Yearly population growth will lead to a significant increase in agricultural production in the coming years. Twenty-first century agricultural producers will be facing the challenge of achieving food security and efficiency. This must be achieved while ensuring sustainable agricultural systems and overcoming the problems posed by climate change, depletion of water resources, and the potential for increased erosion and loss of productivity due to extreme weather conditions. Those environmental consequences will directly affect the price setting process. In view of the price oscillations and the lack of transparent information for buyers, a multi-agent system (MAS) is presented in this article. It supports the making of decisions in the purchase of sustainable agricultural products. The proposed MAS consists of a system that supports decision-making when choosing a supplier on the basis of certain preference-based parameters aimed at measuring the sustainability of a supplier and a deep Q-learning agent for agricultural future market price forecast. Therefore, different agri-environmental indicators (AEIs) have been considered, as well as the use of edge computing technologies to reduce costs of data transfer to the cloud. The presented MAS combines price setting optimizations and user preferences in regards to accessing, filtering, and integrating information. The agents filter and fuse information relevant to a user according to supplier attributes and a dynamic environment. The results presented in this paper allow a user to choose the supplier that best suits their preferences as well as to gain insight on agricultural future markets price oscillations through a deep Q-learning agent.

中文翻译：

---

面向可持续农业市场的 eep Q-Learning 和基于偏好的多智能体系统

每年的人口增长将导致未来几年农业生产的显着增加。二十一世纪的农业生产者将面临实现粮食安全和效率的挑战。这必须在确保可持续农业系统并克服气候变化、水资源枯竭以及由于极端天气条件导致侵蚀增加和生产力损失的可能性所带来的问题的同时实现。这些环境后果将直接影响价格制定过程。鉴于价格波动和买家缺乏透明信息，本文提出了一种多代理系统（MAS）。它支持购买可持续农产品的决策。拟议的 MAS 包括一个系统，该系统在根据某些基于偏好的参数选择供应商时支持决策，旨在衡量供应商的可持续性，以及一个用于农业未来市场价格预测的深度 Q 学习代理。因此，已经考虑了不同的农业环境指标（AEI），以及使用边缘计算技术来降低数据传输到云端的成本。提出的 MAS 结合了价格设置优化和用户在访问、过滤和集成信息方面的偏好。代理根据供应商属性和动态环境过滤和融合与用户相关的信息。