Incorporating Farmer Trust Into AI-Driven Agricultural Management Optimization

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

doi:10.1109/CAI64502.2025.00270

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Conference proceeding

Incorporating Farmer Trust Into AI-Driven Agricultural Management Optimization

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

2025 IEEE Conference on Artificial Intelligence (CAI), pp.1469-1474

05/05/2025

DOI: 10.1109/CAI64502.2025.00270

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Abstract

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.

Agricultural Management

Mathematical Models

Optimization

Sustainable Development

Artificial intelligence

Faces

Iterative methods

Medical services

Production

Reinforcement learning

Resource management

Trust model

Details

Title: Subtitle: Incorporating Farmer Trust Into AI-Driven Agricultural Management Optimization
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: Conference proceeding
Publication Details: 2025 IEEE Conference on Artificial Intelligence (CAI), pp.1469-1474
DOI: 10.1109/CAI64502.2025.00270
Publisher: IEEE
Grant note: 2226936,2420405 / National Science Foundation (10.13039/100000001)
Language: English
Date published: 05/05/2025
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: 9984848017802771

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