粮食生产大数据平台研究进展与展望

doi:10.12133/j.smartag.SA202409014

Smart Agriculture

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粮食生产大数据平台研究进展与展望

杨贵军¹^,²(), 赵春江¹, 杨小冬¹, 杨浩¹, 胡海棠¹, 龙慧灵¹, 裘正军³, 李娴⁴, 江冲亚⁵, 孙亮⁶, 陈雷⁷, 周清波⁴, 郝星耀¹, 郭威¹, 王培⁸, 高美玲²

^1. 北京市农林科学院信息技术研究中心，北京 100097，中国
^2. 长安大学地质工程与测绘学院，陕西西安 710054，中国
^3. 浙江大学生物系统工程与食品科学学院，浙江杭州 310058，中国
^4. 中国农业科学院农业信息研究所，北京 100081，中国
^5. 南京农业大学国家信息农业工程技术中心，江苏南京 210095，中国
^6. 中国农业科学院农业资源与农业区划研究所，北京 100081，中国
^7. 中国科学院合肥物质科学研究院智能机械研究所，安徽合肥 230031，中国
^8. 北京市农林科学院智能装备技术研究中心，北京 100097，中国

收稿日期:2024-09-19 出版日期:2025-01-21
基金项目:
“十四五”国家重点研发计划项目(2023YFD2000100)
通信作者:
杨贵军，博士，研究员，研究方向为农业定量遥感机理模型及应用研究。E-mail：guijun.yang@163.com

Grain Production Big Data Platform: Progress and Prospects

YANG Guijun¹^,²(), ZHAO Chunjiang¹, YANG Xiaodong¹, YANG Hao¹, HU Haitang¹, LONG Huiling¹, QIU Zhengjun³, LI Xian⁴, JIANG Chongya⁵, SUN Liang⁶, CHEN Lei⁷, ZHOU Qingbo⁴, HAO Xingyao¹, GUO Wei¹, WANG Pei⁸, GAO Meiling²

^1. Information Technology Research Center, Beijing Academy of Agriculture and Forestry Science, Beijing 100097, China
^2. College of Geological Engineering and Geomatics, Changan University, Xi'an 710054, China
^3. College of Biosystem Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
^4. Agricultural Information Institute of CAAS, Beijing 100081, China
^5. National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
^6. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
^7. Institute of intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
^8. Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

Received:2024-09-19 Online:2025-01-21
Foundation items:The National Key Research and Development Program of China(2023YFD2000100)
Corresponding author:
YANG Guijun, E-mail: guijun.yang@163.com

摘要/Abstract

摘要：

［目的/意义］ 农业大数据爆炸式发展，加速农业生产迈入数字化、智能化新时代。作为新质生产力，大数据服务于粮食生产全过程综合智能化管理决策，面临粮食生产大数据资源治理机制不明、全链条化粮食生产决策核心算法体系缺乏且对外依存度高、粮食生产全过程全要素的大数据平台缺乏等问题。 ［进展］ 本文综合分析了国内外粮食生产大数据、农情监测与智能决策算法、大数据平台方面的相关进展和面临的挑战，面向产前规划、产中监测与决策、产后综合评价等粮食生产全程管理决策需求，构建由多源异构粮食生产大数据治理、粮食生产知识图谱、“数据获取-信息提取-知识构建-智能决策-农机作业”全链条标准化算法体系、数字孪生典型应用场景等环节组成的粮食生产大数据智能平台。 ［结论/展望］ 应重点关注宏观管理监测和微观农场全程智能化生产作业需求，聚焦粮食生产典型应用场景，充分融合大数据与人工智能、数字孪生及云边端等新技术，探索技术联通集成为本，智能化服务为魂的大数据平台研发路径，创建开放式作物与环境传感接入、核心算法成熟度分级与云原生封装、高效数据与决策服务响应等为核心特色的开放共生型粮食生产大数据平台，实现数据-算法-服务全链条智能化、决策信息与智能装备作业一体化、粮食生产大数据平台与应用体系标准化，形成保障粮食安全高效绿色生产的新质生产力。

关键词: 粮食生产, 大数据平台, 农情监测, 智能算法, 决策支持, 新质生产力

Abstract:

[Significance] The explosive development of agricultural big data has accelerated agricultural production into a new era of digitalization and intelligentialize. Agricultural big data is the core element to promote agricultural modernization and the foundation of intelligent agriculture. As a new productive forces, big data enhances the comprehensive intelligent management decision-making during the whole process of grain production. But it facing the problems such as the indistinct management mechanism of grain production big data resources, the lack of the full-chain decision-making algorithm system and big data platform for the whole process and full elements of grain production. [Progress] The big data platform for grain production is a comprehensive service platform that uses modern information technologies such as big data, Internet of Things, remote sensing and cloud computing, and provides intelligent decision-making support for the whole process of grain production based on intelligent algorithms for data collection, processing, analysis and monitoring related to grain production. This paper reviews the progress and challenges in grain production big data, monitoring and decision-making algorithms, as well as big data platforms in China and worldwide. With the development of the Internet of Things and high-resolution multi-modal remote sensing technology, the massive agricultural big data generated by the "Space-Air-Ground" Integrated Agricultural Monitoring System, has laid an important foundation for smart agriculture and promoted the shift of smart agriculture from model-driven to data-driven. However, there are still some problems in field management decision-making, such as the requirements for high spatio-temporal resolution and timeliness of the information are difficult to meet, and the algorithm migration and localization methods based on big data need to be studied. In addition, the agricultural machinery operation and spatio-temporal scheduling algorithm based on remote sensing and Internet of Things monitoring information to determine the appropriate operation time window and operation prescription, needs to be further developed, especially the cross-regional scheduling algorithm of agricultural machinery for summer harvest in China. Aiming at the problems that the monitoring and decision-making algorithms of grain production are not bi-connected, and the integration of agricultural machinery and information perception is insufficient, a framework of the grain production big data intelligent platform is proposed based on digital twins. The platform is based on multi-source heterogeneous grain production big data, incorporates with the full-chain standardized algorithms including data acquisition, information extraction, knowledge map construction, intelligent decision-making, full-chain collaboration of agricultural machinery operations, involving the typical application scenarios such as irrigation, fertilization, pests and diseases, drought and flood disaster emergency response, by digital twins. [Conclusions and Prospects] The emphasis should be the requirements of monitoring at macro-level management and intelligent production at micro-level farm, fully integrating big data technology with artificial intelligence, digital twin, cloud-edge computing, and other emerging technologies. The suggestions and trend for development of grain production big data platform are summarized in three aspects. (1) Creating an open, symbiotic grain production big data platform, with core characteristics such as open interface for crop and environmental sensors, maturity grading and cloud-native packaging mechanism of the core algorithms, highly efficient response to data and decision services. (2) Focusing on the typical application scenarios of grain production, take the exploration of technology integration and bi-directional connectivity as the base, and the intelligent service as the soul of the development path for the big data platform research. (3) the data-algorithm-service self-organizing regulation mechanism, the integration of decision-making information with the intelligent equipment operation, and the standardized, compatible and open service capabilities, can form the new quality productivity forces ensuring food safety, and green efficiency grain production.

Key words: grain production, big data platform, agricultural condition monitoring, intelligent algorithm, decision support, new productive forces

中图分类号:

S126

杨贵军, 赵春江, 杨小冬, 杨浩, 胡海棠, 龙慧灵, 裘正军, 李娴, 江冲亚, 孙亮, 陈雷, 周清波, 郝星耀, 郭威, 王培, 高美玲. 粮食生产大数据平台研究进展与展望[J]. 智慧农业(中英文), doi: 10.12133/j.smartag.SA202409014.

YANG Guijun, ZHAO Chunjiang, YANG Xiaodong, YANG Hao, HU Haitang, LONG Huiling, QIU Zhengjun, LI Xian, JIANG Chongya, SUN Liang, CHEN Lei, ZHOU Qingbo, HAO Xingyao, GUO Wei, WANG Pei, GAO Meiling. Grain Production Big Data Platform: Progress and Prospects[J]. Smart Agriculture, doi: 10.12133/j.smartag.SA202409014.

图/表 7

表1

表2

国内外主要粮食生产大数据

数据类型		国内	国外
农业综合大数据		全国农业科教云平台：全国节水肥料、栽培植保、农情调度、春耕生产、市场等13类涉农信息（www.agricoop.net）；农业农村部大数据发展中心：以农业农村用地一张图中中国10亿多地块信息为基础，涵盖农情、农事、农机、农资、农技、销售和金融等方面数据（https：//www.abdc.org.cn/）	美国农业部：气象、土壤、水文、农业调查等近10大类近30年农业大数据（www.nass.usda.gov；fas.usda.gov/data）；欧盟农业部Agri-food Data Portal：作物类别、种植结构、生育期、农情、灾害监测和市场交易等方面的数据（agridata.ec.europa.eu）
农业专题大数据	农情监测数据	中国农业科学院CHARMS系统、中国科学院CropWatch系统，提供全球或全国农情监测和产量预报服务（http：//cropwatch.com.cn/）	美国CropExplorer和欧盟MARS提供全球农情监测和粮食安全评估数据（https：//ipad.fas.usda.gov/cropexplorer/； https：//joint-research-centre.ec.europa.eu/monitoring-agricultural-resources-mars_en）
	生产数据	中化MAP涵盖遥感、气象、土壤、种植、农田管理等数据，线上服务农田超667万公顷，数据规模PB级（https：//www.sinochem.com.cn/）	FBN涵盖地块、种植、气象、病虫害、市场、物流、农资等数据，汇集0.563亿公顷1 000亿个数据点农田数据和6 800个品种的信息（https：//www.fbn.com/）
	气象数据	中国气象科学数据中心大气再分析数据CMA-RA：1979年至今，10 km逐时（http：//www.nmic.cn/）	欧洲中期天气预报中心ERA5，1940年至今，0.1°逐时；美国NCEP，1948年至今，2.5°逐6小时。（https：//www.ecmwf.int/）
	农机作业数据	全国农机作业远程监测与智能管理调度平台监管全国13个省6.8万台农机，作业地块1 800万个、面积2 666万公顷，总数据55 TB	德国科乐收（CLAAS）公司研发的TELEMATICS系统能够访问机器性能参数、电子数据、作业数据，并进行远程故障维修和智能运维服务（https：//www.claas.co.uk/）
	农业知识图谱	中国农业科学院农业信息研究所构建了农业专业知识服务系统，包含2 000万条农业知识规则，其中粮食生产相关知识不足20% （https：//agri.nais.net.cn/index.html）	Google公司2020年发布了面向通用领域的包含570亿实体关系的大规模知识图谱，但农业领域知识图谱及知识挖掘的应用较少（https：//cloud.tencent.com/developer/news/716354）

表2

图1

图2

图3

图4

图5

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国内	国外
中化现代农业平台（Modern Agriculture Platform， MAP）：涵盖遥感、气象等基础数据，数据规模PB级；提供品种规划、测土配肥、定制植保、农机服务、农业金融和农用柴油供应等“7+3”服务项目，服务时效3~5天；将服务1 500多个示范农场，覆盖面积约200万公顷（https：//www.sinochem.com.cn/）	Climate FieldView（德国拜耳）：涵盖遥感、金融保险、土壤分析等70多个生产模块，覆盖生产全要素全过程大数据达EB级；基于大数据分析和决策核心算法，提供农场尺度的气象/土壤分析、病虫害诊断，精准施肥/药、农业保险、销售管理等全生产流程实时在线服务，为全球约1亿公顷农田提供服务（https：//www.climatefieldview.co.uk）
爱科农智慧种植决策系统：以自主研发的植物-土壤-大气连续体模型为基础，提供地力评估、田间环境大数据分析、农作物分布与长势分析、科学水肥管理、灾害预警、产量预测六大服务，并致力于打造了1.33万公顷爱科农试验示范基地“超级田”（https：//www.aikenong.com.cn）	xarvio数字农业服务平台（巴斯夫）：包括三个主要产品：xarvio SCOUTING， xarvio FIELD MANAGER和xarvio HEALTHY FIELDS，提供拍照识别农田和病虫害杂草、种植规划及生产决策、植保策略等服务（https：//www.basf.com/）
大疆智慧农业服务平台：提供无人机快速巡田、长势分析、农机作业指导和监管等服务（https：//ag.dji.com）	美国FBN：利用大数据技术引导农业革命，创新独特的基于农业大数据的商业模式，提供农田管理决策服务和农资及农产品营销渠道和金融渠道（https：//www.fbn.com/）

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[2]	郭威, 吴华瑞, 郭旺, 顾静秋, 朱华吉. 特色农产品设施环境下品质智能管控技术研究现状与展望[J]. 智慧农业(中英文), 2024, 6(6): 44-62.
[3]	曹冰雪, 李鸿飞, 赵春江, 李瑾. 智慧农业科技创新引领农业新质生产力发展路径[J]. 智慧农业(中英文), 2024, 6(4): 116-127.
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粮食生产大数据平台研究进展与展望

Grain Production Big Data Platform: Progress and Prospects

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