智慧农业（中英文）2024，Vol.6Issue(2)：1-13,13.DOI:10.12133/j.smartag.SA202403015
农业大模型:关键技术、应用分析与发展方向
Big Models in Agriculture:Key Technologies,Application and Future Directions
摘要
Abstract
[Significance]Big Models,or Foundation Models,have offered a new paradigm in smart agriculture.These models,built on the Trans-former architecture,incorporate numerous parameters and have undergone extensive training,often showing excellent performance and adaptability,making them effective in addressing agricultural issues where data is limited.Integrating big models in agriculture promises to pave the way for a more comprehensive form of agricultural intelligence,capable of processing diverse inputs,making in-formed decisions,and potentially overseeing entire farming systems autonomously. [Progress]The fundamental concepts and core technologies of big models are initially elaborated from five aspects:the generation and core principles of the Transformer architecture,scaling laws of extending big models,large-scale self-supervised learning,the general capabilities and adaptions of big models,and the emerging capabilities of big models.Subsequently,the possible application scenarios of the big model in the agricultural field are analyzed in detail,the development status of big models is described based on three types of the models:Large language models(LLMs),large vision models(LVMs),and large multi-modal models(LMMs).The progress of applying big models in agriculture is discussed,and the achievements are presented. [Conclusions and Prospects]The challenges and key tasks of applying big models technology in agriculture are analyzed.Firstly,the current datasets used for agricultural big models are somewhat limited,and the process of constructing these datasets can be both ex-pensive and potentially problematic in terms of copyright issues.There is a call for creating more extensive,more openly accessible datasets to facilitate future advancements.Secondly,the complexity of big models,due to their extensive parameter counts,poses sig-nificant challenges in terms of training and deployment.However,there is optimism that future methodological improvements will streamline these processes by optimizing memory and computational efficiency,thereby enhancing the performance of big models in agriculture.Thirdly,these advanced models demonstrate strong proficiency in analyzing image and text data,suggesting potential fu-ture applications in integrating real-time data from IoT devices and the Internet to make informed decisions,manage multi-modal data,and potentially operate machinery within autonomous agricultural systems.Finally,the dissemination and implementation of these big models in the public agricultural sphere are deemed crucial.The public availability of these models is expected to refine their capabili-ties through user feedback and alleviate the workload on humans by providing sophisticated and accurate agricultural advice,which could revolutionize agricultural practices.关键词
生成式人工智能/大模型/农业知识服务/机器学习/自主决策/多模态/深度学习Key words
artificial intelligence generated content(AIGC)/big models/agricultural knowledge services/machine learning/auton-omous decision-making/multi-modality/deep learning分类
信息技术与安全科学引用本文复制引用
郭旺,杨雨森,吴华瑞,朱华吉,缪祎晟,顾静秋..农业大模型:关键技术、应用分析与发展方向[J].智慧农业（中英文）,2024,6(2):1-13,13.基金项目
科技创新2030"新一代人工智能"重大项目(2021ZD0113604) （2021ZD0113604）
财政部和农业农村部:国家现代农业产业技术体系资助(CARS-23-D07) （CARS-23-D07）
北京市农林科学院创新能力建设项目(KJCX20230219) Innovation 2030 Major S&T Projects of China(2021ZD0113604) （KJCX20230219）
China Agriculture Research System of MOF and MARA Grant(CARS-23-D07) （CARS-23-D07）
Beijing Academy of Agricultural and Forestry Sciences:Innovation Capacity Building Project(KJCX20230219) （KJCX20230219）
