双语话农:农业大数据

农业大数据是通过物联网、遥感等技术采集农业生产、经营、管理等环节的海量数据集合，具有规模大、类型多、价值密度低、处理速度快、精确度高和复杂度高等特征。

Agricultural big data refers to the massive collection of data from agricultural production, operation, management and other links collected through technologies such as the Internet of Things and remote sensing. It is characterized by large scale, diverse types, low value density, fast processing speed, high accuracy and high complexity.

含义

Implication

农业大数据具有快速化、多样化、精确化和复杂性等特征，并保留了大数据自身的规模巨大、类型多样、价值密度低、处理速度快、精确度高和复杂度高等基本特征。其在农业领域涵盖农业生产、管理、经营等各项数据信息，包括生产信息、技术信息、市场信息和国家政策法规信息等 。农业大数据的作用主要包括提高农业生产效率，通过实时监测和数据分析优化生产管理；降低生产成本，通过精准种植和施肥减少投入；促进农业科技创新，为科研提供丰富数据资源；提升农产品安全，通过数据追溯全程监控生产过程；以及促进农业科学发展和预知涉农企业生产现状，提高竞争力 。

Agricultural big data is characterized by rapidity, diversification, precision and complexity, while retaining the basic features of big data itself, such as huge scale, diverse types, low value density, fast processing speed, high accuracy and high complexity.In the agricultural sector, agricultural big data covers various data and information related to agricultural production, management and operation, including production information, technical information, market information, national policies, laws and regulations, etc.The functions of agricultural big data mainly include improving agricultural production efficiency by optimizing production management through real-time monitoring and data analysis; reducing production costs by cutting input through precise planting and fertilization; promoting innovation in agricultural science and technology by providing abundant data resources for scientific research; enhancing the safety of agricultural products by monitoring the whole production process through data traceability; as well as boosting the scientific development of agriculture, predicting the production status of agriculture-related enterprises and improving their competitiveness.

特征

Characteristic

农业大数据的特性满足大数据的五个特性，一是数据量大、二是处理速度快、三是数据类型多、四是价值大、五是精确性高。农业大数据是融合了农业地域性、季节性、多样性、周期性等自身特征后产生的数据集合，保留了大数据自身具有的规模巨大、类型多样、价值密度低、处理速度快、精确度高和复杂度高等基本特征。

Agricultural big data conforms to the five characteristics of big data: first, large data volume; second, high processing speed; third, diverse data types; fourth, great value; fifth, high accuracy. Agricultural big data is a data set formed by integrating agricultural features such as regionality, seasonality, diversity and periodicity. It retains the basic characteristics of big data, including huge scale, diverse types, low value density, fast processing speed, high accuracy and high complexity.

影响

Influence

农业大数据与物联网、遥感等技术融合应用，有效提升了农业生产精准化与环境监测智能化水平。它整合气象、土壤、作物生长等多维数据形成科学决策，实现水肥药精准施用，降低资源消耗，重构农业生产、管理与销售模式。在经济管理方面，农业大数据推动管理智能化、多元化，发挥预防与导向作用，规避风险，支撑农业决策从经验驱动转向数据驱动。在经营环节，大数据平台打通产销信息，匹配市场需求，降低交易成本，缓解小生产与大市场的矛盾。此外，它还能消除信息不对称，助力乡村发展与基层治理精细化，推动传统农业向现代农业转型，促进农业信息化与现代化深度融合。

The integrated application of agricultural big data with technologies such as the Internet of Things and remote sensing has effectively improved the precision of agricultural production and the intelligence of environmental monitoring. It integrates multi-dimensional data including weather, soil and crop growth to form scientific decisions, realizes the precise application of water, fertilizer and pesticides, reduces resource consumption, and reconstructs agricultural production, management and sales models. In terms of economic management, agricultural big data promotes intelligent and diversified management, plays a preventive and guiding role, avoids risks, and supports the transformation of agricultural decision-making from experience-driven to data-driven. In the operation link, big data platforms connect production and marketing information, match market demand, reduce transaction costs, and alleviate the contradiction between small-scale production and a large market. In addition, it eliminates information asymmetry, facilitates rural development and refined grassroots governance, promotes the transformation of traditional agriculture to modern agriculture, and advances the deep integration of agricultural informatization and modernization.