Current agricultural data management and analysis paradigms are to large extent traditional, in which data collecting, curating, integration, loading, storing, sharing and analyzing still involve too much human effort and know-how. The experts, researchers and the farm operators need to understand the data and the whole process of data management pipeline to make fully use of the data. The essential problem of the traditional paradigm is the lack of a layer of orchestrational intelligence which can understand, organize and coordinate the data processing utilities to maximize data management and analysis outcome. The emerging reasoning and tool mastering abilities of large language models (LLM) make it a potentially good fit to this position, which helps a shift from the traditional user-driven paradigm to AI-driven paradigm. In this paper, we propose and explore the idea of a LLM based copilot for autonomous agricultural data management and analysis. Based on our previously developed platform of Agricultural Data Management and Analytics (ADMA), we build a proof-of-concept multi-agent system called ADMA Copilot, which can understand user's intent, makes plans for data processing pipeline and accomplishes tasks automatically, in which three agents: a LLM based controller, an input formatter and an output formatter collaborate together. Different from existing LLM based solutions, by defining a meta-program graph, our work decouples control flow and data flow to enhance the predictability of the behaviour of the agents. Experiments demonstrates the intelligence, autonomy, efficacy, efficiency, extensibility, flexibility and privacy of our system. Comparison is also made between ours and existing systems to show the superiority and potential of our system.

翻译：当前的农业数据管理与分析范式在很大程度上仍较为传统，其中数据的收集、整理、集成、加载、存储、共享和分析仍需耗费大量人力且依赖专业知识。专家、研究人员及农场经营者需要理解数据及其管理流程的各个环节，才能充分利用数据。传统范式的核心问题在于缺乏一层能够理解、组织并协调数据处理工具以最大化数据管理与分析成效的编排智能层。大型语言模型（LLM）新兴的推理与工具掌握能力使其有望胜任这一角色，从而推动从传统的用户驱动范式向AI驱动范式的转变。本文提出并探讨了一种基于LLM的自主农业数据管理与分析副驾驶构想。基于我们先前开发的农业数据管理与分析平台（ADMA），我们构建了一个概念验证型多智能体系统——ADMA Copilot。该系统能够理解用户意图，为数据处理流程制定计划并自动完成任务，其中三个智能体——基于LLM的控制器、输入格式化器与输出格式化器——协同工作。与现有基于LLM的解决方案不同，通过定义元程序图，我们的工作将控制流与数据流解耦，从而增强了智能体行为的可预测性。实验验证了本系统在智能性、自主性、有效性、效率、可扩展性、灵活性及隐私保护方面的表现。通过与现有系统的对比，进一步展现了本系统的优越性与潜力。