Enhancing Agricultural Machinery Management through Advanced LLM Integration

TL;DR

This paper presents a novel approach using GPT-4 and multi-round prompt engineering to improve decision-making in agricultural machinery management, demonstrating superior performance over existing methods.

Contribution

It introduces a systematic prompt engineering method with GPT-4 for agricultural machinery management, outperforming baseline and state-of-the-art models.

Findings

Our method achieves higher accuracy and relevance.

GPT-4 outperforms LLama-2-70B and ChatGPT.

Enhanced prompt engineering improves AI robustness in agriculture.

Abstract

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.