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

arXiv: https://arxiv.org/abs/2505.10803 · HTML: https://mlsanity.com/paper/2505.10803/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 14 of its 51 references that are in the map.
- First posted on arXiv 16 May 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 14 of 14)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [A Survey of Multi-Objective Sequential Decision-Making](https://mlsanity.com/paper/1402.0590/) (2014)
- [Dynamic Weights in Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/1809.07803/) (2018)
- [Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/1610.02707/) (2016)
- [Optimizing Nitrogen Management with Deep Reinforcement Learning and Crop Simulations](https://mlsanity.com/paper/2204.10394/) (2022)
- [Trust in Human-AI Interaction: Scoping Out Models, Measures, and Methods](https://mlsanity.com/paper/2205.00189/) (2022)
- [gym-DSSAT: a crop model turned into a Reinforcement Learning environment](https://mlsanity.com/paper/2207.03270/) (2022)
- [Towards Transparent Robotic Planning via Contrastive Explanations](https://mlsanity.com/paper/2003.07425/) (2020)
- [Learning-based agricultural management in partially observable environments subject to climate variability](https://mlsanity.com/paper/2401.01273/) (2024)
- [Reinforcement Learning Based Temporal Logic Control with Soft Constraints Using Limit-deterministic Generalized Buchi Automata](https://mlsanity.com/paper/2101.10284/) (2021)
- [Intelligent Traffic Light via Policy-based Deep Reinforcement Learning](https://mlsanity.com/paper/2112.13817/) (2021)
- [Model-free Motion Planning of Autonomous Agents for Complex Tasks in Partially Observable Environments](https://mlsanity.com/paper/2305.00561/) (2023)
- [Intelligent Agricultural Management Considering N2O Emission and Climate Variability with Uncertainties](https://mlsanity.com/paper/2402.08832/) (2024)

## Abstract (arXiv)

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.