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 (LLM...
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Main Authors | , |
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Format | Journal Article |
Language | English |
Published |
30.07.2024
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.2408.04640 |