A domain-specific expert question-answering system，FruitQA-Sys，was proposed by integrating domain data construction，parameter-efficient fine-tuning，and retrieval-augmented generation to solve the problems of insufficient knowledge coverage and difficulty in meeting the practical requirements of intelligent applications in specialized sub-scenarios including the planting of fruit and vegetable with existing large language models.The proposed system constructed a large-scale and high-quality fruit and vegetable question-answer dataset through a generator-scorer module，finely tuned the domain-specific large model FruitBOT based on the LoRA strategy，and incorporated the guided retrieval-augmented generation technique IntroRAG to achieve accurate question and answer of professional knowledge of fruit and vegetable.The results show that in automatic evaluation，the FruitQA-Sys achieves a BERTScore of 0.76， which is a 1.3% improvement over the DeepSeek-V3， and is on par with another mainstream general-purpose large model Qwen-3.0； its GLEU score is 0.37， representing a 2.7% improvement over both DeepSeek-V3 and Qwen-3.0. The overall score in human evaluation reached 8.56， representing improvements of 11.5% and 15.7% over DeepSeek-V3 and Qwen-3.0， respectively.It is indicated that the collaborative mechanism of domain-specific fine-tuning and IntroRAG can effectively improve the professionalism and accuracy of intelligent question-answering systems in scenarios of planting fruit and vegetable.