[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3781":3,"related-3781":108},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":81,"slug":84,"view_count":85,"doi":86,"paper":87,"created_at":107},3781,"A Big Data Curation Framework for Precision Agriculture: Actions, Actors and Challenges in the Indian Context","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fajaar\u002F2026\u002Fv26i10763","Precision agriculture produces large volumes of heterogeneous data from IoT sensor networks, satellite imagery, unmanned aerial surveys, crop registries and weather stations. India has committed substantial public investment to agricultural data infrastructure under the Digital Agriculture Mission, which by August 2026 had generated more than 10.31 crore Farmer IDs and surveyed over 31 crore plots. No published framework specifies what curation these data require, which institutions are responsible for each activity, or what obstacles arise in the Indian operating environment. This study develops such a framework through a systematic review of 214 sources published between January 2015 and August 2026, drawn from five bibliographic databases and supplemented by Government of India policy documents. Two coders agreed on 88.5 per cent of coding decisions for a random subsample of 50 records. The framework specifies curation actions at eight stages of the agricultural data lifecycle, assigns each action to one or more of nine institutional actor categories, and documents the associated challenges. Three findings distinguish precision agriculture from the general big data case: raster imagery requires curation procedures with no counterpart in text or sensor-stream data; machine-learning models function as curation objects rather than analytical tools alone; and metadata operating only in English cannot support a national agricultural data system in a country with 22 scheduled languages, a requirement now given legal force by the Digital Personal Data Protection Rules 2025. Those Rules, notified in November 2025, impose consent, erasure and breach-notification obligations that apply across the full data lifecycle rather than at the point of collection alone. The framework gives agricultural universities, ICAR institutes and state departments a basis for assigning data management roles and planning curation training. Because it rests on published literature and policy documents, it has not yet been tested against current institutional practice.","精准农业通过物联网传感器网络、卫星影像、无人机航测、作物登记系统和气象站产生大量异构数据。印度根据“数字农业使命”对农业数据基础设施投入了大量公共资金，截至2026年8月，已生成超过1031万个农民ID，并调查了超过3100万块地块。目前尚无已发表的框架明确规定这些数据需要何种数据策管、各项活动由哪些机构负责，或在印度运行环境中会出现哪些障碍。本研究通过对2015年1月至2026年8月间发表的214份文献进行系统综述，构建了这样一个框架，文献取自五个书目数据库，并辅以印度政府政策文件。两名编码员对随机抽取的50条记录子样本的编码决定一致率为88.5%。该框架明确了农业数据生命周期八个阶段的数据策管行动，将每项行动分配给九类机构行动者类别中的一个或多个，并记录了相关挑战。三项发现将精准农业与一般大数据情形区分开来：栅格影像所需的策管程序在文本或传感器流数据中没有对应物；机器学习模型作为策管对象发挥作用，而不仅仅是分析工具；仅以英语运行的元数据无法在一个拥有22种表列语言的国家支撑国家农业数据系统，而2025年《数字个人数据保护规则》现已赋予这一要求法律效力。该规则于2025年11月通告，规定了同意、删除和违规通知义务，这些义务适用于整个数据生命周期，而不仅限于收集环节。该框架为农业院校、ICAR研究所和各邦部门分配数据管理角色和规划策管培训提供了依据。由于该框架基于已发表文献和政策文件，尚未针对当前机构实践进行检验。",null,"Asian Journal of Advances in Agricultural Research","2026-09-28T00:00:00Z","论文",10,false,84,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,23,18,13,8,1,"系统综述214篇文献构建印度精准农业数据策管框架，提出栅格影像、机器学习模型与多语言元数据三大特殊性，对农业数据治理有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字农业","智慧农业","农业大数据","数据治理","印度农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":48},[45,55,62,69,76],{"code":46,"score":47,"supported":48,"title":49,"abstract":51,"tags":54},"I1",19,true,[50],"precision agriculture",[52,27,50,53],"精准农业","digital agriculture",[27,28],{"code":33,"score":56,"supported":48,"title":57,"abstract":58,"tags":61},6,[],[59,60],"物联网","IoT",[],{"code":63,"score":64,"supported":48,"title":65,"abstract":66,"tags":68},"I6",5,[],[67],"agricultural data",[29,30],{"code":70,"score":71,"supported":48,"title":72,"abstract":73,"tags":75},"I2",3,[],[74],"机器学习",[],{"code":77,"score":22,"supported":14,"title":78,"abstract":79,"tags":80},"J3",[],[],[31],[82,83],"印度 数字农业使命 农民ID","精准农业 数据策管 框架","印度数字农业使命农民ID-3781",2,"10.9734\u002Fajaar\u002F2026\u002Fv26i10763",{"doi":86,"openalex_id":88,"authors":89,"venue":10,"cited_by_count":98,"oa_url":6,"card":99,"direction":105,"ingested_from":106},"W7214617849",[90,93,96],{"name":91,"orcid":92},"Suresh Kumar Sharma","https:\u002F\u002Forcid.org\u002F0000-0002-5054-6170",{"name":94,"orcid":95},"Parul Dhull","https:\u002F\u002Forcid.org\u002F0009-0005-8368-3058",{"name":97,"orcid":9},"Pratibha Manohar",0,{"tldr":100,"method":101,"finding":102,"direction":103,"opportunity":104},"通过系统综述构建印度精准农业大数据治理框架，明确8阶段数据策展行动、9类机构角色及挑战。","系统综述214篇文献与印度政策文件，双编码者一致性88.5%。","栅格影像、机器学习模型作为策展对象、多语言元数据是精准农业区别于一般大数据的三大特征。","数字乡村与农业信息化","可实证检验该框架在印度机构实践中的适用性，并探索多语言元数据与数据保护法规的落地机制。","智慧农业 \u002F 农业物联网","openalex","2026-09-29T23:30:15.187320Z",{"total":56,"page":22,"page_size":56,"items":109},[110,193,261,376,432,483],{"id":111,"title":112,"url":113,"summary":114,"summary_zh":115,"content":9,"source_name":116,"source_url":113,"published_at":117,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":118,"score_detail":119,"sources":124,"tags":126,"primary_topic":130,"secondary_topics":131,"classification":132,"search_phrases":173,"slug":176,"view_count":22,"doi":177,"paper":178,"created_at":192},4745,"Digital Agricultural Transformation under Climate Risk: A Policy and Financing Perspective within the National Economy Model","https:\u002F\u002Fdoi.org\u002F10.24925\u002Fturjaf.v14i10.3038-3043.9028","Today, the global food system is undergoing a multidimensional transformation driven by climate change, population growth and rising food demand. Irregular precipitation regimes, increasing water scarcity and ecosystem degradation are making agricultural production increasingly unpredictable and fragile. In addition to these ecological pressures, shrinking arable land, the ageing agricultural labour force and rising input costs further challenge the sustainability of conventional farming systems. The growing frequency of extreme weather events demonstrates the vulnerability of existing production models and highlights the need for structural transformation in agriculture. Digital agriculture offers a potential solution through a technology-oriented paradigm shift. Technologies such as the Internet of Things (IoT), artificial intelligence (AI)-based decision support systems, remote sensing and precision agriculture enable site-specific resource management, reducing environmental impact while improving productivity. However, the adoption of these technologies is constrained by high investment costs and financial uncertainty, particularly for small and medium-scale producers. Therefore, integrating digital technologies into agricultural production depends on reducing financial and operational risks through effective public policy instruments. This conceptual analysis state-led strategic support mechanisms for digital agricultural transformation within the framework of the National Economy Model (NEM). The model’s production-oriented and cost-protection principles provide a stabilisation framework that may facilitate technological adoption. The study argues that public support mechanisms function not only as financial assistance but also as strategic policy instruments that enhance agricultural resilience and production continuity under climate-related risks.","当今，全球粮食体系正在气候变化、人口增长和粮食需求上升的驱动下经历多维转型。不规则的降水模式、日益加剧的水资源短缺和生态系统退化，使农业生产日益难以预测且脆弱。除这些生态压力外，耕地面积缩减、农业劳动力老龄化和投入成本上升，进一步挑战了传统农业系统的可持续性。极端天气事件日益频繁，表明现有生产模式的脆弱性，并凸显了农业结构性转型的必要性。数字农业通过技术导向的范式转变提供了潜在解决方案。物联网（IoT）、基于人工智能（AI）的决策支持系统、遥感和精准农业等技术，能够实现因地制宜的资源管理，在提高生产力的同时减少环境影响。然而，这些技术的采用受到高投资成本和财务不确定性的制约，对中小规模生产者尤为如此。因此，将数字技术融入农业生产，取决于通过有效的公共政策工具降低财务和运营风险。本概念性分析阐述了在国民经济模式（NEM）框架下，国家主导的战略支持机制对数字农业转型的作用。该模式以生产为导向和成本保护的原则提供了稳定化框架，可能促进技术采用。研究认为，公共支持机制不仅发挥财政援助功能，同时也是增强农业韧性和气候相关风险下生产连续性的战略性政策工具。","Turkish Journal of Agriculture - Food Science and Technology","2026-10-11T00:00:00Z",61,{"impact":120,"substance":121,"depth":122,"authority":120,"freshness":21,"relevant":22,"comment":123},12,15,14,"概念性论文，从政策与融资视角讨论气候风险下数字农业转型，有一定理论参考价值但缺乏实证数据，属细分领域学术探讨。",[125],{"name":116,"url":113},[27,28,127,128,129],"农业政策","农业金融","气候风险",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":133},{"candidates":134,"agricultural_evidence":48},[135,140,146,154,160,167],{"code":46,"score":136,"supported":48,"title":137,"abstract":138,"tags":139},11,[],[52,27,50,53],[27,28],{"code":33,"score":141,"supported":48,"title":142,"abstract":143,"tags":145},9,[],[59,144,60],"internet of things",[],{"code":147,"score":148,"supported":48,"title":149,"abstract":150,"tags":153},"H7",7,[],[151,152],"气候变化","climate change",[129],{"code":70,"score":56,"supported":48,"title":155,"abstract":156,"tags":159},[],[157,158],"artificial intelligence","decision support",[],{"code":161,"score":56,"supported":48,"title":162,"abstract":163,"tags":166},"I3",[],[164,165],"遥感","remote sensing",[],{"code":168,"score":71,"supported":48,"title":169,"abstract":170,"tags":172},"H3",[],[171],"水资源",[],[174,175],"数字农业 转型 气候风险","农业政策 农业金融 数字农业 智慧农业","数字农业转型气候风险-4745","10.24925\u002Fturjaf.v14i10.3038-3043.9028",{"doi":177,"openalex_id":179,"authors":180,"venue":116,"cited_by_count":98,"oa_url":113,"card":187,"direction":105,"ingested_from":106},"W7221460591",[181,184],{"name":182,"orcid":183},"Mehmet Metin Özgüven","https:\u002F\u002Forcid.org\u002F0000-0002-6421-4804",{"name":185,"orcid":186},"Saliha Kilavuz","https:\u002F\u002Forcid.org\u002F0009-0006-6830-0730",{"tldr":188,"method":189,"finding":190,"direction":103,"opportunity":191},"基于国民经济模型，从政策与融资视角分析气候风险下数字农业转型的公共支持机制。","概念分析，结合NEM框架与物联网、AI、遥感等数字农业技术。","公共支持不仅是资金援助，更是提升农业气候韧性与生产连续性的战略政策工具。","可实证检验NEM政策工具对中小农户数字技术采纳与融资约束的缓解效果。","2026-10-12T23:30:12.745005Z",{"id":194,"title":195,"url":196,"summary":197,"summary_zh":198,"content":9,"source_name":199,"source_url":196,"published_at":200,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":201,"score_detail":202,"sources":205,"tags":207,"primary_topic":211,"secondary_topics":212,"classification":213,"search_phrases":234,"slug":237,"view_count":98,"doi":238,"paper":239,"created_at":260},4663,"Sensor Data Transmission in Smart Agriculture: Challenges, Potentials, and Limitations in the Fourth Industrial Revolution","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsignals7050098","Smart agriculture is a key application domain of the Fourth Industrial Revolution (4IR), where cyber–physical systems, the Internet of Things (IoT), and data-driven analytics converge to improve productivity, sustainability, and resource efficiency. In this context, large volumes of sensor data collected in the field form the basis for automated monitoring, decision support, and adaptive control across agricultural processes. However, the value of such data depends not only on sensing technologies and connectivity, but also on how data are generated, transferred, processed, and interpreted throughout their lifecycle. This paper is organized in two parts. The first part reports a narrative review of the literature, based on a documented search protocol, and provides an overview of sensor data and sensor data transmission networks in smart agriculture. It examines the main challenges, potentials, and limitations associated with IoT-based field deployments in 4IR environments, and consolidates them into ten open problems. These problems are ordered from the device layer to the socio-economic layer and span physical robustness, device security, rural connectivity, aerial sensing, interoperability, labelled-data scarcity, explainability, federated access control, data ownership, and cost. The second part responds to these open problems with the conceptualization of a layered architecture and a set of design proposals, covering hierarchical edge security, offline-first synchronization, semantic interoperability, explainable decision support, and farmer-centric data governance, that are grounded in established techniques and standards but have not been implemented or empirically validated in this work; they are intended as a design blueprint and a starting point for future prototyping and evaluation rather than as measured results. The paper is accordingly positioned as a conceptual, perspective-oriented contribution: it states the procedure by which the open problems were derived from the reviewed works, it states what the proposed integration is claimed to add over existing smart-agriculture architectures, and it sets out an evaluation plan that fixes the metrics and the baselines against which each proposed component would have to be tested. Throughout both parts, particular attention is given to the role of data integrity and provenance as foundational elements for trustworthy agricultural data, enabling reliable analysis, auditability, and long-term reuse. By synthesizing current approaches, open issues, and conceptual solution patterns, this paper aims to support the design of resilient, transparent, and future-ready smart agriculture systems aligned with the principles of the 4IR.","智慧农业是第四次工业革命（4IR）的关键应用领域，信息物理系统、物联网（IoT）和数据驱动分析在此汇聚，以提升生产力、可持续性和资源效率。在此背景下，田间采集的大量传感器数据构成了农业过程自动化监测、决策支持和自适应控制的基础。然而，此类数据的价值不仅取决于传感技术和连接能力，还取决于数据在其生命周期中如何生成、传输、处理和解释。本文分为两部分。第一部分基于有据可查的检索方案，对文献进行叙述性综述，概述智慧农业中的传感器数据及传感器数据传输网络。它考察了4IR环境下基于物联网的田间部署所面临的主要挑战、潜力和局限，并将其归纳为十个开放性问题。这些问题从设备层到社会经济层依次排列，涵盖物理鲁棒性、设备安全、农村连接、空中传感、互操作性、标注数据稀缺、可解释性、联邦访问控制、数据所有权和成本。第二部分针对这些开放性问题，提出了分层架构的概念化和一组设计建议，涵盖分层边缘安全、离线优先同步、语义互操作性、可解释决策支持以及以农户为中心的数据治理。这些建议以已有技术和标准为基础，但尚未在本文中实现或经过实证验证；它们旨在作为设计蓝图和未来原型开发与评估的起点，而非实测结果。因此，本文定位为概念性、视角导向的贡献：它说明了开放性问题如何从所综述的工作中推导出来的过程，阐明了所提出的集成方案相对于现有智慧农业架构声称增加的贡献，并提出了一个评估计划，确定了每个所提组件必须接受测试的指标和基线。在两部分中，特别关注数据完整性和来源作为可信农业数据基础要素的作用，从而实现可靠分析、可审计性和长期复用。","Signals","2026-10-10T00:00:00Z",71,{"impact":121,"substance":19,"depth":203,"authority":20,"freshness":21,"relevant":22,"comment":204},17,"系统梳理智慧农业传感器数据传输的十大开放问题并提出分层架构蓝图，属概念性综述，有参考价值但未经实证验证。",[206],{"name":199,"url":196},[28,208,209,30,210],"农业人工智能","农业物联网","传感器网络",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":214},{"candidates":215,"agricultural_evidence":48},[216,221,225,230],{"code":46,"score":121,"supported":48,"title":217,"abstract":219,"tags":220},[218],"smart agriculture",[28,218],[28],{"code":33,"score":13,"supported":48,"title":222,"abstract":223,"tags":224},[],[59,144,60],[209],{"code":70,"score":226,"supported":48,"title":227,"abstract":228,"tags":229},4,[],[158],[208],{"code":63,"score":226,"supported":48,"title":231,"abstract":232,"tags":233},[],[67],[30],[235,236],"智慧农业 传感器数据传输","农业物联网 数据完整性","智慧农业传感器数据传输-4663","10.3390\u002Fsignals7050098",{"doi":238,"openalex_id":240,"authors":241,"venue":199,"cited_by_count":98,"oa_url":196,"card":255,"direction":105,"ingested_from":106},"W7221365687",[242,244,247,250,252],{"name":243,"orcid":9},"Alvertos Ioannis Mourikis",{"name":245,"orcid":246},"Iosif Polenakis","https:\u002F\u002Forcid.org\u002F0000-0002-6427-5519",{"name":248,"orcid":249},"Christos Sarantidis","https:\u002F\u002Forcid.org\u002F0009-0003-9486-8483",{"name":251,"orcid":9},"Romanos Kalamatianos",{"name":253,"orcid":254},"Ioannis Karydis","https:\u002F\u002Forcid.org\u002F0000-0002-9470-2729",{"tldr":256,"method":257,"finding":258,"direction":105,"opportunity":259},"综述智慧农业传感器数据传输挑战，归纳十大开放问题并提出分层架构设计蓝图。","文献综述与概念架构设计，基于搜索协议梳理IoT部署问题。","从设备到社会经济层归纳十大开放问题，提出边缘安全、语义互操作等设计提案。","所提分层架构尚未实现与实证，可开展原型验证并量化数据完整性、溯源与离线同步效果。","2026-10-11T23:30:08.094185Z",{"id":262,"title":263,"url":264,"summary":265,"summary_zh":266,"content":9,"source_name":267,"source_url":264,"published_at":268,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":269,"score_detail":270,"sources":273,"tags":275,"primary_topic":277,"secondary_topics":281,"classification":288,"search_phrases":351,"slug":354,"view_count":98,"doi":355,"paper":356,"created_at":375},4302,"BIG DATA IN AGRONOMY: TECHNOLOGIES, APPLICATIONS, BENEFITS AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.4238\u002Fk6hcgw77","Big Data is becoming an important component of modern agronomy by enabling the integration, processing and analysis of large, diverse and continuously generated datasets for improved agricultural decision-making. Agricultural Big Data is generated from multiple sources, including remote sensing satellites, Unmanned Aerial Vehicles (UAVs), Internet of Things (IoT) sensors, weather stations, farm machinery, field observations and historical databases. The integration of these data sources with Geographic Information Systems (GIS), cloud computing, Artificial Intelligence (AI) and Machine Learning (ML) enables the identification of spatial and temporal patterns that are difficult to detect using conventional approaches. Big Data applications in agronomy include climate and weather prediction, pest and disease management, soil health and nutrient management, irrigation scheduling, crop yield prediction, precision agriculture, crop insurance and weed management. Examples from India and other regions demonstrate the application of satellite imagery, UAV-based sensing, IoT systems, crop-growth models and machine-learning algorithms for field-scale and regional agricultural decision making. Big Data analytics can improve resource-use efficiency, support predictive and real-time monitoring, strengthen agricultural risk assessment and facilitate site-specific management. However, effective implementation is constrained by challenges such as high infrastructure and operational costs, data quality and interoperability issues, data privacy and ownership concerns, limited rural digital infrastructure, inadequate technical skills and increasing requirements for data storage and computing. Therefore, the effective integration of Big Data with agronomic knowledge and decision-support systems can contribute to more precise, efficient and sustainable agricultural production while addressing emerging challenges related to climate variability, resource scarcity and food security.","大数据正成为现代农学的重要组成部分，它能够整合、处理和分析大规模、多样化且持续生成的数据集，从而改进农业决策。农业大数据来源于多种渠道，包括遥感卫星、无人机（UAV）、物联网（IoT）传感器、气象站、农业机械、田间观测和历史数据库。将这些数据源与地理信息系统（GIS）、云计算、人工智能（AI）和机器学习（ML）相结合，能够识别传统方法难以发现的空间和时间模式。大数据在农学中的应用包括气候与天气预测、病虫害管理、土壤健康与养分管理、灌溉调度、作物产量预测、精准农业、作物保险和杂草管理。来自印度及其他地区的案例展示了卫星影像、基于无人机的遥感、物联网系统、作物生长模型和机器学习算法在田块尺度和区域农业决策中的应用。大数据分析可以提高资源利用效率，支持预测性和实时监测，加强农业风险评估，并促进因地制宜的管理。然而，有效实施受到诸多挑战的制约，如高昂的基础设施和运营成本、数据质量和互操作性问题、数据隐私和所有权担忧、农村数字基础设施有限、技术技能不足以及数据存储和计算需求不断增加。因此，将大数据与农学知识和决策支持系统有效整合，有助于实现更精准、高效和可持续的农业生产，同时应对气候变异性、资源稀缺和粮食安全等方面的新兴挑战。","Genetics and Molecular Research","2026-10-05T00:00:00Z",76,{"impact":19,"substance":271,"depth":203,"authority":20,"freshness":21,"relevant":22,"comment":272},20,"系统综述农业大数据技术、应用与挑战，信息密度高，对智慧农业决策有参考价值，但属综述性论文，非突破性成果。",[274],{"name":267,"url":264},[28,208,29,52,276],"遥感监测",{"code":70,"slug":278,"name":279,"path":280,"parent_code":37},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[282,287],{"code":283,"slug":284,"name":285,"path":286,"parent_code":37},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":289},{"candidates":290,"agricultural_evidence":48},[291,296,303,307,311,315,322,329,335,339,345],{"code":70,"score":13,"supported":48,"title":292,"abstract":293,"tags":295},[],[74,294,157],"machine learning",[208],{"code":283,"score":141,"supported":48,"title":297,"abstract":298,"tags":302},[],[299,300,301],"地理信息","GIS","geographic information",[],{"code":33,"score":141,"supported":48,"title":304,"abstract":305,"tags":306},[],[59,144,60],[],{"code":46,"score":21,"supported":48,"title":308,"abstract":309,"tags":310},[],[52,50],[28,52],{"code":161,"score":148,"supported":48,"title":312,"abstract":313,"tags":314},[],[164,165],[276],{"code":316,"score":56,"supported":48,"title":317,"abstract":318,"tags":321},"A8",[],[319,320],"粮食安全","food security",[],{"code":323,"score":56,"supported":48,"title":324,"abstract":325,"tags":328},"H2",[],[326,327],"土壤","soil",[],{"code":168,"score":56,"supported":48,"title":330,"abstract":331,"tags":334},[],[332,333],"灌溉","irrigation",[],{"code":63,"score":226,"supported":48,"title":336,"abstract":337,"tags":338},[],[29],[29],{"code":340,"score":71,"supported":48,"title":341,"abstract":342,"tags":344},"F3",[],[343],"养分管理",[],{"code":346,"score":71,"supported":48,"title":347,"abstract":348,"tags":350},"F5",[],[349],"农业机械",[],[352,353],"农业大数据 遥感 物联网","机器学习 作物产量预测","农业大数据遥感物联网-4302","10.4238\u002Fk6hcgw77",{"doi":355,"openalex_id":357,"authors":358,"venue":267,"cited_by_count":98,"oa_url":264,"card":369,"direction":105,"ingested_from":106},"W7220374836",[359,361,363,365,367],{"name":360,"orcid":9},"Kodavali Aswini",{"name":362,"orcid":9},"Raju Marimuthu",{"name":364,"orcid":9},"Guru Gurusamy",{"name":366,"orcid":9},"Kannan Pandian",{"name":368,"orcid":9},"Anandham Rangasamy",{"tldr":370,"method":371,"finding":372,"direction":373,"opportunity":374},"综述农学大数据的技术、应用、效益与挑战，强调多源数据融合支撑精准农业决策。","整合遥感、无人机、物联网、GIS、云计算与AI\u002FML的多源农业数据分析。","大数据可提升资源效率与实时监测，但受成本、数据质量、隐私和农村数字基建制约。","农业人工智能与决策模型","可研究低成本边缘计算与数据互操作标准，破解小农户场景下大数据落地瓶颈。","2026-10-06T23:30:11.880721Z",{"id":377,"title":378,"url":379,"summary":380,"summary_zh":381,"content":9,"source_name":382,"source_url":379,"published_at":268,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":383,"score_detail":384,"sources":387,"tags":389,"primary_topic":392,"secondary_topics":396,"classification":398,"search_phrases":415,"slug":418,"view_count":98,"doi":419,"paper":420,"created_at":431},4295,"Smart Soil Monitoring Systems: Challenges and Strategic Solutions for Enhanced Adoption","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fwjimt.v9.no12.2025.pg245.258","Smart Soil Monitoring Systems (SSMS) represent a key innovation in precision agriculture, enabling continuous monitoring of soil parameters such as moisture, pH, nutrient content, temperature, and salinity. These systems integrate Internet of Things (IoT) technologies, wireless sensor networks, and data analytics to provide real-time insights that support data-driven decision-making, optimize resource use, and enhance crop productivity. Despite their potential, the adoption of SSMS in developing institutions, including Federal Polytechnic Bali (FPB), remains limited due to financial constraints, inadequate technical skills, low awareness, and infrastructural challenges such as unreliable internet connectivity and power supply. The high cost of devices, lack of training, and insufficient institutional policies further impede practical implementation in teaching, research, and extension activities. This study employed a descriptive survey design targeting academic and technical staff in FPB’s agricultural departments. Data collected via a structured questionnaire were analyzed using descriptive statistics, with mean scores indicating key challenges and potential strategies. Findings revealed that high device costs, poor network infrastructure, and low technical capacity were the most critical barriers. To address these, respondents emphasized capacity-building workshops, improved digital infrastructure, awareness campaigns, funding support, and policy frameworks as strategic interventions. The study concludes that enhancing SSMS adoption requires integrated solutions addressing economic, technical, and institutional barriers. Implementation of these strategies is expected to strengthen hands-on learning, improve research outputs, and promote sustainable, technology-driven agriculture at FPB. The study contributes to understanding practical adoption challenges of smart agricultural technologies in resource-constrained educational settings.","智能土壤监测系统（Smart Soil Monitoring Systems, SSMS）是精准农业的一项关键创新，能够对土壤湿度、pH值、养分含量、温度和盐度等参数进行持续监测。这些系统集成了物联网（IoT）技术、无线传感器网络和数据分析，可提供实时洞察，支持数据驱动的决策、优化资源利用并提高作物生产力。尽管潜力巨大，但包括巴厘联邦理工学院（Federal Polytechnic Bali, FPB）在内的发展中机构对SSMS的采用仍然有限，原因在于资金约束、技术技能不足、认知度低以及互联网连接和电力供应不可靠等基础设施挑战。设备成本高昂、缺乏培训以及制度政策不完善，进一步阻碍了其在教学、研究和推广活动中的实际应用。本研究采用描述性调查设计，以FPB农业部门的学术和技术人员为研究对象。通过结构化问卷收集的数据采用描述性统计进行分析，以平均分表明主要挑战和潜在策略。研究发现，设备成本高、网络基础设施差和技术能力低是最关键的障碍。为应对这些问题，受访者强调能力建设研讨会、改善数字基础设施、宣传活动、资金支持和政策框架是战略性干预措施。研究得出结论，提升SSMS的采用需要综合解决方案，以应对经济、技术和制度层面的障碍。这些策略的实施有望加强实践学习、提高研究产出，并推动FPB可持续的、技术驱动的农业发展。本研究有助于理解资源受限教育环境中智能农业技术的实际采用挑战。","WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY",52,{"impact":21,"substance":385,"depth":122,"authority":56,"freshness":21,"relevant":22,"comment":386},16,"聚焦资源受限院校智慧土壤监测落地障碍的实证调查，结论实用但样本局限于单一院校，影响力有限。",[388],{"name":382,"url":379},[27,28,59,390,391],"土壤监测","农业传感器",{"code":323,"slug":327,"name":393,"path":394,"parent_code":395},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[397],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":399},{"candidates":400,"agricultural_evidence":48},[401,405,411],{"code":323,"score":122,"supported":48,"title":402,"abstract":403,"tags":404},[327],[326,327],[],{"code":33,"score":136,"supported":48,"title":406,"abstract":407,"tags":410},[],[59,408,144,60,409],"无线传感","wireless sensor",[391,59],{"code":46,"score":21,"supported":48,"title":412,"abstract":413,"tags":414},[],[52,50],[27,28],[416,417],"农业传感器 土壤监测 数字农业 智慧农业","农业传感器 土壤监测","农业传感器土壤监测数字农业智慧农业-4295","10.56201\u002Fwjimt.v9.no12.2025.pg245.258",{"doi":419,"openalex_id":421,"authors":422,"venue":382,"cited_by_count":98,"oa_url":425,"card":426,"direction":105,"ingested_from":106},"W7220426775",[423],{"name":424,"orcid":9},"NUHU ABDULLAHI","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FWJIMT\u002FVOL. 9 NO. 12 2025\u002FSmart Soil Monitoring Systems 245-258.pdf",{"tldr":427,"method":428,"finding":429,"direction":105,"opportunity":430},"调查尼日利亚某院校智能土壤监测系统采纳障碍并提出策略。","描述性问卷调研农业院系教职工，用描述统计与均值分析。","设备成本高、网络差、技术能力低是主要障碍，需培训与政策支持。","可研究资源受限院校中低成本物联网土壤监测方案与采纳机制。","2026-10-06T23:30:07.776821Z",{"id":433,"title":434,"url":435,"summary":436,"summary_zh":437,"content":9,"source_name":438,"source_url":435,"published_at":439,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":269,"score_detail":440,"sources":442,"tags":444,"primary_topic":446,"secondary_topics":447,"classification":448,"search_phrases":468,"slug":471,"view_count":22,"doi":9,"paper":472,"created_at":482},4070,"Digital Transformation of Agribusiness: Pathways Towards Smart and Sustainable Food Systems in Nordic and Indian Agricultural Contexts","https:\u002F\u002Fballisticsjournal.com\u002Findex.php\u002Fjournal\u002Farticle\u002Fview\u002F720","Digital transformation is reshaping agribusiness by integrating advanced technologies into production, processing, marketing, and supply chain management, thereby creating more efficient, resilient, and sustainable food systems. In India, increasing demand for food, climate variability, shrinking landholdings, and resource constraints have accelerated the need for smart agricultural solutions. This review examines the emerging role of digital technologies, including artificial intelligence, precision agriculture, the Internet of Things, blockchain, big data analytics, drones, and digital platforms, in modernizing agribusiness and strengthening food value chains. It synthesizes recent scientific evidence on technological innovations, adoption patterns, opportunities, barriers, and sustainability outcomes across the agricultural sector. The review highlights that digital technologies improve productivity, resource-use efficiency, traceability, market connectivity, and climate resilience while supporting informed decision-making. However, challenges related to digital infrastructure, affordability, data governance, and digital literacy remain significant. The paper concludes that supportive policies, inclusive innovation, capacity building, and multi-stakeholder collaboration are essential to accelerate India's transition toward smart, resilient, and sustainable food systems.","数字化转型正通过将先进技术融入生产、加工、营销和供应链管理，重塑农业综合企业，从而构建更高效、更具韧性且可持续的粮食系统。在印度，粮食需求不断增长、气候多变、土地持有规模缩小以及资源约束，加速了对智慧农业解决方案的需求。本文综述了人工智能、精准农业、物联网、区块链、大数据分析、无人机和数字平台等数字技术在推动农业综合企业现代化和强化食品价值链中的新兴作用。文章综合了近期关于农业领域技术创新、采用模式、机遇、障碍和可持续性成果的科学证据。综述指出，数字技术提高了生产力、资源利用效率、可追溯性、市场连通性和气候韧性，同时支持知情决策。然而，数字基础设施、可负担性、数据治理和数字素养方面的挑战依然显著。本文结论认为，支持性政策、包容性创新、能力建设和多利益相关方合作对于加速印度向智慧、有韧性且可持续的粮食系统转型至关重要。","Dandao Xuebao\u002FJournal of Ballistics","2026-10-01T00:00:00Z",{"impact":19,"substance":271,"depth":203,"authority":20,"freshness":21,"relevant":22,"comment":441},"综述性论文，系统梳理数字技术在印度与北欧农业的应用与障碍，具行业参考价值但非突发资讯。",[443],{"name":438,"url":435},[27,28,208,52,445],"农业区块链",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":449},{"candidates":450,"agricultural_evidence":48},[451,455,459,463],{"code":46,"score":120,"supported":48,"title":452,"abstract":453,"tags":454},[],[28,52,50],[27,28,52],{"code":33,"score":56,"supported":48,"title":456,"abstract":457,"tags":458},[],[59,144],[],{"code":70,"score":226,"supported":48,"title":460,"abstract":461,"tags":462},[],[157],[208],{"code":147,"score":71,"supported":48,"title":464,"abstract":465,"tags":467},[],[466],"climate resilience",[],[469,470],"印度 数字农业 转型","北欧 智慧农业 食品系统","印度数字农业转型-4070",{"doi":9,"openalex_id":473,"authors":474,"venue":438,"cited_by_count":98,"oa_url":435,"card":477,"direction":105,"ingested_from":106},"W7217570283",[475],{"name":476,"orcid":9},"Anjali Adhikari",{"tldr":478,"method":479,"finding":480,"direction":103,"opportunity":481},"综述数字技术在印度农业综合企业中的应用，探讨迈向智慧可持续食品系统的路径。","综述AI、物联网、区块链、大数据、无人机等数字技术在农业价值链中的证据。","数字技术提升生产力、资源效率、可追溯性和气候韧性，但基础设施、成本和数字素养仍是障碍。","可对比北欧与印度情境，研究数字农业技术适配性与包容性创新政策。","2026-10-03T23:30:14.654788Z",{"id":484,"title":485,"url":486,"summary":487,"summary_zh":488,"content":9,"source_name":489,"source_url":486,"published_at":490,"category":12,"cover_url":9,"hotness":491,"is_selected":14,"score":492,"score_detail":493,"sources":495,"tags":499,"primary_topic":502,"secondary_topics":503,"classification":504,"search_phrases":529,"slug":532,"view_count":226,"doi":533,"paper":534,"created_at":544},3482,"Digital Transformation and the Reconfiguration of Farming Systems: Understanding How Tech-nological Adoption and Institutional Conditions Shape Sustainable and Inclusive Agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22932900","Digital agriculture is progressively reshaping farming through the integration of sensors, artificial intelligence, Internet of Things technologies, digital platforms, data analytics, robotics and automated decision-support systems. This paper examines this transformation by connecting three complementary dimensions: the socio-technical development of digital agriculture, the determinants and processes of technological adoption, and the economic, organizational and inclusive consequences of digitalization for farming systems. The literature indicates that agricultural digitalization cannot be reduced to the availability of increasingly sophisticated technologies. Adoption and sustained use depend on farmers’ perceptions of usefulness and compatibility, farm resources, human capital, infrastructure, institutional support, advisory systems and the capacity to integrate technologies into established production routines. Digital technologies may improve resource efficiency, information management, productivity and sustainability, while also modifying labor organization, farmer autonomy, data governance and relationships among actors within agricultural value chains. These effects remain uneven across farms and territories, particularly where smallholders face financial, infrastructural and capability constraints. The paper therefore interprets digital agriculture as a multidimensional transformation whose outcomes depend on the interaction between technological capabilities, farmer behavior and institutional conditions. Inclusive and sustainable digitalization requires attention not only to innovation diffusion but also to governance, skills, accessibility and the distribution of technological benefits.","数字农业正通过传感器、人工智能、物联网技术、数字平台、数据分析、机器人技术和自动化决策支持系统的整合，逐步重塑农业生产方式。本文通过连接三个互补维度来考察这一转型：数字农业的社会技术发展、技术采纳的决定因素与过程，以及数字化对农业系统产生的经济、组织和包容性后果。文献表明，农业数字化不能简化为日益复杂的技术供给。采纳和持续使用取决于农民对有用性和兼容性的认知、农场资源、人力资本、基础设施、制度支持、咨询系统以及将技术整合到既定生产惯例中的能力。数字技术可能提高资源效率、信息管理、生产力和可持续性，同时也会改变劳动组织、农民自主性、数据治理以及农业价值链中参与者之间的关系。这些影响在农场和区域之间仍不均衡，尤其是小农户面临资金、基础设施和能力约束的地方。因此，本文将数字农业解释为一种多维转型，其结果取决于技术能力、农民行为和制度条件之间的相互作用。包容和可持续的数字化不仅需要关注创新扩散，还需要关注治理、技能、可及性和技术收益的分配。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-24T00:00:00Z",25,77,{"impact":19,"substance":271,"depth":203,"authority":20,"freshness":141,"relevant":22,"comment":494},"系统梳理数字农业技术采纳与制度条件的研究综述，对智慧农业政策与推广有参考价值，但属文献综述类，非重大突破。",[496,497],{"name":489,"url":486},{"name":489,"url":498},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22932899",[27,28,208,500,501],"小农户","技术采纳",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":505},{"candidates":506,"agricultural_evidence":48},[507,511,515,520,524],{"code":46,"score":21,"supported":48,"title":508,"abstract":509,"tags":510},[],[27,53],[27,28],{"code":33,"score":56,"supported":48,"title":512,"abstract":513,"tags":514},[],[59,144],[],{"code":516,"score":226,"supported":48,"title":517,"abstract":518,"tags":519},"G2",[],[500],[500],{"code":70,"score":226,"supported":48,"title":521,"abstract":522,"tags":523},[],[157],[208],{"code":525,"score":22,"supported":14,"title":526,"abstract":527,"tags":528},"G1",[],[],[501],[530,531],"农业人工智能 技术采纳 数字农业 智慧农业","农业人工智能 技术采纳","农业人工智能技术采纳数字农业智慧农业-3482","10.5281\u002Fzenodo.22932900",{"doi":533,"openalex_id":535,"authors":536,"venue":489,"cited_by_count":98,"oa_url":486,"card":539,"direction":105,"ingested_from":106},"W7214187008",[537],{"name":538,"orcid":9},"Chilufya Banda",{"tldr":540,"method":541,"finding":542,"direction":103,"opportunity":543},"综述数字农业转型，分析技术采纳与制度条件如何共同塑造可持续包容性农业。","文献综述，整合社会技术、技术采纳与数字化经济组织后果三维度。","数字化成效取决于技术能力、农户行为与制度条件的交互，小农面临多重约束。","可实证检验制度支持与农户能力如何调节数字技术对包容性和可持续性的影响。","2026-09-25T23:30:18.463733Z"]