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CAMAGRI-GPT:A parameter-efficient agricultural knowledge system using domain-adapted large language models with retrieval augmentation

Qianchuan Li Xin Dai Feng Yu Junsheng Dai Shengwei Wang Rupeng Luan Xiaowei Wang Xiaojing Qin Shiwei Xu

International Journal of Agricultural and Biological Engineering2026，Vol.19Issue(2)：P.245-261,17.

International Journal of Agricultural and Biological Engineering2026，Vol.19Issue(2)：P.245-261,17.DOI:10.25165/j.ijabe.20261902.9671

CAMAGRI-GPT:A parameter-efficient agricultural knowledge system using domain-adapted large language models with retrieval augmentation

Qianchuan Li ¹Xin Dai ²Feng Yu ¹Junsheng Dai ³Shengwei Wang ²Rupeng Luan ¹Xiaowei Wang ⁴Xiaojing Qin ¹Shiwei Xu⁵

作者信息

1. Institute of Data Science and Agricultural Economics,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China Key Laboratory of Intelligent Seedling Technology Innovation,Ministry of Agriculture and Rural Affairs,Beijing 100097,China
2. Agricultural Information Institute,Chinese Academy of Agricultural Sciences,Beijing 100081,China
3. Xinjiang Academy of Agricultural Sciences,Urumqi 830091,China
4. Institute of Agricultural Information,Xinjiang Academy of Agricultural Sciences,Urumqi 830091,China
5. Agricultural Information Institute,Chinese Academy of Agricultural Sciences,Beijing 100081,China Key Laboratory of Agricultural Monitoring and Early Warning,Ministry of Agriculture and Rural Affairs,Beijing 100081,China
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摘要

关键词

agricultural large language model/CAMAGRI-GPT/domain adaptation/retrieval-augmented generation/knowledge dissemination/parameter-efficient fine-tuning

分类

农业科技

引用本文复制引用

Qianchuan Li,Xin Dai,Feng Yu,Junsheng Dai,Shengwei Wang,Rupeng Luan,Xiaowei Wang,Xiaojing Qin,Shiwei Xu..CAMAGRI-GPT:A parameter-efficient agricultural knowledge system using domain-adapted large language models with retrieval augmentation[J].International Journal of Agricultural and Biological Engineering,2026,19(2):P.245-261,17.

基金项目

supported by the National Key Research and Development Program of China(Grant No.2023YFD2000105) （Grant No.2023YFD2000105）

the Beijing Academy of Agriculture and Forestry Sciences Reform and Development Program“Research and Application of Agricultural Data Science Technology”(Grant No.GGFZSJS2026)。（Grant No.GGFZSJS2026）

International Journal of Agricultural and Biological Engineering

ISSN：1934-6344

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