[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2778":3,"related-2778":112},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":11,"published_at":12,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":16,"score_detail":17,"sources":25,"tags":27,"primary_topic":33,"secondary_topics":39,"classification":40,"search_phrases":82,"slug":85,"view_count":86,"doi":87,"paper":88,"created_at":111},2778,"DS2-Based Cross-Data-Space Interoperability for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.48550\u002Farxiv.2609.17185","Despite the strategies of modern precision agriculture to leverage the integration of legacy agricultural systems, the challenges of IoT data fragmentation, farmers' sovereignty preservation, and limited interoperability still persist. This paper presents our work, conducted within the Horizon Europe DS2 project (DataSpace, DataShare 2.0), that applies an interoperability-oriented framework supporting participants of different agricultural data spaces to share data products and services under secure, sovereign, and transparent methods. The suggested methodology follows a layered reference architecture, where each layer consists of independent operational modules that facilitate the inter-sector data exchange between DigiAgro and AgroScience Data Spaces. The result of this work is an automated ecosystem for sharing diverse farm IoT measurements, satellite images and metrics, weather forecasts, and analytics services across distinct data spaces, aiming to generate accurate recommendations on crop practices, such as irrigation schedules, that farmers and agronomists will rely on to increase crop production while maintaining sustainability.","尽管现代精准农业策略致力于整合传统农业系统，但物联网数据碎片化、农民主权保护以及互操作性有限等挑战依然存在。本文介绍了我们在Horizon Europe DS2项目（DataSpace，DataShare 2.0）中开展的工作，该工作应用了一个面向互操作性的框架，支持不同农业数据空间的参与者在安全、主权和透明的方法下共享数据产品与服务。所建议的方法遵循分层参考架构，其中每一层由独立的操作模块组成，促进DigiAgro与AgroScience数据空间之间的跨部门数据交换。这项工作的成果是一个自动化生态系统，用于在不同数据空间之间共享多样化的农场物联网测量数据、卫星图像与指标、天气预报以及分析服务，旨在生成关于作物实践（如灌溉计划）的准确建议，农民和农艺师将依赖这些建议来提高作物产量，同时保持可持续性。",null,"arXiv (Cornell University)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.17185","2026-09-15T00:00:00Z","论文",10,false,77,{"impact":18,"substance":19,"depth":20,"authority":21,"freshness":22,"relevant":23,"comment":24},18,21,17,13,8,1,"欧盟DS2项目下跨数据空间互操作框架，方法新颖且面向精准农业落地，对农业数据要素流通有参考价值。",[26],{"name":10,"url":11},[28,29,30,31,32],"智慧农业","农业物联网","遥感","农业数据空间","数据互操作",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":41,"locked":15,"version":42,"method":43,"evidence":44},"classified","2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":49},[46,55,62,70,77],{"code":47,"score":48,"supported":49,"title":50,"abstract":52,"tags":54},"I1",15,true,[51],"precision agriculture",[53,51],"精准农业",[28],{"code":34,"score":56,"supported":49,"title":57,"abstract":58,"tags":61},7,[],[59,60],"物联网","IoT",[29],{"code":63,"score":64,"supported":49,"title":65,"abstract":66,"tags":69},"H3",6,[],[67,68],"灌溉","irrigation",[],{"code":71,"score":72,"supported":49,"title":73,"abstract":74,"tags":76},"I6",3,[],[75],"agricultural data",[],{"code":78,"score":23,"supported":15,"title":79,"abstract":80,"tags":81},"I3",[],[],[30],[83,84],"农业数据空间 农业物联网 数据互操作 智慧农业","农业数据空间 农业物联网","农业数据空间农业物联网数据互操作智慧农业-2778",2,"10.48550\u002Farxiv.2609.17185",{"doi":87,"openalex_id":89,"authors":90,"venue":10,"cited_by_count":102,"oa_url":103,"card":104,"direction":108,"ingested_from":110},"W7213417676",[91,93,96,98,100],{"name":92,"orcid":9},"Katerina Kyriakou",{"name":94,"orcid":95},"Ilias Syrigos","https:\u002F\u002Forcid.org\u002F0000-0002-4392-6036",{"name":97,"orcid":9},"Ioannis Moutsinas",{"name":99,"orcid":9},"Panagiotis Tzimotoudis",{"name":101,"orcid":9},"Thanasis Korakis",0,"https:\u002F\u002Farxiv.org\u002Fpdf\u002F2609.17185",{"tldr":105,"method":106,"finding":107,"direction":108,"opportunity":109},"提出基于DS2的跨数据空间互操作框架，实现农业数据安全共享与精准推荐。","分层参考架构，连接DigiAgro与AgroScience数据空间，集成IoT、","自动化生态系统可跨数据空间共享数据并生成灌溉等精准农事建议。","智慧农业 \u002F 农业物联网","可研究跨数据空间互操作中的语义对齐、信任机制与实时决策优化。","openalex","2026-09-17T23:30:18.248884Z",{"total":64,"page":23,"page_size":64,"items":113},[114,232,290,356,429,491],{"id":115,"title":116,"url":117,"summary":118,"summary_zh":119,"content":9,"source_name":120,"source_url":117,"published_at":121,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":122,"score_detail":123,"sources":128,"tags":130,"primary_topic":134,"secondary_topics":138,"classification":145,"search_phrases":202,"slug":205,"view_count":102,"doi":206,"paper":207,"created_at":231},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today","2026-10-21T00:00:00Z",79,{"impact":18,"substance":124,"depth":18,"authority":125,"freshness":126,"relevant":23,"comment":127},22,12,9,"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[129],{"name":120,"url":117},[28,131,30,132,133],"农业人工智能","作物水分胁迫","精准灌溉",{"code":63,"slug":68,"name":135,"path":136,"parent_code":137},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[139,144],{"code":140,"slug":141,"name":142,"path":143,"parent_code":38},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":41,"locked":15,"version":42,"method":43,"evidence":146},{"candidates":147,"agricultural_evidence":49},[148,152,159,164,170,175,182,189,196],{"code":63,"score":48,"supported":49,"title":149,"abstract":150,"tags":151},[68],[67,68],[133],{"code":140,"score":14,"supported":49,"title":153,"abstract":154,"tags":158},[],[155,156,157],"机器学习","machine learning","artificial intelligence",[131],{"code":34,"score":126,"supported":49,"title":160,"abstract":161,"tags":163},[],[59,162,60],"internet of things",[],{"code":47,"score":56,"supported":49,"title":165,"abstract":166,"tags":169},[],[167,168],"数字孪生","digital twin",[28],{"code":78,"score":56,"supported":49,"title":171,"abstract":172,"tags":174},[],[30,173],"remote sensing",[30],{"code":176,"score":64,"supported":49,"title":177,"abstract":178,"tags":181},"A2",[],[179,180],"小麦","wheat",[],{"code":183,"score":64,"supported":49,"title":184,"abstract":185,"tags":188},"A3",[],[186,187],"玉米","maize",[],{"code":190,"score":64,"supported":49,"title":191,"abstract":192,"tags":195},"A5",[],[193,194],"马铃薯","potato",[],{"code":197,"score":72,"supported":49,"title":198,"abstract":199,"tags":201},"B2",[],[200],"葡萄",[],[203,204],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":206,"openalex_id":208,"authors":209,"venue":120,"cited_by_count":102,"oa_url":117,"card":225,"direction":108,"ingested_from":110},"W7223088921",[210,213,216,219,222],{"name":211,"orcid":212},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":214,"orcid":215},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":217,"orcid":218},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":220,"orcid":221},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":223,"orcid":224},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":226,"method":227,"finding":228,"direction":229,"opportunity":230},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","农业遥感与作物表型","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","2026-10-22T11:20:24.526683Z",{"id":233,"title":234,"url":235,"summary":236,"summary_zh":237,"content":9,"source_name":238,"source_url":235,"published_at":121,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":239,"score_detail":240,"sources":242,"tags":244,"primary_topic":247,"secondary_topics":248,"classification":249,"search_phrases":274,"slug":277,"view_count":102,"doi":278,"paper":279,"created_at":289},5554,"Smart Agriculture using IoT","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531185","The Smart Agriculture Using IoT system uses different sensors installed in the agricultural field to collect data about soil and environmental conditions. The collected data is sent to a microcontroller, such as an ESP32 or Arduino with a suitable communication module. When the soil moisture level becomes lower than the required level, the system can automatically switch on the water pump. When sufficient moisture is detected, the pump switches off. Farmers can also monitor field conditions through a mobile application or web dashboard. This system reduces manual work and helps farmers use water and other resources efficiently.","基于物联网的智慧农业系统利用安装在农田中的各类传感器采集土壤与环境状况数据。采集到的数据被传送至微控制器，如ESP32或配备合适通信模块的Arduino。当土壤湿度低于所需水平时，系统可自动开启水泵；当检测到湿度充足时，水泵则关闭。农民还可通过移动应用程序或网页仪表盘监测田间状况。该系统减少了人工劳作，帮助农民高效利用水资源及其他资源。","Zenodo (CERN European Organization for Nuclear Research)",49,{"impact":22,"substance":125,"depth":14,"authority":126,"freshness":14,"relevant":23,"comment":241},"论文介绍基于ESP32\u002FArduino的土壤湿度自动灌溉与远程监测方案，属常规技术实现，方法新颖性有限，但主题贴合智慧农业且时效性强，可作为一般资讯收录。",[243],{"name":238,"url":235},[28,29,245,246],"智能灌溉","土壤墒情",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":15,"version":42,"method":43,"evidence":250},{"candidates":251,"agricultural_evidence":49},[252,257,261,268],{"code":47,"score":48,"supported":49,"title":253,"abstract":255,"tags":256},[254],"smart agriculture",[28,254],[28],{"code":34,"score":48,"supported":49,"title":258,"abstract":259,"tags":260},[60],[59,60],[29],{"code":262,"score":56,"supported":49,"title":263,"abstract":264,"tags":267},"H2",[],[265,266],"土壤","soil",[246],{"code":63,"score":269,"supported":49,"title":270,"abstract":271,"tags":273},4,[],[272],"水资源",[245],[275,276],"ESP32 土壤湿度 自动灌溉","农业物联网 移动端监测","ESP32土壤湿度自动灌溉-5554","10.5281\u002Fzenodo.23531185",{"doi":278,"openalex_id":280,"authors":281,"venue":238,"cited_by_count":102,"oa_url":235,"card":284,"direction":108,"ingested_from":110},"W7223137846",[282],{"name":283,"orcid":9},"Prachi Khairnar",{"tldr":285,"method":286,"finding":287,"direction":108,"opportunity":288},"基于物联网传感器实现农田土壤湿度自动监测与灌溉控制。","部署土壤与环境传感器，经ESP32\u002FArduino采集数据并联动水泵。","系统可自动启停水泵并通过手机或网页端远程监控，节水省工。","可引入多源传感融合与预测性灌溉决策，弥补仅靠阈值控制的局限。","2026-10-22T11:20:18.299106Z",{"id":291,"title":292,"url":293,"summary":294,"summary_zh":295,"content":9,"source_name":238,"source_url":293,"published_at":121,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":296,"score_detail":297,"sources":301,"tags":303,"primary_topic":306,"secondary_topics":307,"classification":313,"search_phrases":340,"slug":343,"view_count":102,"doi":344,"paper":345,"created_at":355},5552,"INTEGRATING IoT, REMOTE SENSING, AND ARTIFICIAL INTELLIGENCE FOR SMART IRRIGATION: A REVIEW OF ARCHITECTURES, METHODS, AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531020","This research paper reviews the integration of Internet of Things (IoT), remote sensing, and Artificial Intelligence (AI) in smart irrigation systems. It discusses system architectures, intelligent irrigation techniques, automated water management, key challenges, and future research opportunities to improve water-use efficiency and promote sustainable agriculture.","本文综述了物联网（Internet of Things, IoT）、遥感与人工智能（Artificial Intelligence, AI）在智能灌溉系统中的融合应用，探讨了系统架构、智能灌溉技术、自动化水资源管理、关键挑战及未来研究方向，以提升用水效率并促进可持续农业发展。",75,{"impact":18,"substance":298,"depth":299,"authority":125,"freshness":126,"relevant":23,"comment":300},20,16,"系统综述物联网、遥感与AI融合的智能灌溉架构与方法，属智慧农业细分领域有实质增量的国际综述，值得入选。",[302],{"name":238,"url":293},[131,29,30,304,305],"智慧灌溉","节水农业",{"code":63,"slug":68,"name":135,"path":136,"parent_code":137},[308,309],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"code":78,"slug":310,"name":311,"path":312,"parent_code":38},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":41,"locked":15,"version":42,"method":43,"evidence":314},{"candidates":315,"agricultural_evidence":49},[316,321,325,329,333],{"code":63,"score":317,"supported":49,"title":318,"abstract":319,"tags":320},19,[68],[67,272,68],[304,305],{"code":34,"score":18,"supported":49,"title":322,"abstract":323,"tags":324},[60],[59,162,60],[29],{"code":78,"score":48,"supported":49,"title":326,"abstract":327,"tags":328},[173],[30,173],[30],{"code":140,"score":125,"supported":49,"title":330,"abstract":331,"tags":332},[157],[157],[131],{"code":334,"score":64,"supported":49,"title":335,"abstract":336,"tags":339},"H8",[],[337,338],"可持续农业","sustainable agriculture",[],[341,342],"IoT 遥感 AI 智能灌溉","农业人工智能 农业物联网 智慧灌溉 节水农业","IoT遥感AI智能灌溉-5552","10.5281\u002Fzenodo.23531020",{"doi":344,"openalex_id":346,"authors":347,"venue":238,"cited_by_count":102,"oa_url":293,"card":350,"direction":108,"ingested_from":110},"W7223090389",[348],{"name":349,"orcid":9},"Patil",{"tldr":351,"method":352,"finding":353,"direction":108,"opportunity":354},"综述物联网、遥感与人工智能融合的智能灌溉系统架构、方法与挑战。","文献综述，梳理IoT、遥感与AI集成的系统架构与智能灌溉技术。","融合三者可提升用水效率并推动可持续农业，但仍面临关键挑战。","可针对多源数据融合的实时灌溉决策模型与边缘部署开展实证研究。","2026-10-22T11:20:18.028252Z",{"id":357,"title":358,"url":359,"summary":360,"summary_zh":361,"content":9,"source_name":238,"source_url":359,"published_at":362,"category":13,"cover_url":9,"hotness":363,"is_selected":15,"score":364,"score_detail":365,"sources":367,"tags":371,"primary_topic":373,"secondary_topics":374,"classification":376,"search_phrases":406,"slug":409,"view_count":102,"doi":410,"paper":411,"created_at":428},5474,"Real-World Impact: Transforming Agriculture with Technology A Multi-Stakeholder Review of Smart Monitoring, Precision Irrigation, and Digital Value-Chain Integration","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23510500","There's a deliberate and significant transition from input to information. This article combines peer-reviewed evidence and public-sector data on six interrelated technology clusters that are fundamentally changing on-farm and off-farm decision making artificial intelligence (AI) based Farm Management Platforms, Internet of Things (IoT) smart irrigation, aerial and satellite crop monitoring, blockchain-enabled supply-chain digitisation, sustainability and carbon-tracking instruments, and farm data analytics dashboards. In the literature examined, precision tools have been correlated with 15% to 30% gains in crop yields, 30% to 55% of water saved during irrigation, and 10% to 20% of operational costs reduced, all with a range of variability based on the crop, the geographical region, and the size of the farm. It examines the mechanisms of these effects on the one hand and the differential rates of benefit, on the other, between large commercial farms and smallholders, and the policy and infrastructural conditions that drive actual adoption. It also highlights areas of current evidence which are lacking, such as limited long horizon field trials, poor interoperability standards, and lack of social outcome data. The article ends with a discussion on how to turn pilots into lasting, system-level change in food production, both from the government and enterprise side, as well as from the perspective of small and medium producers.","从投入要素到信息的转变是有意为之且意义重大的。本文结合经同行评审的证据与公共部门数据，聚焦六个相互关联、正在从根本上改变农场内外决策方式的技术集群：基于人工智能（AI）的农场管理平台、物联网（IoT）智能灌溉、航空与卫星作物监测、区块链赋能的供应链数字化、可持续性与碳追踪工具，以及农场数据分析仪表盘。在所考察的文献中，精准工具与作物产量提升15%至30%、灌溉用水节约30%至55%、运营成本降低10%至20%相关联，且这些效果因作物、地理区域和农场规模而存在不同程度的差异。本文一方面考察这些效应的作用机制，另一方面分析大型商业农场与小农户之间受益程度的差异，以及驱动实际采用的政策与基础设施条件。文章还指出了当前证据的薄弱之处，如缺乏长期田间试验、互操作性标准不完善以及社会成效数据缺失。文章最后讨论了如何从政府与企业两侧，以及中小生产者视角出发，将试点项目转化为粮食生产中持久、系统层面的变革。","2026-11-25T00:00:00Z",25,73,{"impact":124,"substance":19,"depth":20,"authority":21,"freshness":102,"relevant":23,"comment":366},"多利益相关方综述整合六类技术集群的量化效益与采纳差异，信息增量足、产业参考价值高，但发布于一个月后，时效性不足，暂不宜进入每日精选。",[368,369],{"name":238,"url":359},{"name":238,"url":370},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[28,131,29,372,133],"区块链溯源",{"code":63,"slug":68,"name":135,"path":136,"parent_code":137},[375],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":41,"locked":15,"version":42,"method":43,"evidence":377},{"candidates":378,"agricultural_evidence":49},[379,383,387,391,397,402],{"code":63,"score":48,"supported":49,"title":380,"abstract":381,"tags":382},[68],[67,68],[133],{"code":34,"score":14,"supported":49,"title":384,"abstract":385,"tags":386},[],[59,162,60],[29],{"code":140,"score":269,"supported":49,"title":388,"abstract":389,"tags":390},[],[157],[131],{"code":392,"score":72,"supported":49,"title":393,"abstract":394,"tags":396},"G2",[],[395],"小农户",[],{"code":398,"score":23,"supported":15,"title":399,"abstract":400,"tags":401},"E4",[],[],[372],{"code":47,"score":23,"supported":15,"title":403,"abstract":404,"tags":405},[],[],[28],[407,408],"Zenodo 智慧农业 多利益相关方综述","精准灌溉 物联网 区块链 供应链","Zenodo智慧农业多利益相关方综述-5474","10.5281\u002Fzenodo.23510500",{"doi":410,"openalex_id":412,"authors":413,"venue":238,"cited_by_count":102,"oa_url":359,"card":422,"direction":108,"ingested_from":110},"W7223034914",[414,417,419],{"name":415,"orcid":416},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":418,"orcid":9},"Dr.T.Baskar",{"name":420,"orcid":421},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":423,"method":424,"finding":425,"direction":426,"opportunity":427},"综述六大农业数字技术集群对农场决策的变革及实际效益与采纳差异。","综合同行评议证据与公共部门数据，多利益相关方综述六类技术。","精准技术可增产15-30%、节水30-55%、降本10-20%，但小农获益不均。","数字乡村与农业信息化","缺乏长期田间试验、互操作标准与社会效益数据，小农采纳机制是研究空白。","2026-10-21T23:30:08.684798Z",{"id":430,"title":431,"url":432,"summary":433,"summary_zh":434,"content":9,"source_name":435,"source_url":432,"published_at":436,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":437,"score_detail":438,"sources":440,"tags":442,"primary_topic":445,"secondary_topics":446,"classification":448,"search_phrases":465,"slug":468,"view_count":102,"doi":469,"paper":470,"created_at":490},5376,"Agri-Synergy: A Hardware–Software Co-Designed Hybrid IoT System for Sensor Interface Standardization and LLM-Driven Interactive Decision Support","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriengineering8100446","This paper presents Agri-Synergy, a hybrid Internet of Things (IoT) system featuring a hardware–software co-design to tackle sensor data heterogeneity and the barriers to effective AI-driven decision support in smart agriculture. Unlike software-based data harmonization, we propose a universal sensor printed circuit board (PCB) with RS485\u002FModbus interface for hardware unification, complemented by on-node edge processing that ensures standardized data from source. To lower the entry barrier for farmers, the system integrates the DeepSeek large language model (LLM) API, combining static agricultural knowledge with real-time sensor data, for context-aware, natural language-based decision support, functioning without local model deployment. Field deployment showed reference-verified sensor accuracy predominantly within manufacturer specifications, stable 4G connectivity during the 4-h benchmark but intermittent dropouts during the 20-day deployment, and remote-control success rates of 96% (normal) and 80% (weak signal at 300 m). Multi-node concurrent transmission resulted in average latency of 238.0 ± 27.1 ms and packet loss below 0.5%. The system achieves a 3–5× cost reduction compared to commercial platforms. These results demonstrate that addressing data heterogeneity from source through hardware unification and software edge processing, while leveraging AI to lower the barrier to system adoption, provides a more reliable foundation for data synchronization and accessible AI-driven analytics.","本文提出Agri-Synergy，一种采用软硬件协同设计的混合物联网（IoT）系统，旨在解决智慧农业中传感器数据异构性问题以及有效AI驱动决策支持的障碍。与基于软件的数据协调方式不同，我们提出了一种带有RS485\u002FModbus接口的通用传感器印刷电路板（PCB）以实现硬件统一，并辅以节点端边缘处理，确保从源头获得标准化数据。为降低农民的使用门槛，该系统集成了DeepSeek大语言模型（LLM）API，将静态农业知识与实时传感器数据相结合，提供上下文感知的、基于自然语言的决策支持，且无需本地部署模型即可运行。现场部署表明，经参考验证的传感器精度基本处于制造商规格范围内；在4小时基准测试期间4G连接稳定，但在20天部署期间出现间歇性断连；远程控制成功率分别为96%（正常信号）和80%（300 m处弱信号）。多节点并发传输的平均延迟为238.0 ± 27.1 ms，丢包率低于0.5%。与商业平台相比，该系统实现了3–5倍的成本降低。这些结果表明，通过硬件统一和软件边缘处理从源头解决数据异构性问题，同时利用AI降低系统采用门槛，可为数据同步和可及的AI驱动分析提供更可靠的基础。","AgriEngineering","2026-10-19T00:00:00Z",76,{"impact":299,"substance":19,"depth":18,"authority":125,"freshness":126,"relevant":23,"comment":439},"硬件统一接口与边缘处理结合大模型决策支持的农业物联网系统论文，方法新颖、有实地部署数据，但属细分技术进展，公共影响有限。",[441],{"name":435,"url":432},[28,131,29,443,444],"大语言模型","传感器标准化",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[447],{"code":140,"slug":141,"name":142,"path":143,"parent_code":38},{"status":41,"locked":15,"version":42,"method":43,"evidence":449},{"candidates":450,"agricultural_evidence":49},[451,455,461],{"code":34,"score":18,"supported":49,"title":452,"abstract":453,"tags":454},[60],[59,162,60],[29],{"code":140,"score":299,"supported":49,"title":456,"abstract":458,"tags":460},[457],"decision support",[459,457],"large language model",[131,443],{"code":47,"score":56,"supported":49,"title":462,"abstract":463,"tags":464},[],[28,254],[28],[466,467],"Agri-Synergy 物联网 传感器","DeepSeek 决策支持","Agri-Synergy物联网传感器-5376","10.3390\u002Fagriengineering8100446",{"doi":469,"openalex_id":471,"authors":472,"venue":435,"cited_by_count":102,"oa_url":432,"card":485,"direction":108,"ingested_from":110},"W7222920424",[473,475,477,479,481,483],{"name":474,"orcid":9},"Leqi Lai",{"name":476,"orcid":9},"Langtao Duan",{"name":478,"orcid":9},"Yuhao Chen",{"name":480,"orcid":9},"Yuhang Zheng",{"name":482,"orcid":9},"Jinyu Yang",{"name":484,"orcid":9},"Mingyao Wen",{"tldr":486,"method":487,"finding":488,"direction":108,"opportunity":489},"提出软硬件协同的混合物联网系统，统一传感器接口并用大模型提供交互式决策支持。","通用RS485\u002FModbus传感器PCB、节点边缘处理、DeepSeek大模型A","传感器精度达标，多节点延迟238ms、丢包低于0.5%，成本降低3-5倍。","可研究弱信号下通信可靠性提升与边缘-云端LLM协同的长期田间鲁棒性。","2026-10-20T23:30:08.319698Z",{"id":492,"title":493,"url":494,"summary":495,"summary_zh":496,"content":9,"source_name":497,"source_url":494,"published_at":498,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":499,"score_detail":500,"sources":502,"tags":504,"primary_topic":507,"secondary_topics":513,"classification":520,"search_phrases":561,"slug":564,"view_count":102,"doi":9,"paper":565,"created_at":583},5313,"Real-Time IoT-Integrated Ensemble LSTM–Bi-LSTM Framework for Greenhouse Microclimate Forecasting","https:\u002F\u002Fwasrpublication.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F512","Accurate prediction of greenhouse microclimates is essential for optimizing crop growth and resource utilization in precision agriculture. The research presents an IoT-integrated ensemble deep learning framework for real-time greenhouse microclimate forecasting using Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. Environmental variables, including temperature, light intensity, soil moisture, and humidity, were acquired through IoT sensor nodes deployed in a controlled greenhouse environment. A benchmark greenhouse dataset was first used for model development and hyperparameter optimization, while an original IoT-based dataset collected during cucumber, tomato, and pepper cultivation at the Federal University Wukari, Nigeria, was used for validation and real-world deployment. The data were preprocessed through noise filtering, missing value imputation, Z-score-based outlier removal, and Min–Max normalization. A weighted ensemble strategy was then applied to combine LSTM and Bi-LSTM predictions for improved robustness. Experimental results show that the Bi-LSTM model outperformed the LSTM model, achieving R² = 0.9981, MAE = 0.0088, RMSE = 0.0110, and MSE = 0.0001, compared to LSTM results of R² = 0.9956, MAE = 0.0133, RMSE = 0.0167, and MSE = 0.0003. Statistical validation using paired t-test, Wilcoxon signed-rank test, confidence intervals, and multi-run analysis confirmed that the performance improvement is statistically significant and robust. The proposed framework enables accurate real-time microclimate forecasting and intelligent environmental monitoring, providing a scalable solution for precision greenhouse management, improved crop productivity, optimized energy utilization, and efficient irrigation scheduling.","准确预测温室微气候对于精准农业中优化作物生长和资源利用至关重要。本研究提出了一种集成物联网的集成深度学习框架，利用长短期记忆网络（LSTM）和双向长短期记忆网络（Bi-LSTM）进行实时温室微气候预测。温度、光照强度、土壤湿度和湿度等环境变量通过部署在受控温室环境中的物联网传感器节点采集。研究首先使用基准温室数据集进行模型开发和超参数优化，随后使用在尼日利亚武卡里联邦大学黄瓜、番茄和辣椒种植期间采集的原始物联网数据集进行验证和实际部署。数据经过噪声滤波、缺失值插补、基于Z分数的异常值去除和最小—最大归一化预处理。然后采用加权集成策略融合LSTM和Bi-LSTM的预测结果以提高鲁棒性。实验结果表明，Bi-LSTM模型优于LSTM模型，其R² = 0.9981、MAE = 0.0088、RMSE = 0.0110、MSE = 0.0001，而LSTM的结果为R² = 0.9956、MAE = 0.0133、RMSE = 0.0167、MSE = 0.0003。采用配对t检验、Wilcoxon符号秩检验、置信区间和多轮运行分析进行的统计验证证实，性能提升具有统计显著性和鲁棒性。所提出的框架能够实现准确的实时微气候预测和智能环境监测，为精准温室管理、提高作物生产力、优化能源利用和高效灌溉调度提供了可扩展的解决方案。","World Journal of Multidisciplinary Studies","2026-10-16T00:00:00Z",67,{"impact":125,"substance":19,"depth":20,"authority":126,"freshness":22,"relevant":23,"comment":501},"方法扎实、验证充分，但属单点应用研究，公共影响有限，适合作为技术前沿条目而非头条精选。",[503],{"name":497,"url":494},[28,131,29,505,506],"温室环境","小气候预测",{"code":508,"slug":509,"name":510,"path":511,"parent_code":512},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[514,515],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"code":516,"slug":517,"name":518,"path":519,"parent_code":512},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",{"status":41,"locked":15,"version":42,"method":43,"evidence":521},{"candidates":522,"agricultural_evidence":49},[523,527,534,543,547,553,557],{"code":34,"score":48,"supported":49,"title":524,"abstract":525,"tags":526},[60],[59,60],[29],{"code":508,"score":528,"supported":49,"title":529,"abstract":531,"tags":533},14,[530],"greenhouse",[532,530],"温室",[],{"code":516,"score":126,"supported":49,"title":535,"abstract":536,"tags":542},[],[537,538,539,540,541],"番茄","辣椒","黄瓜","tomato","cucumber",[],{"code":47,"score":56,"supported":49,"title":544,"abstract":545,"tags":546},[],[53,51],[28],{"code":140,"score":56,"supported":49,"title":548,"abstract":549,"tags":552},[],[550,551],"深度学习","deep learning",[131],{"code":262,"score":64,"supported":49,"title":554,"abstract":555,"tags":556},[],[265,266],[],{"code":63,"score":64,"supported":49,"title":558,"abstract":559,"tags":560},[],[67,68],[],[562,563],"温室 小气候 LSTM 预测","尼日利亚 温室 IoT 黄瓜 番茄","温室小气候LSTM预测-5313",{"doi":9,"openalex_id":566,"authors":567,"venue":497,"cited_by_count":102,"oa_url":9,"card":578,"direction":108,"ingested_from":110},"W7222909837",[568,570,572,574,576],{"name":569,"orcid":9},"Siman Emmanuel*",{"name":571,"orcid":9},"Edward Onyebueke Agu",{"name":573,"orcid":9},"Musa Gani",{"name":575,"orcid":9},"Oladunjoye John Abiodun",{"name":577,"orcid":9},"Anagu Emmanuel John",{"tldr":579,"method":580,"finding":581,"direction":108,"opportunity":582},"提出物联网集成LSTM与Bi-LSTM加权集成框架，实时预测温室微气候。","IoT传感器采集温光湿数据，经预处理后用LSTM-Bi-LSTM加权集成建模。","Bi-LSTM优于LSTM，R²达0.9981，统计检验证实性能提升显著稳健。","可探索边缘端轻量化部署与多作物跨温室迁移学习，提升实时预测泛化性。","2026-10-19T23:30:13.295236Z"]