[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3806":3,"related-3806":107},{"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":63,"slug":66,"view_count":54,"doi":67,"paper":68,"created_at":106},3806,"Data Management in Agricultural Enterprises: A Framework for Data-Driven Decision Support","https:\u002F\u002Fdoi.org\u002F10.7160\u002Faol.2026.180305","The growing digitalisation of agriculture is leading to a rapid increase in the volume of data produced by agricultural enterprises; however, its practical use is often limited by fragmented data sources, varying data quality, insufficient metadata, and inconsistent storage and management practices. The aim of this article is to propose a practical framework for the systematic management of data in agricultural enterprises to support its long-term availability, reuse, and integration into decision-making processes. The proposed approach is based on four interlinked steps: identifying data needs and issues, creating and recording a data inventory, drawing up a Data Management Plan (DMP), and implementing it in practice. The framework links technical aspects of data management – such as data formats, metadata, quality, integrity, security, backup and archiving – with the definition of responsibilities for individual staff members and farm (agricultural enterprise) management. For practical implementation, two basic approaches to data storage are distinguished: file systems and data platforms or repositories. The more advanced platform-based approach enables the centralisation of heterogeneous data sources, the automation of their transfer and processing, and subsequently the integration of the acquired data with analytical tools and decision-support systems. The proposed framework thus represents a path from the isolated collection of operational data to its systematic management and utilisation as a long-term information source for both operational and strategic decision-making within an agricultural enterprise.","农业日益数字化，导致农业企业产生的数据量迅速增加；然而，其实际应用往往受限于数据来源分散、数据质量参差不齐、元数据不足以及存储和管理实践不一致。本文旨在提出一个实用框架，用于农业企业数据的系统化管理，以支持其长期可用性、再利用以及融入决策过程。所提出的方法基于四个相互关联的步骤：识别数据需求与问题、创建并记录数据清单、制定数据管理计划（Data Management Plan, DMP），以及在实践中实施该计划。该框架将数据管理的技术层面——如数据格式、元数据、质量、完整性、安全性、备份与归档——与个别工作人员及农场（农业企业）管理层的责任界定联系起来。在实践实施方面，区分了两种基本的数据存储方式：文件系统和数据平台或存储库。更为先进的基于平台的方式能够实现异构数据源的集中化、其传输与处理的自动化，并随后将所获取的数据与分析工具及决策支持系统相集成。因此，所提出的框架代表了一条从孤立收集运营数据到系统化管理并将其作为农业企业内部运营和战略决策长期信息源加以利用的路径。",null,"Agris on-line Papers in Economics and Informatics","2026-09-28T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,18,16,13,8,1,"核心期刊论文，提出农业企业数据管理四步框架，方法清晰但偏理论，对农业数据治理实践有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字农业","智慧农业","数据治理","决策支持","农业数据管理",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,58],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":51},9,true,[49],"decision support",[],[30],{"code":53,"score":54,"supported":14,"title":55,"abstract":56,"tags":57},"I1",2,[],[],[27,28],{"code":59,"score":22,"supported":14,"title":60,"abstract":61,"tags":62},"I6",[],[],[29],[64,65],"农业企业 数据管理 框架","农业数据管理计划 DMP","农业企业数据管理框架-3806","10.7160\u002Faol.2026.180305",{"doi":67,"openalex_id":69,"authors":70,"venue":10,"cited_by_count":97,"oa_url":6,"card":98,"direction":104,"ingested_from":105},"W7214583099",[71,74,77,80,83,85,88,90,93,95],{"name":72,"orcid":73},"Michal Stočes","https:\u002F\u002Forcid.org\u002F0000-0001-7128-1071",{"name":75,"orcid":76},"Jan Jarolímek","https:\u002F\u002Forcid.org\u002F0000-0001-7194-3055",{"name":78,"orcid":79},"Irena Benešová","https:\u002F\u002Forcid.org\u002F0000-0002-9381-063X",{"name":81,"orcid":82},"Pavel Šimek","https:\u002F\u002Forcid.org\u002F0000-0001-9244-1476",{"name":84,"orcid":9},"Zdeňka Žáková Kroupová",{"name":86,"orcid":87},"Ludmila Pánková","https:\u002F\u002Forcid.org\u002F0000-0001-8861-4733",{"name":89,"orcid":9},"Jiří Vaněk",{"name":91,"orcid":92},"Eva Kánská","https:\u002F\u002Forcid.org\u002F0000-0001-5249-6405",{"name":94,"orcid":9},"Martin Havránek",{"name":96,"orcid":9},"Anderle Michael",0,{"tldr":99,"method":100,"finding":101,"direction":102,"opportunity":103},"提出农业企业数据管理框架，实现数据从孤立采集到系统化决策支持。","四步框架：识别需求、数据清单、管理计划、实施；区分文件系统与数据平台。","平台化存储可集中异构数据、自动化处理并集成决策支持系统。","数字乡村与农业信息化","可研究农业数据管理成熟度评估模型及平台落地对决策绩效的实证影响。","农业人工智能与决策模型","openalex","2026-09-29T23:31:01.874610Z",{"total":108,"page":22,"page_size":108,"items":109},6,[110,163,249,311,383,452],{"id":111,"title":112,"url":113,"summary":114,"summary_zh":9,"content":9,"source_name":115,"source_url":9,"published_at":116,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":117,"sources":119,"tags":121,"primary_topic":124,"secondary_topics":125,"classification":126,"search_phrases":149,"slug":152,"view_count":97,"doi":153,"paper":154,"created_at":162},5529,"数字农业技术采纳的驱动因素与障碍：基于复杂网络与演化博弈的分析","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1894648\u002Ffull","文章从演化博弈视角构建数字农业技术采纳的驱动与阻碍机制框架，系统梳理影响农户采纳数字农业技术的关键因素与障碍路径。研究强调通用大模型难以直接满足专业知识、结果依据与溯源能力要求，为政策设计提供了技术采纳侧的理论依据。","Frontiers in Sustainable Food Systems 2026-10-19","2026-10-19T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":118},"从演化博弈视角分析农户采纳数字农业技术的驱动与障碍，方法有新意、结论有政策参考价值，但属理论性论文，影响范围限于细分领域。",[120],{"name":115,"url":113},[27,28,122,123],"技术采纳","农户行为",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":127},{"candidates":128,"agricultural_evidence":47},[129,133,139,144],{"code":53,"score":20,"supported":47,"title":130,"abstract":131,"tags":132},[27],[27],[27,28],{"code":33,"score":134,"supported":47,"title":135,"abstract":136,"tags":138},3,[],[137],"大模型",[],{"code":140,"score":22,"supported":14,"title":141,"abstract":142,"tags":143},"G1",[],[],[122],{"code":145,"score":22,"supported":14,"title":146,"abstract":147,"tags":148},"G2",[],[],[123],[150,151],"数字农业 技术采纳 演化博弈","农户行为 技术采纳 数字农业 智慧农业","数字农业技术采纳演化博弈-5529","10.3389\u002Ffsufs.2026.1894648\u002Ffull",{"doi":153,"openalex_id":9,"authors":155,"venue":9,"cited_by_count":97,"oa_url":9,"card":156,"direction":102,"ingested_from":161},[],{"tldr":157,"method":158,"finding":159,"direction":102,"opportunity":160},"构建演化博弈框架分析农户采纳数字农业技术的驱动因素与障碍路径。","演化博弈建模与复杂网络分析，梳理采纳影响因素。","通用大模型难以满足专业知识、结果依据与溯源要求，需政策引导。","可结合实证数据检验博弈模型，并探索面向溯源与专业知识的专用农业模型设计。","agent","2026-10-22T00:07:09.498742Z",{"id":164,"title":165,"url":166,"summary":167,"summary_zh":168,"content":9,"source_name":169,"source_url":166,"published_at":170,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":171,"score_detail":172,"sources":176,"tags":178,"primary_topic":182,"secondary_topics":183,"classification":190,"search_phrases":227,"slug":230,"view_count":134,"doi":231,"paper":232,"created_at":248},4693,"Scalable, cloud-native decision support system for Variable Rate Application (VRA) based on multi-year NDRE time series in arable precision agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23282433","This paper presents an automated, cloud-native geospatial Decision Support System (DSS) that generates variable rate prescription maps directly from Sentinel-2 satellite observations. Designed as an end-to-end arable field VRA architecture, the system operates completely autonomously with minimal input, requiring only the field boundary polygon, the crop phenology class (winter or spring crop), and the multi-year observation window. The computation engine streams Cloud-Optimized GeoTIFF (COG) rasters via asynchronous cloud connectivity through the Microsoft Planetary Computer (MPC) SpatioTemporal Asset Catalog (STAC) Application Programming Interface (API), enforcing data quality through multi-stage Scene Classification Layer (SCL) filtering. Instead of the rapidly saturating Normalized Difference Vegetation Index (NDVI), the model relies on the Normalized Difference Red Edge (NDRE) index, which accurately captures canopy chlorophyll and green biomass dynamics. For each native 10×10 m grid cell, the system extracts the 95th percentile (P95) peak value within a crop-specific reproductive phenological window, eliminating single-day snapshot noise while validating winter or spring growth trajectories via automated phenological ratio calculations. The decision and prescription engine is built upon two core agronomic safety pillars: An exponential empirical Bayesian stability shrinkage that dampens unstable, high-variance zones toward the safe field average, preventing the over-application of inputs on drought-prone areas. A trigonometric Horn slope attenuation derived from digital elevation models that systematically reduces application rates on erosion-prone steep slopes. Cell-level rates are distributed within the target tolerance range using a rank-allocation algorithm coupled with a Hamilton quota (largest remainder method) correction, guaranteeing strict integer-based machine compatibility and zero-sum conservation of the total field input budget. The technology was validated on a 139.03 ha commercial production field across four reference seasons (2022, 2023, 2024, and 2026) for Variable Rate Seeding (VRS) in winter wheat (Triticum aestivum L., 230 kg\u002Fha baseline seed rate) in a rolling hilly landscape in Hungary, benchmarked against conventional uniform application and conservative multi-year VRS planning. The primary quantitative findings are summarized below: · Failure of conventional uniform application: Across the 14,250 cells, the flat 230 kg\u002Fha rate was optimal on only 1.1% of the field area. · Input reallocation: The model reallocated 1,633.7 kg of seed (and 1,167.1 kg compared to conservative VRS) from unproductive areas into high-capacity zones (Figure 1.). Across 98.9% of the field, conventional uniform management generated systematic under- or over-seeding, inducing yield losses or wasted operational expenditure. · Drought mitigation: In temporally unstable zones subject to extreme weather risk (covering 33.6% of the field, 46.69 ha), Bayesian stability shrinkage reduced seed allocation by 495 kg compared to uniform seeding. Establishing lower canopy densities in these vulnerable zones mitigates intra-crop water competition and prevents early-season forced ripening (Lollato et al., 2017). · Erosion control: On steep slopes exceeding 12% inclination (accounting for 9.75% of the field, 13.56 ha), the Horn topographic attenuation engine reduced seed input by 553 kg relative to the fixed 230 kg\u002Fha baseline. This intervention minimizes surface runoff damage and agrochemical leaching. · Economic return: Spatially optimizing plant density based on pedological and topographic constraints delivers substantial yield gains. Reallocating seed across the field—without any expenditure on supplemental fertilizer—produces a projected yield increase of +0.20 to +0.40 t\u002Fha (+28 to +56 tons of grain across the evaluated field) (Kindred et al., 2016). At a baseline wheat price of €192\u002Ft (70,000 HUF\u002Ft), this generates an estimated net value of €5,376 to €10,752 (1.96 to 3.92M HUF) on this single 139 ha field.","本文提出了一种自动化、云原生的地理空间决策支持系统（DSS），可直接从Sentinel-2卫星观测数据生成变量施肥处方图。该系统设计为端到端的耕地变量施肥（VRA）架构，完全自主运行且输入需求极低，仅需田块边界多边形、作物物候类别（冬作或春作）以及多年观测窗口。计算引擎通过Microsoft Planetary Computer（MPC）时空资产目录（STAC）应用程序接口（API）以异步云连接方式流式传输云优化GeoTIFF（COG）栅格数据，并通过多阶段场景分类层（SCL）滤波确保数据质量。该模型不采用易快速饱和的归一化差异植被指数（NDVI），而是依赖归一化差异红边指数（NDRE），后者能够准确捕捉冠层叶绿素和绿色生物量动态。对于每个原生10×10 m网格单元，系统提取作物特定生殖物候窗口内的第95百分位数（P95）峰值，消除单日快照噪声，同时通过自动化物候比率计算验证冬作或春作生长轨迹。决策与处方引擎建立在两个核心农艺安全支柱之上：指数经验贝叶斯稳定性收缩，将不稳定的高方差区域向安全的田块平均值收缩，防止在易旱区域过量施用投入品；基于数字高程模型推导的三角函数Horn坡度衰减，系统性地降低易侵蚀陡坡上的施用量。单元级施用量通过秩分配算法结合Hamilton配额（最大余数法）校正，在目标容差范围内分配，确保严格的基于整数的机器兼容性和田块总投入预算的零和守恒。该技术已在匈牙利起伏丘陵地貌的139.03 ha商业化生产田块上，针对冬小麦（Triticum aestivum L.，基准播种量230 kg\u002Fha）的变量播种（VRS），在四个参考季节（2022、2023、2024和2026年）进行了验证，并以常规均一施用和保守的多年VRS规划为基准进行比较。主要定量结果总结如下：· 常规均一施用的失败：在14,250个单元中，统一的230 kg\u002Fha施","Zenodo (CERN European Organization for Nuclear Research)","2026-10-10T00:00:00Z",83,{"impact":18,"substance":173,"depth":174,"authority":20,"freshness":13,"relevant":22,"comment":175},23,19,"基于多年度NDRE时序的云原生变量施肥决策系统，方法新颖、验证数据扎实，对精准农业信息化有较高参考价值。",[177],{"name":169,"url":166},[27,28,179,180,181],"变量施肥","精准农业","遥感",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[184],{"code":185,"slug":186,"name":187,"path":188,"parent_code":189},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",{"status":40,"locked":14,"version":41,"method":42,"evidence":191},{"candidates":192,"agricultural_evidence":47},[193,199,203,208,216,222],{"code":53,"score":194,"supported":47,"title":195,"abstract":197,"tags":198},11,[196],"precision agriculture",[],[27,28,180],{"code":33,"score":194,"supported":47,"title":200,"abstract":201,"tags":202},[49],[49],[],{"code":185,"score":46,"supported":47,"title":204,"abstract":205,"tags":207},[],[187,206,186],"winter wheat",[],{"code":209,"score":210,"supported":47,"title":211,"abstract":212,"tags":215},"F3",7,[],[213,214],"施肥","fertilizer",[179],{"code":217,"score":134,"supported":47,"title":218,"abstract":219,"tags":221},"H7",[],[220],"drought",[],{"code":223,"score":22,"supported":14,"title":224,"abstract":225,"tags":226},"I3",[],[],[181],[228,229],"Sentinel-2 NDRE 变量施肥","云原生 决策支持系统 精准农业","Sentinel-2NDRE变量施肥-4693","10.5281\u002Fzenodo.23282433",{"doi":231,"openalex_id":233,"authors":234,"venue":169,"cited_by_count":97,"oa_url":166,"card":242,"direction":102,"ingested_from":105},"W7221365319",[235,237,239],{"name":236,"orcid":9},"Zoltán Szenek",{"name":238,"orcid":9},"Donát Szám",{"name":240,"orcid":241},"Gábor Milics","https:\u002F\u002Forcid.org\u002F0000-0002-4499-8322",{"tldr":243,"method":244,"finding":245,"direction":246,"opportunity":247},"构建云原生决策支持系统，用多年NDRE时序自动生成精准农业变量施肥\u002F播种处方图。","Sentinel-2 NDRE、COG\u002FSTAC云流式处理、贝叶斯收缩与坡度衰减","在匈牙利139公顷冬小麦田验证，系统优于传统均一施用与保守多年VRS规划。","农业遥感与作物表型","可探索将NDRE时序DSS扩展至多作物、多区域及实时物联网数据融合的通用处方框架。","2026-10-11T23:30:36.790037Z",{"id":250,"title":251,"url":252,"summary":253,"summary_zh":254,"content":9,"source_name":169,"source_url":252,"published_at":170,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":255,"score_detail":256,"sources":259,"tags":261,"primary_topic":265,"secondary_topics":266,"classification":267,"search_phrases":294,"slug":297,"view_count":22,"doi":298,"paper":299,"created_at":310},4657,"Agriculture and Farmer Digital Assistant: An AI-Based Digital Decision Support Platform for Smart Farming","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23278187","Abstract Farmers frequently make decisions under uncertain weather, incomplete agricultural information, changing soil conditions, and limited access to timely expert guidance. The Agriculture and Farmer Digital Assistant (AFDA) is a web-based digital decision-support platform designed to bring agricultural data, artificial intelligence, recommendation services, weather information, and role-based administration into a single system. The platform accepts agricultural inputs from farmers and processes them to provide crop recommendations and basic farming guidance. The design also considers soil analysis, pest and disease support, market information, notifications, multilingual assistance, and future sensor integration. The implemented project version focuses on user registration and login, agricultural data input, crop recommendation, basic weather guidance, and a recommendation dashboard, while several advanced capabilities remain future extensions. A layered architecture separates the user interface, application logic, AI processing, database, and notification\u002Frecommendation functions. The project further defines functional and non-functional requirements, data-flow and entity relationships, user workflows, testing procedures, and maintenance considerations. The reported evaluation is functional rather than numerical: the major input, authentication, recommendation, navigation, and output-display workflows were checked during development, while no measured accuracy or response-time benchmark is claimed. AFDA therefore provides a foundation for a connected and extensible agricultural assistance system intended to support better-informed and more sustainable farming decisions. Keywords Agriculture and Farmer Digital Assistant, AFDA, artificial intelligence, machine learning, crop recommendation, smart farming, agricultural decision support, weather guidance, role-based access control.","农户常常在天气不确定、农业信息不完整、土壤条件不断变化以及难以及时获得专家指导的情况下作出决策。农业与农户数字助手（Agriculture and Farmer Digital Assistant，AFDA）是一个基于网络的数字决策支持平台，旨在将农业数据、人工智能、推荐服务、天气信息和基于角色的管理整合到一个系统中。该平台接收农户输入的农业数据，并对其进行处理，以提供作物推荐和基本农耕指导。其设计还考虑了土壤分析、病虫害支持、市场信息、通知、多语言辅助以及未来的传感器集成。已实现的项目版本侧重于用户注册与登录、农业数据输入、作物推荐、基本天气指导以及推荐仪表板，而若干高级功能仍留作未来扩展。分层架构将用户界面、应用逻辑、人工智能处理、数据库以及通知\u002F推荐功能相互分离。该项目进一步定义了功能性与非功能性需求、数据流与实体关系、用户工作流程、测试程序以及维护方面的考虑。所报告的评估是功能性的而非数值性的：在开发过程中检查了主要输入、身份验证、推荐、导航和输出显示等工作流程，但未声称有任何经过测量的准确性或响应时间基准。因此，AFDA为一个互联且可扩展的农业辅助系统提供了基础，旨在支持更明智且更可持续的农耕决策。关键词：农业与农户数字助手，AFDA，人工智能，机器学习，作物推荐，智慧农业，农业决策支持，天气指导，基于角色的访问控制。",54,{"impact":21,"substance":257,"depth":17,"authority":13,"freshness":13,"relevant":22,"comment":258},14,"面向农户的AI决策支持平台论文，架构与需求设计较完整，但仅做功能验证、无精度或性能数据，属早期原型，参考价值有限。",[260],{"name":169,"url":252},[27,28,262,263,264],"农业人工智能","作物推荐","农业决策支持",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":268},{"candidates":269,"agricultural_evidence":47},[270,277,284,289],{"code":33,"score":18,"supported":47,"title":271,"abstract":272,"tags":276},[49],[273,274,275,49],"机器学习","machine learning","artificial intelligence",[262],{"code":278,"score":108,"supported":47,"title":279,"abstract":280,"tags":283},"H2",[],[281,282],"土壤","soil",[],{"code":53,"score":285,"supported":47,"title":286,"abstract":287,"tags":288},5,[],[28],[27,28],{"code":59,"score":134,"supported":47,"title":290,"abstract":291,"tags":293},[],[292],"agricultural data",[],[295,296],"AFDA 农业数字助手","农业人工智能 农业决策支持 作物推荐 数字农业","AFDA农业数字助手-4657","10.5281\u002Fzenodo.23278187",{"doi":298,"openalex_id":300,"authors":301,"venue":169,"cited_by_count":97,"oa_url":252,"card":304,"direction":309,"ingested_from":105},"W7221331787",[302],{"name":303,"orcid":9},"B. Siddhu, G. Naveen, S. Guru Charan, T. Ajay, C. Naresh",{"tldr":305,"method":306,"finding":307,"direction":104,"opportunity":308},"开发了基于AI的农业数字决策支持平台AFDA，为农民提供作物推荐和农事指导。","采用分层架构的Web平台，集成AI处理、数据库和推荐服务，支持多语言。","平台实现了用户认证、数据输入、作物推荐和天气指导等核心功能，但未提供精度或响应时间基准。","可补充传感器实时数据集成与模型精度评估，提升决策支持的可靠性和实时性。","智慧农业 \u002F 农业物联网","2026-10-11T23:30:07.538409Z",{"id":312,"title":313,"url":314,"summary":315,"summary_zh":316,"content":9,"source_name":317,"source_url":314,"published_at":318,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":319,"score_detail":320,"sources":322,"tags":324,"primary_topic":326,"secondary_topics":327,"classification":328,"search_phrases":363,"slug":366,"view_count":367,"doi":368,"paper":369,"created_at":382},4105,"Artificial Intelligence in Agriculture: Enhancing Yield and Sustainable Farming Practices among Farmers in India","https:\u002F\u002Fdoi.org\u002F10.31235\u002Fosf.io\u002Fg7ypt_v1","Artificial intelligence (AI) is increasingly being applied across sectors, including agriculture. In India, agriculture remains central to employment, rural livelihoods, and national food security. Rising population pressure, climate change, declining land availability, pest and disease risks, labour shortages, and resource constraints have intensified the need for more productive and sustainable farming systems. This paper uses secondary data to examine how AI can enhance agricultural yield and support sustainable farming practices among farmers in India. It reviews applications such as precision agriculture, drones, crop disease detection, digital advisory services, market information systems, and resource optimisation tools. The paper also analyses the major barriers to AI adoption, including weak rural connectivity, high initial investment, limited digital literacy, and the need for localised solutions. The study concludes that AI has strong potential to improve productivity and sustainability in Indian agriculture, provided that adoption is supported through public investment, affordable access models, local-language advisory systems, farmer training, and public- private partnerships.","人工智能（AI）正日益应用于包括农业在内的各个领域。在印度，农业仍然是就业、农村生计和国家粮食安全的核心。人口压力不断上升、气候变化、可用土地减少、病虫害风险、劳动力短缺以及资源约束，加剧了对更高生产力和更可持续农业体系的需求。本文利用二手数据，考察人工智能如何提升印度农民的农业产量并支持可持续耕作方式。文章综述了精准农业、无人机、作物病害检测、数字咨询服务、市场信息系统和资源优化工具等应用。本文还分析了人工智能应用的主要障碍，包括农村连通性薄弱、初始投资高、数字素养有限以及对本地化解决方案的需求。研究认为，人工智能在提升印度农业生产力和可持续性方面具有巨大潜力，前提是通过公共投资、可负担的获取模式、本地语言咨询系统、农民培训以及公私合作来支持其应用。","OpenAlex","2026-10-02T00:00:00Z",69,{"impact":18,"substance":19,"depth":257,"authority":17,"freshness":46,"relevant":22,"comment":321},"基于二手数据综述印度AI农业应用与推广障碍，结论有参考价值但缺乏一手数据与实证方法创新。",[323],{"name":317,"url":314},[27,28,262,180,325],"印度农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":329},{"candidates":330,"agricultural_evidence":47},[331,335,339,346,352,358],{"code":33,"score":17,"supported":47,"title":332,"abstract":333,"tags":334},[275],[275],[262],{"code":53,"score":46,"supported":47,"title":336,"abstract":337,"tags":338},[],[180,196],[27,28,180],{"code":340,"score":108,"supported":47,"title":341,"abstract":342,"tags":345},"A8",[],[343,344],"粮食安全","food security",[],{"code":217,"score":108,"supported":47,"title":347,"abstract":348,"tags":351},[],[349,350],"气候变化","climate change",[],{"code":353,"score":134,"supported":47,"title":354,"abstract":355,"tags":357},"H8",[],[356],"可持续农业",[],{"code":359,"score":22,"supported":14,"title":360,"abstract":361,"tags":362},"J3",[],[],[325],[364,365],"印度 农业人工智能 可持续农业","印度 精准农业 无人机","印度农业人工智能可持续农业-4105",4,"10.31235\u002Fosf.io\u002Fg7ypt_v1",{"doi":368,"openalex_id":370,"authors":371,"venue":9,"cited_by_count":97,"oa_url":314,"card":377,"direction":102,"ingested_from":105},"W7215740376",[372,375],{"name":373,"orcid":374},"Seema Wankhade","https:\u002F\u002Forcid.org\u002F0009-0005-6663-3729",{"name":376,"orcid":9},"Subhash Patil",{"tldr":378,"method":379,"finding":380,"direction":309,"opportunity":381},"基于二手数据综述AI在印度农业中提升产量与可持续性的应用及障碍。","使用二手数据，综述精准农业、无人机、病害检测等AI应用。","AI能提升印度农业生产力与可持续性，但受网络、成本、数字素养等制约。","可研究小农户低成本AI采纳路径、本地语言咨询系统及公私合作模式的效果评估。","2026-10-03T23:30:42.370830Z",{"id":384,"title":385,"url":386,"summary":387,"summary_zh":388,"content":9,"source_name":389,"source_url":386,"published_at":390,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":391,"score_detail":392,"sources":394,"tags":396,"primary_topic":399,"secondary_topics":400,"classification":401,"search_phrases":426,"slug":429,"view_count":54,"doi":430,"paper":431,"created_at":451},3881,"A Human-Centered Machine Learning Dashboard for Real-Time Smart Farming Decision Support","https:\u002F\u002Fdoi.org\u002F10.68012\u002Fshem.v2i2.302","The integration of machine learning (ML) into precision agriculture has advanced predictive accuracy for tasks such as crop monitoring, irrigation scheduling, and yield estimation. However, many existing IoT-based smart farming systems remain technically driven, offering limited attention to how farmers and field operators actually interact with and interpret model outputs. This gap often results in low adoption rates, as complex predictions fail to translate into actionable, trustworthy decisions for end users. This study proposes a human-centered machine learning dashboard designed to bridge predictive modeling with real-time, farmer-friendly decision support. The system integrates IoT sensor data streams with ML-based prediction modules, presented through an interactive, usability-tested interface that emphasizes clarity, accessibility, and interpretability over raw technical complexity. A working prototype was developed and evaluated using both quantitative usability metrics (e.g., task completion time, System Usability Scale scores) and qualitative feedback from target users to assess the dashboard's effectiveness in supporting field-level decisions. The proposed system architecture, dashboard interface, and underlying ML pipeline are implemented as open and reproducible source code, enabling replication and adaptation across similar smart farming contexts. Results indicate that the human-centered design significantly improves users' comprehension of model outputs and their confidence in acting on system recommendations, without compromising predictive performance. This research contributes a practical framework for designing machine intelligence systems that prioritize end-user experience alongside technical accuracy, offering implications for the broader development of human-centered AI in agricultural and IoT-driven decision support environments.","机器学习（ML）在精准农业中的融合提升了作物监测、灌溉调度和产量估算等任务的预测准确性。然而，许多现有的基于物联网的智慧农业系统仍然以技术驱动为主，对农民和田间操作人员实际如何与模型输出交互并加以解读关注有限。这一差距往往导致采用率偏低，因为复杂的预测无法转化为最终用户可操作、可信赖的决策。本研究提出了一种以人为中心的机器学习仪表盘，旨在将预测建模与实时、面向农民的决策支持相衔接。该系统将物联网传感器数据流与基于机器学习的预测模块相集成，并通过一个经过可用性测试的交互式界面呈现，强调清晰性、可及性和可解释性，而非原始的技术复杂性。我们开发了一个可运行的原型，并使用定量可用性指标（如任务完成时间、系统可用性量表评分）和来自目标用户的定性反馈进行评估，以考察该仪表盘在支持田间决策方面的有效性。所提出的系统架构、仪表盘界面和底层机器学习流水线均以开放且可复现的源代码形式实现，便于在类似的智慧农业场景中复制和适配。结果表明，以人为中心的设计显著提升了用户对模型输出的理解以及依据系统建议采取行动的信心，同时未损害预测性能。本研究为设计在技术准确性的同时优先考虑最终用户体验的机器智能系统提供了一个实用框架，对农业及物联网驱动的决策支持环境中以人为中心的人工智能的更广泛发展具有启示意义。","Smart Human-centered Emerging Research in Machine Intelligence","2026-09-30T00:00:00Z",64,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":21,"relevant":22,"comment":393},"提出以人为本的机器学习仪表盘，兼顾可用性与预测性能，并开源实现，对智慧农业落地有参考价值。",[395],{"name":389,"url":386},[28,262,397,30,398],"物联网","人机交互",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":402},{"candidates":403,"agricultural_evidence":47},[404,409,413,419],{"code":33,"score":405,"supported":47,"title":406,"abstract":407,"tags":408},27,[274,49],[273,274,49],[262,30],{"code":53,"score":13,"supported":47,"title":410,"abstract":411,"tags":412},[],[28,180,196],[28],{"code":414,"score":210,"supported":47,"title":415,"abstract":416,"tags":418},"I5",[],[397,417],"IoT",[397],{"code":420,"score":108,"supported":47,"title":421,"abstract":422,"tags":425},"H3",[],[423,424],"灌溉","irrigation",[],[427,428],"农业人工智能 人机交互 决策支持 智慧农业","农业人工智能 人机交互","农业人工智能人机交互决策支持智慧农业-3881","10.68012\u002Fshem.v2i2.302",{"doi":430,"openalex_id":432,"authors":433,"venue":389,"cited_by_count":97,"oa_url":386,"card":446,"direction":309,"ingested_from":105},"W7214922384",[434,437,439,441,443],{"name":435,"orcid":436},"Adam Faturahman","https:\u002F\u002Forcid.org\u002F0000-0001-9727-9092",{"name":438,"orcid":9},"James Williams",{"name":440,"orcid":9},"Evelyn Anderson",{"name":442,"orcid":9},"Goh Yao",{"name":444,"orcid":445},"Muhammad Rifat Syauqy","https:\u002F\u002Forcid.org\u002F0009-0001-4582-5693",{"tldr":447,"method":448,"finding":449,"direction":309,"opportunity":450},"提出以人为中心的机器学习仪表盘，融合物联网与预测模型，为农户提供实时可解释的决策支持。","集成物联网传感器数据流与机器学习预测模块，开发交互式仪表盘，并用SUS量表和用户","以人为中心的设计显著提升用户对模型输出的理解与采纳信心，且不牺牲预测性能。","可探索不同作物、地区和文化背景下农户对可解释AI界面的差异化需求与长期采纳效果。","2026-10-01T23:30:09.481397Z",{"id":453,"title":454,"url":455,"summary":456,"summary_zh":9,"content":9,"source_name":457,"source_url":455,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":458,"score_detail":459,"sources":462,"tags":464,"primary_topic":467,"secondary_topics":468,"classification":470,"search_phrases":489,"slug":492,"view_count":134,"doi":493,"paper":494,"created_at":505},3807,"Reliability Auditing and Capacity-Constrained Review for Wheat-Yield Decision Support under Temporal and Spatial Shift","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10698325\u002Fv1","Reliability Auditing and Capacity-Constrained Review for Wheat-Yield Decision Support under Temporal and Spatial Shift。Research Square","Research Square",52,{"impact":21,"substance":257,"depth":460,"authority":108,"freshness":46,"relevant":22,"comment":461},15,"预印本论文，聚焦小麦产量决策支持系统的可靠性审计与时空迁移问题，方法有一定新意但尚未经同行评审，公共影响有限。",[463],{"name":457,"url":455},[28,262,30,465,466],"遥感监测","小麦产量",{"code":185,"slug":186,"name":187,"path":188,"parent_code":189},[469],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":471},{"candidates":472,"agricultural_evidence":47},[473,477,481,485],{"code":33,"score":20,"supported":47,"title":474,"abstract":475,"tags":476},[49],[49],[262,30],{"code":185,"score":194,"supported":47,"title":478,"abstract":479,"tags":480},[186],[186],[],{"code":53,"score":22,"supported":14,"title":482,"abstract":483,"tags":484},[],[],[28],{"code":223,"score":22,"supported":14,"title":486,"abstract":487,"tags":488},[],[],[465],[490,491],"小麦产量 决策支持 遥感","Research Square 小麦 产量预测","小麦产量决策支持遥感-3807","10.21203\u002Frs.3.rs-10698325\u002Fv1",{"doi":493,"openalex_id":495,"authors":496,"venue":457,"cited_by_count":97,"oa_url":455,"card":9,"direction":104,"ingested_from":105},"W7214624863",[497,500,503],{"name":498,"orcid":499},"Xin Li","https:\u002F\u002Forcid.org\u002F0009-0005-0670-5006",{"name":501,"orcid":502},"Bojian Guo","https:\u002F\u002Forcid.org\u002F0009-0005-8353-2436",{"name":504,"orcid":9},"Asel Kartanova","2026-09-29T23:31:02.997580Z"]