FruitQA-Sys： an expert question-answering system for fruit and vegetable domain

LIU Qi; WANG Haoran; FANG Wanru; XU Juan; WEI Xiaomei

doi:10.13300/j.cnki.hnlkxb.2026.03.006

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FruitQA-Sys： an expert question-answering system for fruit and vegetable domain

Agricultural artificial intelligence | 更新时间：2026-11-05

- FruitQA-Sys： an expert question-answering system for fruit and vegetable domain
- Journal of Huazhong Agricultural University Vol. 45, Issue 3, Pages: 68-76(2026)
- 作者机构：
  
  1.华中农业大学信息学院，武汉430070
  2.果蔬园艺作物种质创新与利用全国重点实验室，武汉430070
  3.农业智能技术教育部工程研究中心，武汉430070
- 作者简介：
- 基金信息：
- DOI：10.13300/j.cnki.hnlkxb.2026.03.006
  CLC： S126
- Received：30 September 2025，
  
  Published：25 May 2026
- 稿件说明：
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刘琪，王皓冉，方婉茹，等.FruitQA-Sys：面向农业果蔬领域的专家问答系统［J］.华中农业大学学报，2026，45（3）：68‐76.

LIU Qi,WANG Haoran,FANG Wanru,et al.FruitQA-Sys： an expert question-answering system for fruit and vegetable domain[J].Journal of Huazhong Agricultural University,2026,45(03):68-76.
刘琪，王皓冉，方婉茹，等.FruitQA-Sys：面向农业果蔬领域的专家问答系统［J］.华中农业大学学报，2026，45（3）：68‐76. DOI： 10.13300/j.cnki.hnlkxb.2026.03.006.

LIU Qi,WANG Haoran,FANG Wanru,et al.FruitQA-Sys： an expert question-answering system for fruit and vegetable domain[J].Journal of Huazhong Agricultural University,2026,45(03):68-76. DOI： 10.13300/j.cnki.hnlkxb.2026.03.006.

摘要

为解决现有大语言模型在农业果蔬种植等专业细分场景中知识覆盖不足、难以满足实际智能化应用需求的问题，采用领域数据构建、参数高效微调与检索增强生成相结合的方法，提出了一种面向农业果蔬领域的专家问答系统FruitQA-Sys。该系统通过生成器-打分器模块构建大规模高质量果蔬问答数据集，基于LoRA策略对领域大模型FruitBOT进行微调，并结合引导式检索增强生成技术IntroRAG，实现果蔬专业知识的精准问答。结果显示，在自动评估中，FruitQA-Sys的BERTScore为0.76，较DeepSeek-V3提升1.3%，与Qwen-3.0持平； GLEU为0.37，较DeepSeek-V3和Qwen-3.0分别提升2.7%；在人工评估中，系统综合得分达到8.56，较DeepSeek-V3和Qwen-3.0分别提升11.5%和15.7%。结果表明，领域微调大模型与IntroRAG的协同机制能够有效提升果蔬种植场景下智能问答系统的专业性与准确性。

Abstract

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.

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references

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