Treffer: 多智能体大模型在农业中的应用研究与展望.

[Significance] With the rapid advancement of large language models (LLM) and multi-agent systems, their integration, multiagent large language models, is emerging as a transformative force in modern agriculture. Agricultural production involves complex, sequential, and highly environment-dependent processes, including tillage, planting, management, and harvesting. Traditional intelligent systems often struggle with the diversity, uncertainty, and coordination of these stages' demand. Multi-agent LLMs offer a new paradigm for agricultural intelligence by combining deep semantic understanding with distributed collaboration and adaptive coordination. Through role specialization, real-time perception, and cooperative decision-making, they can decompose complex workflows, adapt to changing conditions, and enable robust, full-process automation, making them well-suited to the challenges of modern agriculture. More importantly, their application marks a critical step toward the digital transformation, precision management, and sustainable development of agriculture. By enabling intelligent decision-making across the entire agricultural lifecycle, they provide both theoretical foundations and practical tools for building next-generation smart and unmanned farming systems. [Progress] The core concepts of multi-agent LLMs are first elucidated, covering the composition and characteristics of multi-agent systems as well as the development and training pipelines of LLMs. Then, the overall architecture of multi-agent systems is presented, encompassing both the environments in which agents operate and their internal structures. The collaborative patterns of multi-agent LLMs are then examined in terms of coordination structures and temporal organization. Following this, interaction mechanisms are discussed from multiple dimensions, including interactions between agents and the external environment, inter-agent communication, communication protocol frameworks, and communication security. To demonstrate the varying task specializations of different multi-agent frameworks, a comparative benchmark survey table is provided by synthesizing benchmark tasks and results reported in existing studies. The results show that different multi-agent large language model architectures tend to perform better on specific types of tasks, reflecting the influence of agent framework design characteristics such as role assignment strategies, communication protocols, and decision-making mechanisms. Furthermore, several representative architectures of multi-agent LLMs, as proposed in existing studies, are briefly reviewed. Based on their design features, their potential applicability to agricultural scenarios is discussed. Finally, current research progress and practical applications of LLMs, multimodal large models, and multi-agent LLMs in the agricultural domain are surveyed. The application architecture of agricultural LLMs is summarized, using rice cultivation as a representative scenario to illustrate the collaborative process of a multi-agent system powered by LLMs. This process involves data acquisition agents, data processing agents, task allocation and coordination agents, task execution agents, and feedback and optimization agents. The roles and functions of each kind of agent in enabling automated and intelligent operations throughout the entire agricultural lifecycle, including tillage, planting, management, and harvesting, are comprehensively described. In addition, drawing on existing research on multimodal data processing, the pseudocode is provided to illustrate the basic logic of the data processing agents. [Conclusions and Prospects] Multiagent LLMs technology holds vast promise in agriculture but still confronts several challenges. First, limited model interpretability, stemming from opaque internal reasoning and high-dimensional parameter mappings, hinders decision transparency, traceability, user trust, and debugging efficiency. Second, model hallucination is significant, probabilistic generation may deviate from facts, leading to erroneous environmental perception and decisions that cause resource waste or crop damage. Third, multi-modal agricultural data acquisition and processing remain complex due to non-uniform equipment standards, heterogeneous data, and insufficient cross-modal reasoning, complicating data fusion and decision-making. Future directions include: (1) enhancing interpretability via chain-ofthought techniques to improve reasoning transparency and traceability; (2) reducing hallucinations by integrating knowledge bases, retrieval-augmented generation, and verification mechanisms to bolster decision reliability; and (3) standardizing data formats to strengthen cross-modal fusion and reasoning. These measures will improve system stability and efficiency, providing solid support for the advancement of smart agriculture. [ABSTRACT FROM AUTHOR]

［目的/意义］ 随着多智能体系统和大模型技术的迅速发展, 其在农业领域的应用将继续成为推动农业现 代化和智能化的重要驱动力。本文旨在为农业数字化转型提供理论支持和实践参考, 助力农业生产的精准化、智 能化和可持续发展。［进展］ 本文首先阐述了多智能体系统和大语言模型的基本概念, 系统梳理了多智能体系统和 大语言模型的基本原理与框架。其次, 着重分析了多智能体大模型的技术特点及其在农业中的潜在价值, 列举了 大模型在农业中的研究和应用状况, 提出了大模型在农业中的应用架构和多智能体协作流程。最后, 指出多智能 体大模型在农业领域的核心挑战和未来研究方向。［结论/展望］ 多智能体大模型将凭借其高效协作与场景交互能 力, 有望在"耕、种、管、收"全流程智能化管理、农业灾害预警与应急决策、品种改良与育种创新等方面发挥 更大作用。然而, 当前多智能体大模型仍面临可解释性不足、幻觉问题, 以及多模态数据的采集和处理方面的挑 战。未来需通过引入更先进的推理机制、构建高效的知识库, 以及开发针对农业场景的多模态大模型, 进一步提 升系统的可靠性和效率, 为农业的智能化发展提供强有力的技术支持。 [ABSTRACT FROM AUTHOR]