LLMs for Enhanced Agricultural Meteorological Recommendations
<|reference_start|>LLMs for Enhanced Agricultural Meteorological Recommendations: 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.<|reference_end|>
@article{park2024llms,
    title={LLMs for Enhanced Agricultural Meteorological Recommendations},
    author={Ji-jun Park, Soo-joon Choi},
    journal={arXiv preprint arXiv:2408.04640},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04640},
    primaryClass={cs.CL}
}