Traditional precision irrigation methods often suffer from high operational thresholds and adjustment costs, as well as limited optimality and generalizability, making actual deployment challenging. In response to these limitations, this study proposes an autonomous framework for agricultural irrigation decision-making using artificial intelligence (AI) based on a large language model (LLM). The framework introduces a crop-model-based simulation data generation approach, integrating agricultural knowledge base with historical data. It uses a tailored retrieval-augmented generation (RAG) strategy and, innovatively, an LLM performance evaluation system built upon the WOFOST simulation model. By combining the interpretability of the white-box WOFOST model with the flexibility of the black-box LLM, this framework achieves a balanced integration of usability, scalability, and precision. Experimental results demonstrate that the proposed method reduces the average total irrigation volume by 0.24 cm compared to conventional irrigation strategies, while achieving a significantly shorter average decision time of 62 s, in contrast to 986 s required by traditional methods. It is seen that augmenting data with knowledge substantially enhances model performance and mitigates hallucination issues, with only a minimal increase in token usage and decision latency.