随着农业生产、管理、经营和服务进入大数据时代，标准化工作的重要性日益体现。为了保障多种设备和仪器采集结果间的可比性、多源数据间的可兼容性、多类数据分析系统间的可集成性以及全球农业产品的质量、不同生产和经营过程之间的连贯性，都需要有相应的标准作为保障。本文综述了大数据科学领域和农业领域的国内外标准制定的现状，指出目前能够直接指导农业大数据发展的标准和规范较少，农业大数据领域标准缺乏，亟需开展标准体系框架研究，以保障和促进农业大数据应用的不断深入发展。本文借鉴了国际标准化组织/国际电工技术委员会提出的从部门视角、信息视角、计算视角、工程视角、技术视角等五个方面开展标准体系框架分析的方法，从信息视角和计算视角出发，分析了农业大数据标准化的需求，提出了农业大数据标准体系框架。该框架包含指导标准、通用标准和应用标准三个层次。其中，农业大数据指导标准是农业大数据标准制定和协调的依据，包括国家大数据相关法律、法规、政策，以及大数据相关国家标准；农业大数据通用标准包括4个类别的农业领域通用性的标准，即农业大数据基础标准、农业大数据采集处理标准、农业大数据管理标准和农业大数据共享服务标准；农业大数据应用标准是对农业要素和权属信息、农业生产过程、农业经营、农业管理等农业大数据全过程中的特定环节制定的标准规范。农业大数据标准体系顶层设计工作是一项复杂而庞大的系统工程，需要多部门、多学科人员参与，是农业大数据领域的未来工作重点。

As agricultural production, management, operations, and services enter the big data era, standardization is becoming increasingly important in ensuring the comparability between the collected results generated by diverse devices and instruments, compatibility between multi-source data, integrability between multiple types of data analysis systems, the quality of agricultural products at a global scale, and the coherence between different production and management processes. This article summarizes the domestic and foreign standards in the field of agricultural big data. There are currently few standards and norms that can directly guide the development of agricultural big data. Research studies for a big data standards framework are critically needed to guarantee and promote the continuous and in-depth development of agricultural big data applications. This paper draws on the methods proposed by the International Organization for Standardization and International Electrotechnical Commission to analyze the standard system for a framework from the perspectives of enterprice, information, computation, engineering, and technology. From informational and computational perspectives, the needs in developing agricultural big data standards were analyzed, and a framework for an agricultural big data standard system is proposed. This framework contains standards for fundamental guidelines, general practice, and applications. Among these needs, the fundamental guidelines for agricultural big data are the basis for the formulation and coordination of agricultural big data standards, including national big data-related laws, regulations, policies, and national standards related to big data. The general standards for agricultural big data include four categories of common agricultural standards: i.e., agricultural big data foundation, agricultural big data collection and processing, agricultural big data management, and agricultural big data-sharing services. The agricultural big data application standards are the agriculture standards and regulations formulated for specific parts of the whole process of agricultural big data, such as agricultural elements and ownership information, agricultural production processes, and agricultural operation and management. The design of the agricultural big data standard system is a complex and huge systematic project that requires the participation of multisector and multidisciplinary personnel, and it is one of the top priorities in agricultural big data development.