The integration of artificial intelligence into agricultural practices, specifically through Consultation on Intelligent Agricultural Machinery Management (CIAMM), has the potential to revolutionize efficiency and sustainability in farming. This paper introduces a novel approach that leverages large language models (LLMs), particularly GPT-4, combined with multi-round prompt engineering to enhance decision-making processes in agricultural machinery management. We systematically developed and refined prompts to guide the LLMs in generating precise and contextually relevant outputs. Our approach was evaluated using a manually curated dataset from various online sources, and performance was assessed with accuracy and GPT-4 Scores. Comparative experiments were conducted using LLama-2-70B, ChatGPT, and GPT-4 models, alongside baseline and state-of-the-art methods such as Chain of Thought (CoT) and Thought of Thought (ThoT). The results demonstrate that our method significantly outperforms these approaches, achieving higher accuracy and relevance in generated responses. This paper highlights the potential of advanced prompt engineering techniques in improving the robustness and applicability of AI in agricultural contexts.

翻译：人工智能在农业实践中的集成，特别是通过智能农业机械管理咨询（CIAMM），具有革新农业生产效率与可持续性的潜力。本文提出一种创新方法，利用大语言模型（LLMs），特别是GPT-4，结合多轮提示工程来增强农业机械管理中的决策过程。我们系统性地开发并优化了提示指令，以引导大语言模型生成精确且符合情境的输出。我们的方法使用来自多个网络渠道手动整理的数据集进行评估，并通过准确率和GPT-4评分来衡量性能。我们使用LLama-2-70B、ChatGPT和GPT-4模型，以及思维链（CoT）和思维网络（ThoT）等基准方法与前沿技术进行了对比实验。结果表明，我们的方法显著优于这些现有方案，在生成响应的准确性和相关性方面均取得更高水平。本文强调了先进提示工程技术在提升人工智能于农业场景中的鲁棒性与适用性方面的潜力。