钱晔, 孙吉红, 黎斌林, 彭琳, 沈颖鸣, 沈其蓥. 大数据环境下我国智慧农业发展策略与路径[J]. 云南农业大学学报(社会科学), 2019, 13(1): 6-10. DOI: 10.3969/j.issn.1004-390X(s).2019.01.002
引用本文: 钱晔, 孙吉红, 黎斌林, 彭琳, 沈颖鸣, 沈其蓥. 大数据环境下我国智慧农业发展策略与路径[J]. 云南农业大学学报(社会科学), 2019, 13(1): 6-10. DOI: 10.3969/j.issn.1004-390X(s).2019.01.002
QIAN Ye, SUN Jihong, LI Binlin, PENG Lin, SHENG Yingming, SHENG Qiying. Development Strategy and Path of Intelligent Agriculture in China under Big Data Environment[J]. Journal of Yunnan Agricultural University (Social Science), 2019, 13(1): 6-10. DOI: 10.3969/j.issn.1004-390X(s).2019.01.002
Citation: QIAN Ye, SUN Jihong, LI Binlin, PENG Lin, SHENG Yingming, SHENG Qiying. Development Strategy and Path of Intelligent Agriculture in China under Big Data Environment[J]. Journal of Yunnan Agricultural University (Social Science), 2019, 13(1): 6-10. DOI: 10.3969/j.issn.1004-390X(s).2019.01.002

大数据环境下我国智慧农业发展策略与路径

Development Strategy and Path of Intelligent Agriculture in China under Big Data Environment

  • 摘要: 农业作为党和国家历来重视的重点工作之一,是关系社会长治久安的关键所在,是社会和谐发展的基础。传统农业生产由于生产技术落后,缺少数字化、信息化、智能化的发展,导致农业信息化进程发展缓慢,严重阻碍了农民致富,企业发展壮大。因此,对农业生产模式进行改革势在必行。首先,依靠政府行政职能,收集整理农作物的相关数据,构建基于智能算法、嵌入式的多种预测(预警)模型,将模型融入管理信息系统中,并寄托于云平台上,为种植户、企业提供某种或某几种农作物来年的预测价格、相应病虫害爆发的时间等信息,实现信息收费低、信息质量高的智能化服务。

     

    Abstract: Agriculture, as one of the key tasks of the party and the state, is not only the key to the long-term stability of society, but also the foundation of harmonious social development.Due to the backward production technology, the traditional agricultural production lacks the digital, information and intelligent development, which leads to the slow development of the agricultural information process, which seriously hinders the farmers from becoming rich and the enterprises are developing and expanding. Therefore, it is imperative to reform the mode of agricultural production. Firstly, rely on the administrative functions of the government to collect and collate relevant data of crops. Secondly, a multi prediction (early warning) model based on intelligent algorithm and embedded system is built to integrate the model into the management information system, and place it on the cloud platform. Finally, it provides information for farmers or enterprises to predict the price of some or some crops in the coming year, and the time of outbreak of corresponding pests and diseases, and realizing intelligent service with low information charges and high information quality.

     

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