LLMs for Enhanced Agricultural Meteorological Recommendations

TL;DR

This paper introduces a novel LLM-based framework with multi-round prompting to enhance agricultural meteorological recommendations, significantly improving accuracy and relevance for farmers.

Contribution

It presents a new multi-round prompt engineering approach applied to LLMs for tailored agricultural advice, outperforming baseline models and Chain-of-Thought methods.

Findings

Achieved up to 90% accuracy in recommendations

Significant improvement over baseline models

Validated through real-world pilot studies

Abstract

Agricultural meteorological recommendations are crucial for enhancing crop productivity and sustainability by providing farmers with actionable insights based on weather forecasts, soil conditions, and crop-specific data. This paper presents a novel approach that leverages large language models (LLMs) and prompt engineering to improve the accuracy and relevance of these recommendations. We designed a multi-round prompt framework to iteratively refine recommendations using updated data and feedback, implemented on ChatGPT, Claude2, and GPT-4. Our method was evaluated against baseline models and a Chain-of-Thought (CoT) approach using manually collected datasets. The results demonstrate significant improvements in accuracy and contextual relevance, with our approach achieving up to 90\% accuracy and high GPT-4 scores. Additional validation through real-world pilot studies further confirmed the practical benefits of our method, highlighting its potential to transform agricultural practices and decision-making.