农业工程学报2026，Vol.42Issue(14)：1-15,15.DOI:10.11975/j.issn.1002-6819.202602116
农作物生产大数据管理与服务平台关键技术及研究进展
Big data management and service platform for crop production:key technologies and research progress
摘要
Abstract
Agricultural big data is one of the most important driving elements in smart agriculture.Its service platforms can serve as the primary carriers of big data.This study aims to review agricultural big data and service platforms of crop production as representative scenario.(1)The key technical architecture of these platforms was summarized to classify five modules:a)Data acquisition and transmission with Internet of Things(IoT)sensors,unmanned aerial vehicles(UAVs),and satellite remote sensing;b)Data storage and preprocessing for massive throughput and multi-source heterogeneity;c)Data security and privacy protection using federated learning and cryptographic techniques;d)Data mining and modeling using machine learning and deep learning algorithms;e)Data visualization and decision-making that displays data via charts,maps,and other visual means,and supports farmers' planting decisions with machine learning algorithms.A single mode evolved into differentiated systems for perception,data transmission,computation,and decision-making tailored to various agricultural scenarios,such as field,greenhouse,and orchard.For instance,field applications prioritize large-scale monitoring over extensive areas and medium-to-long-term early warning;greenhouses focus on minute-level real-time control in enclosed spaces;While orchards emphasize precision management of individual fruit trees.(2)Current situations were reviewed.Developed countries in Europe and North America also established an integrated equipment-data-service ecosystem for data-driven services throughout the entire agricultural production.Whereas,in China,despite the progress made in multiple provinces,the challenges remained,including rural infrastructure,weak independent innovation in key technologies,fragmented data resources between different institutions,and a shortage of interdisciplinary talent.(3)According to current research status,the differences between agricultural big data platforms were proposed in six dimensions:a)In terms of federated sharing,AgReFed platform abroad adhered to FAIR principles for cross-institutional interoperability,while China Qilu Agricultural Cloud relied on an administrative framework in the entire region,but lacking in standards and severe data silos;b)Regarding to personalized services,New Zealand's AgYields system was mature and versatile,whereas Zhejiang's platform focused on specialty crops,but its mode was difficult to replicate;c)In multi-dimensional collaboration,Spain's Farmdata boasted a well-established five-party coordination mechanism,while Sinochem MAP integrated full-chain resources,but lacking in the participation from market entities;d)In precision farming,Germany's xarvio technology was maturely implemented with a complete commercial ecosystem,while unmanned farms in South China significantly increased yields,though mostly as demonstration projects;e)In intelligent decision-making,America's Climate FieldView established a closed-loop data-driven decision service,while Shandong's smart farms achieved in intelligent management,but heavily dependent on core technologies;f)In intelligent management,America's John Deere exhibited high equipment integration,and Jilin's"Jinnong Cloud"monitoring system was robust,but prioritized presentation over practical effectiveness.(4)Finally,the existing agricultural big data and service platforms were analyzed to propose recommendations from the perspectives of policy,technology,management,promotion,application,and talent cultivation.This finding can provide a strong reference to promote the iterative optimization of big data and service platforms in sustainable agriculture.关键词
农业大数据/智慧农业/农作物生产/大数据管理与服务平台/数据挖掘与建模Key words
agricultural big data/smart agriculture/crop production/big data management and service platform/data mining and modeling分类
农业科技引用本文复制引用
廖娟,陈华静,何松炜,唐赛欧,陈兴欢,陈汉文,王辉,曾浩求,罗锡文..农作物生产大数据管理与服务平台关键技术及研究进展[J].农业工程学报,2026,42(14):1-15,15.基金项目
岭南现代农业科学与技术广东省实验室科研项目(NT2025006) （NT2025006）
国家重点研发计划项目(2022YFD2002400) （2022YFD2002400）
国家自然科学基金项目(31901401) （31901401）
