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作 者:王儒敬[1,2]
机构地区:[1]中国科学院合肥智能机械研究所 [2]中国科学院中国新农村信息化研究中心,合肥230031
出 处:《中国科学院院刊》2013年第3期337-343,共7页Bulletin of Chinese Academy of Sciences
基 金:国家自然基金项目(31171456);国家科技支撑计划(2013BAD15B03);国家科技支撑计划(2012BAH20B02)
摘 要:农业信息化发展的核心是信息服务,信息服务的重大需求是个性化、专业化与低成本、可持续。以农业为应用背景、农村为实施环境、农民为服务对象的我国农业信息化发展面临的困难与瓶颈问题是:农业生产环境多变,信息要素难以大面积、低成本、快速准确地获取;农业生产过程分散,异质、异构、海量、分布式大数据处理技术缺失;农业生产主体复杂,需求千变万化主动扑捉困难。对此提出如下应对策略:集中我国优势力量突破农业传感器核心技术,打破国外封锁形成系列装置、装备与仪器仪表;加快云计算与大数据处理技术研究与农业应用,开发农业大数据库管理系统、农业大知识库管理系统等基础软件系统,实现数据处理的智能化;在国际数据资源描述标准框架下,建立我国农业信息资源描述标准、数据资源定价协议,实现数据资源建设的标准化、市场化与可持续发展。The kemel of agricultural informatization is information service which should satisfy the needs of specialization, individualization, low cost, and sustainability. The difficulties and bottleneck in agricultural in- formatization mainly include the following aspects. At first, because of complex agricultural production envi- ronment, it is difficult to acquire agricultural production information rapidly and precisely at a low cost in large area. Secondly, due to the highly dispersed agricultural production process, the current information technology is not sufficient to cope with large scale, heterogeneous, and distributed agricultural big data. Finally, it is hard to capture millions of different agricultural users' information needs. Therefore, to challenge the above prob- lems we are facing, we proposed several coping strategies. Firstly, concentrate efforts to break through the key technologies of agricultural sensors and develop a series of related equipments, breaking up the monopoly of foreign countries. Secondly, speed up the application of cloud computing and big data technologies in agricul- tural domain and develop large-scale agricultural database & knowledgebase manage systems to cope with agri- cultural data intelligently. Thirdly, based on international data resources description framework, establish agri- cultural information resources description standards and data resources pricing mechanisms to realize the stan- dard, market-based, and sustainable agricultural data resources construction.
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