LLMs for Enhanced Agricultural Meteorological Recommendations

TL;DR

The paper tackles the challenge of delivering accurate, actionable agricultural meteorological recommendations by integrating weather forecasts, soil data, and crop information. It introduces a multi-round prompt engineering framework that leverages large language models (GPT-4, Claude2, ChatGPT) to iteratively refine guidance. Empirical results from manually collected data and real-world pilots show substantial gains in accuracy (up to 90%) and high GPT-4 scoring, outperforming single-round and chain-of-thought baselines. The work demonstrates the practical potential of AI-driven, context-aware decision support for farmers and sets the stage for broader adoption across crops and regions.

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.