods, our approach directly embeds trust into policy optimization, ensuring AI-generated recommendations are technically robust, economically feasible, context-sensitive, and socially acceptable. By aligning technical performance with human-centered trust, this research provides a practical path toward broader AI adoption in agriculture.

Agricultural Management

Human-AI interaction

Reinforcement learning

Trust model

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
Resource Type: Journal article
Publication Details: Smart agricultural technology, Vol.14, 102145
DOI: 10.1016/j.atech.2026.102145
ISSN: 2772-3755
eISSN: 2772-3755
Publisher: Elsevier B.V
Grant note: National Science Foundation: 2226936, 2420405 U.S. Department of Education: P116S210005
This material is based upon work supported by the National Science Foundation under grant numbers 2226936 and 2420405 and the U.S. Department of Education under grant number ED#P116S210005. Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation and the U.S. Department of Education.
Language: English
Electronic publication date: 04/22/2026
Date published: 08/2026
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: 9985163542802771

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