农业大数据研究发展（2025）

doi:10.19788/j.issn.2096-6369.200008

摘要/Abstract

摘要：

【背景】为系统描绘2020至2024年全球农业大数据研究图景，揭示其核心趋势、热点前沿及主要参与国的差异化发展路径。【方法】本研究基于Web of Science与Scopus数据库的50,502篇文献，采用科学计量学方法，运用PhraseLDA模型聚类主题，构建复合指标遴选热点前沿，并引入四阶段成熟度框架对技术热点前沿进行系统性定位。【内容】研究发现，全球农业大数据研究正从L3（系统智能）向L4（生态协同）阶段加速演进，其核心驱动力源于技术融合的内生变革与可持续发展的外在目标升级。研究识别出三种国家级发展路径，即中国的应用驱动与全链整合模式，欧盟的政策驱动与规范引领模式，以及美国的市场驱动与前沿探索模式。【应用与价值】研究提出，全球农业大数据研究已进入以智能化、绿色化、协同化为特征的快速发展期。尽管各国路径不同，但技术融合与可持续发展已成全球共识。未来，AI大模型、气候智能型农业、开放创新生态及数字育种技术将是重塑农业大数据研究领域的关键。

关键词: 农业大数据研究, 农业大数据获取, 农业大数据分析, 农业大数据应用

Abstract:

To depict the global landscape of agricultural big data research from 2020 to 2024, this study reveals its core trends, emerging frontiers, and the differentiated development paths of key participating countries. Based on 50,502 papers from the Web of Science and Scopus databases, this research employs scientometric methods, utilizes the PhraseLDA model for topic clustering, constructs composite indicators to identify emerging frontiers, and introduces a four-stage maturity framework to systematically position technological frontiers. The study finds that global agricultural big data research is accelerating its evolution from Level 3 (System Intelligence) to Level 4 (Ecological Synergy), driven by both endogenous technological convergence and the external goal of sustainable development. Three national development models are identified: China's application-driven and whole-chain integration model, the EU's policy-driven and standard-led model, and the U.S.'s market-driven and frontier exploration model. The study indicates that global agricultural big data research has entered a rapid development phase characterized by intelligence, green innovation, and synergy. Despite differing national approaches, technological convergence and sustainable development have become global consensus. Future priorities include AI large models, climate-smart agriculture, open innovation ecosystems, and digital breeding, which will reshape the field of agricultural big data research.

Key words: agricultural big data research, agricultural big data acquisition, agricultural big data analysis, agricultural big data application

吴蕾, 马晓敏, 孙巍, 张学福. 农业大数据研究发展（2025）[J]. 农业大数据学报, 2026, 8(1): 1-18.

WU Lei, MA XiaoMin, SUN Wei, ZHANG XueFu. Progress of Agricultural Big Data Research (2025)[J]. Journal of Agricultural Big Data, 2026, 8(1): 1-18.

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评估维度	内容说明
技术路径	衡量技术是作为孤立工具被应用，还是已集成为复杂的系统解决方案。
数据融合	评估数据源的复杂性和异构性，从单一数据源到跨领域、跨价值链数据的全面融合。
分析深度	评价数据分析方法的复杂程度，从基础的描述性统计到由人工智能驱动的认知分析。
应用目标	界定研究旨在解决的问题层次，从提高单一环节的生产效率到实现整个农业生态系统的可持续与韧性发展。

成熟度等级	成熟度等级名称	内容说明
L1	单点应用	聚焦单一技术解决特定问题，数据源单一，分析方法以描述性为主。
L2	流程优化	多技术、多数据源融合，分析扩展至诊断性和预测性分析，目标是优化完整流程。
L3	系统智能	以AI为核心，实现多源异构数据实时融合与分析，具备规范性决策能力，构建能自主学习的智能系统。
L4	生态协同	整合市场、政策、气候等多维度数据，分析达认知层面，目标是优化整个农业价值链的效率、韧性与可持续性。

热点前沿名称	专家研判结果	论文总数（篇）	研究主题热度综合指标	新颖性
基于多模态遥感数据的作物识别研究	重点前沿	18	0.81	2022.10
无人机AI植物表型与病虫害诊断研究	前沿	212	0.81	2022.00
智慧农业感知互联与智能决策系统研究	重点热点	724	0.87	2022.50
遥感机器学习土壤碳固存研究	热点	107	0.87	2022.50
基于遥感与机器学习的农田干旱监测研究	热点	184	0.85	2022.30
AI驱动的农业遥感监测预测与智能制图研究	热点	735	0.86	2022.20
农业土壤环境监测与污染评估研究	热点	105	0.83	2022.20

热点前沿名称	专家研判结果	论文总数（篇）	研究主题热度综合指标	新颖性
机器学习在智能农业中的集成应用研究	重点前沿	4	0.69	2022.00
农田土壤特性智能分析与遥感监测研究	前沿	29	0.82	2022.90
绿色智能农田遥感监测与可持续发展研究	重点热点	377	0.83	2022.30
作物表型监测与农业智能生产决策研究	热点	172	0.84	2022.40
气候智能型农业系统韧性与资源优化管理	热点	297	0.76	2022.40
作物病虫害智能识别与精准防治研究	热点	305	0.73	2022.40
智能农业管理与粮食安全保障研究	热点	336	0.78	2022.80

热点前沿名称	专家研判结果	论文总数（篇）	研究主题热度综合指标	新颖性
农业生态环境监测评估研究	重点前沿	146	0.79	2022.50
农业水肥资源智慧管理	前沿	132	0.69	2022.80
基于遥感大数据的农作物长势监测预测研究	重点热点	207	0.85	2022.20
农业绿色生产效率提升研究	热点	113	0.81	2022.70
区块链农产品质量追溯研究	热点	117	0.81	2022.10
智慧农业系统精准化管理	热点	165	0.77	2022.40
农业经济韧性与数字治理研究	热点	115	0.80	2022.10
区域农业碳排放智能监测研究	热点	142	0.78	2022.50
气候变化适应与农业转型研究	热点	176	0.80	2022.30
智慧农业智能装备与技术集成应用研究	热点	549	0.79	2022.70
深度学习驱动的作物病虫害智能决策与防控研究	热点	436	0.74	2022.40