LLMs for Enhanced Agricultural Meteorological Recommendations
It addresses the need for more accurate and relevant weather-based advice for farmers, though it appears incremental as it builds on existing LLM methods.
This paper tackled the problem of improving agricultural meteorological recommendations by using large language models and prompt engineering, achieving up to 90% accuracy and high GPT-4 scores in evaluations.
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.