# LLMs for Enhanced Agricultural Meteorological Recommendations

arXiv: https://arxiv.org/abs/2408.04640 · HTML: https://mlsanity.com/paper/2408.04640/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 20 references that are in the map.
- First posted on arXiv 30 Jul 2024.

## Builds on (most cited first, 20 of 20)

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- [Farmer's Assistant: A Machine Learning Based Application for Agricultural Solutions](https://mlsanity.com/paper/2204.11340/) (2022)
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## Abstract (arXiv)

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.