[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4987":3,"related-4987":147},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":118,"slug":121,"view_count":122,"doi":123,"paper":124,"created_at":146},4987,"Smart Agriculture Research in Date Palm (Phoenix dactylifera L.) Production: A Comprehensive Bibliometric Study","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fplants15203137","The date palm (Phoenix dactylifera L.) is fundamental to food security, livelihoods, and agro-ecosystem sustainability across the Arabian Peninsula, yet its production is increasingly constrained by water scarcity, salinity, climate change, and biotic stresses. Smart agriculture technologies offer opportunities to improve crop monitoring, resource-use efficiency, and decision-making; however, their application in date palm production remains fragmented and insufficiently validated. This study evaluates the development, geographic distribution, technological focus, research approaches, and thematic structure of smart agriculture research on date palms in the Arabian Peninsula and other major date-producing countries. A bibliometric analysis was conducted using peer-reviewed English-language articles and reviews indexed in Scopus and Web of Science from 2016 to 2026, with 2026 representing a partial year. Following screening and duplicate removal, 164 published studies were retained. Publication output increased gradually, although the trend was not statistically significant (β = 1.25 articles year−1, p = 0.068; Mann–Kendall τ = 0.489, p = 0.060). The literature was concentrated in the Gulf Cooperation Council (GCC) region, with Saudi Arabia contributing 54 publications (32.9%). General agronomy\u002Fother topics accounted for 46.3% of publications, while remote sensing\u002FUAVs (17.1%), GIS\u002Fgeospatial technologies (14.0%), IoT\u002Fsensors (13.4%), and AI\u002Fmachine learning (9.8%) represented the leading specific technology categories. Water and irrigation management dominated thematic applications (46.3%), whereas Smart Farming\u002FIoT accounted for only 11.0%. Observational and monitoring studies comprised 50.0% of publications, compared with only 6.1% involving field experiments, indicating limited field-scale validation. Internationally co-authored studies received significantly more citations than domestically authored studies (16.55 vs. 8.68; p = 0.0007). Thematic analysis identified four major clusters: geospatial monitoring, water management, pest control, and sustainability, with AI\u002Fmachine learning showing marked growth after 2022. Altogether, the field is expanding but remains fragmented, highlighting the need for integrated IoT and sensor networks, rigorous field trials, AI-driven decision-support systems, and stronger regional and international collaboration to enable scalable, context-specific smart agriculture for date palm production.","海枣（Phoenix dactylifera L.）对阿拉伯半岛的粮食安全、生计和农业生态系统可持续性至关重要，然而其生产日益受到水资源短缺、盐碱化、气候变化和生物胁迫的制约。智慧农业技术为改善作物监测、资源利用效率和决策提供了机遇；然而，其在海枣生产中的应用仍然零散且缺乏充分验证。本研究评估了阿拉伯半岛及其他主要海枣生产国海枣智慧农业研究的发展、地理分布、技术重点、研究方法和主题结构。采用文献计量学分析方法，基于Scopus和Web of Science数据库中2016年至2026年索引的同行评审英文论文和综述，其中2026年为部分年份。经过筛选和去重后，共保留164篇已发表研究。发文量逐渐增长，但该趋势未达到统计学显著性（β = 1.25篇\u002F年，p = 0.068；Mann–Kendall τ = 0.489，p = 0.060）。文献集中于海湾合作委员会（GCC）地区，其中沙特阿拉伯贡献了54篇论文（32.9%）。一般农学\u002F其他主题占论文总数的46.3%，而遥感\u002F无人机（17.1%）、GIS\u002F地理空间技术（14.0%）、物联网\u002F传感器（13.4%）和人工智能\u002F机器学习（9.8%）为主要的具体技术类别。水资源与灌溉管理在主题应用中占主导地位（46.3%），而智慧农业\u002F物联网仅占11.0%。观测与监测类研究占论文总数的50.0%，而涉及田间试验的仅占6.1%，表明田间尺度的验证有限。国际合著论文的引用次数显著高于国内独著论文（16.55 vs. 8.68；p = 0.0007）。主题分析识别出四大聚类：地理空间监测、水资源管理、病虫害防治和可持续性，其中人工智能\u002F机器学习在2022年后呈现显著增长。总体而言，该领域正在扩展但仍显零散，凸显了整合物联网与传感器网络、严格的田间试验、人工智能驱动的决策支持系统以及加强区域和国际合作的必要性，以推动可规模化、因地制宜的海枣生产智慧农业。",null,"Plants","2026-10-14T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"基于Scopus与WoS的164篇文献计量分析，揭示枣椰树智慧农业研究碎片化、田间验证不足，对干旱区智慧农业布局有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","农业遥感","农业物联网","枣椰树",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":47},[44,54,61,68,76,83,90,98,105,112],{"code":45,"score":46,"supported":47,"title":48,"abstract":50,"tags":53},"I7",17,true,[49],"bibliometric",[51,52,49],"文献计量","研究方法",[],{"code":55,"score":56,"supported":47,"title":57,"abstract":59,"tags":60},"I1",15,[58],"smart agriculture",[26,58],[26],{"code":32,"score":62,"supported":47,"title":63,"abstract":64,"tags":67},9,[],[65,66,33],"灌溉","水资源",[],{"code":69,"score":70,"supported":47,"title":71,"abstract":72,"tags":75},"I2",7,[],[73,74],"机器学习","machine learning",[27],{"code":77,"score":70,"supported":47,"title":78,"abstract":79,"tags":82},"I3",[],[80,81],"遥感","remote sensing",[28],{"code":84,"score":70,"supported":47,"title":85,"abstract":86,"tags":89},"I5",[],[87,88],"物联网","IoT",[29],{"code":91,"score":92,"supported":47,"title":93,"abstract":94,"tags":97},"A8",6,[],[95,96],"粮食安全","food security",[],{"code":99,"score":92,"supported":47,"title":100,"abstract":101,"tags":104},"H7",[],[102,103],"气候变化","climate change",[],{"code":106,"score":107,"supported":47,"title":108,"abstract":109,"tags":111},"H6",3,[],[110],"农业生态",[],{"code":113,"score":107,"supported":47,"title":114,"abstract":115,"tags":117},"I4",[],[116],"GIS",[],[119,120],"枣椰树 智慧农业 文献计量","农业人工智能 农业物联网 农业遥感 智慧农业","枣椰树智慧农业文献计量-4987",0,"10.3390\u002Fplants15203137",{"doi":123,"openalex_id":125,"authors":126,"venue":10,"cited_by_count":122,"oa_url":6,"card":139,"direction":143,"ingested_from":145},"W7221786893",[127,130,133,136],{"name":128,"orcid":129},"Sajid Safeer","https:\u002F\u002Forcid.org\u002F0000-0002-0120-5900",{"name":131,"orcid":132},"Adnan Nadeem","https:\u002F\u002Forcid.org\u002F0000-0001-9622-150X",{"name":134,"orcid":135},"Sajid Maqsood","https:\u002F\u002Forcid.org\u002F0000-0003-2099-8392",{"name":137,"orcid":138},"Cataldo Pulvento","https:\u002F\u002Forcid.org\u002F0000-0002-2650-1204",{"tldr":140,"method":141,"finding":142,"direction":143,"opportunity":144},"用文献计量分析2016-2026年枣椰树智慧农业研究，发现领域增长但碎片化、田间验证不足。","Scopus与Web of Science文献计量分析，164篇论文，含趋势检验","研究集中于海湾地区，遥感与灌溉为主，田间试验仅6.1%，国际合作论文引用更高。","智慧农业 \u002F 农业物联网","枣椰树智慧农业缺乏IoT传感器集成与田间验证，AI决策支持系统是待填补的研究空白。","openalex","2026-10-15T23:30:08.558630Z",{"total":92,"page":21,"page_size":92,"items":148},[149,233,305,376,442,500],{"id":150,"title":151,"url":152,"summary":153,"summary_zh":154,"content":9,"source_name":155,"source_url":152,"published_at":156,"category":12,"cover_url":9,"hotness":157,"is_selected":14,"score":158,"score_detail":159,"sources":163,"tags":167,"primary_topic":170,"secondary_topics":171,"classification":177,"search_phrases":210,"slug":213,"view_count":122,"doi":214,"paper":215,"created_at":232},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-11-25T00:00:00Z",25,73,{"impact":18,"substance":160,"depth":46,"authority":161,"freshness":122,"relevant":21,"comment":162},21,13,"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。",[164,165],{"name":155,"url":152},{"name":155,"url":166},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[26,27,29,168,169],"区块链溯源","精准灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[172],{"code":84,"slug":173,"name":174,"path":175,"parent_code":176},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":178},{"candidates":179,"agricultural_evidence":47},[180,184,189,195,201,206],{"code":32,"score":56,"supported":47,"title":181,"abstract":182,"tags":183},[33],[65,33],[169],{"code":84,"score":13,"supported":47,"title":185,"abstract":186,"tags":188},[],[87,187,88],"internet of things",[29],{"code":69,"score":190,"supported":47,"title":191,"abstract":192,"tags":194},4,[],[193],"artificial intelligence",[27],{"code":196,"score":107,"supported":47,"title":197,"abstract":198,"tags":200},"G2",[],[199],"小农户",[],{"code":202,"score":21,"supported":14,"title":203,"abstract":204,"tags":205},"E4",[],[],[168],{"code":55,"score":21,"supported":14,"title":207,"abstract":208,"tags":209},[],[],[26],[211,212],"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":214,"openalex_id":216,"authors":217,"venue":155,"cited_by_count":122,"oa_url":152,"card":226,"direction":143,"ingested_from":145},"W7223034914",[218,221,223],{"name":219,"orcid":220},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":222,"orcid":9},"Dr.T.Baskar",{"name":224,"orcid":225},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":227,"method":228,"finding":229,"direction":230,"opportunity":231},"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":234,"title":235,"url":236,"summary":237,"summary_zh":238,"content":9,"source_name":239,"source_url":236,"published_at":240,"category":12,"cover_url":9,"hotness":157,"is_selected":14,"score":241,"score_detail":242,"sources":245,"tags":249,"primary_topic":251,"secondary_topics":252,"classification":254,"search_phrases":283,"slug":286,"view_count":21,"doi":9,"paper":287,"created_at":304},4671,"A COMPREHENSIVE REVIEW ON IOT AND EDGE AI-BASED PRECISION AGRICULTURE SYSTEM FOR WATER OPTIMIZATION","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F316","Water scarcity and inefficient irrigation practices remain major challenges in modern agriculture, leading to reduced crop yield, resource wastage, and increased production costs. The proposed project, “IoT and Edge AI-Based Precision Agriculture System for Water Optimisation,” aims to develop an intelligent, real-time irrigation management system that optimises water usage through data-driven decision-making at the edge. The system integrates Internet of Things (IoT) sensors such as soil moisture, temperature, humidity, and light intensity sensors to continuously monitor field conditions. The collected data is processed locally using an Edge AI module, enabling real-time analysis and predictive irrigation control without relying entirely on cloud connectivity. By deploying machine learning algorithms at the edge, the system can determine optimal watering schedules based on soil conditions, crop requirements, and environmental factors, thereby minimising water wastage and ensuring efficient irrigation. A cloud platform is incorporated for long-term data storage, visualisation, and remote monitoring via a web or mobile dashboard. This hybrid architecture enhances scalability, reduces latency, and improves reliability in rural areas with limited internet connectivity. Automated irrigation control using smart valves or pumps ensures precise water delivery tailored to specific crop zones.","水资源短缺和灌溉效率低下仍是现代农业面临的主要挑战，导致作物减产、资源浪费和生产成本增加。本项目“基于物联网与边缘人工智能的水资源优化精准农业系统”旨在开发一种智能实时灌溉管理系统，通过边缘端的数据驱动决策来优化水资源利用。该系统集成了物联网（IoT）传感器，如土壤湿度、温度、湿度和光照强度传感器，以持续监测田间状况。采集的数据通过边缘人工智能模块在本地进行处理，实现实时分析和预测性灌溉控制，而无需完全依赖云连接。通过在边缘端部署机器学习算法，系统能够根据土壤条件、作物需求和环境因素确定最佳灌溉计划，从而最大限度地减少水资源浪费并确保高效灌溉。系统还包含一个云平台，用于长期数据存储、可视化以及通过网页或移动端仪表盘进行远程监控。这种混合架构增强了可扩展性，降低了延迟，并提高了互联网连接有限的农村地区的可靠性。利用智能阀门或水泵的自动灌溉控制可确保针对特定作物区域进行精准供水。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",53,{"impact":243,"substance":19,"depth":161,"authority":92,"freshness":20,"relevant":21,"comment":244},12,"综述性论文，系统梳理IoT与边缘AI在水肥优化中的应用，方法框架清晰但缺乏原创数据与实证，属细分领域参考性内容。",[246,247],{"name":239,"url":236},{"name":239,"url":248},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F315",[26,27,29,250,169],"边缘计算",{"code":84,"slug":173,"name":174,"path":175,"parent_code":176},[253],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":255},{"candidates":256,"agricultural_evidence":47},[257,262,268,272,276],{"code":84,"score":258,"supported":47,"title":259,"abstract":260,"tags":261},19,[88],[87,187,88],[29,250],{"code":55,"score":56,"supported":47,"title":263,"abstract":265,"tags":267},[264],"precision agriculture",[266,264],"精准农业",[26],{"code":32,"score":13,"supported":47,"title":269,"abstract":270,"tags":271},[],[65,66,33],[169],{"code":69,"score":70,"supported":47,"title":273,"abstract":274,"tags":275},[],[73,74],[27],{"code":277,"score":92,"supported":47,"title":278,"abstract":279,"tags":282},"H2",[],[280,281],"土壤","soil",[],[284,285],"IoT 边缘AI 精准灌溉","智慧灌溉 土壤墒情 传感器","IoT边缘AI精准灌溉-4671",{"doi":9,"openalex_id":288,"authors":289,"venue":239,"cited_by_count":122,"oa_url":236,"card":299,"direction":143,"ingested_from":145},"W7221411136",[290,292,294,296],{"name":291,"orcid":9},"Om Gaikwad",{"name":293,"orcid":9},"Shriraj Chavan",{"name":295,"orcid":9},"Krushnadev Gaikwad",{"name":297,"orcid":298},"Swapnil R. Takale","https:\u002F\u002Forcid.org\u002F0009-0001-3636-4450",{"tldr":300,"method":301,"finding":302,"direction":143,"opportunity":303},"综述基于物联网与边缘AI的精准农业系统，实现实时灌溉优化与节水。","集成土壤湿度等IoT传感器与边缘AI，本地处理数据并云端存储可视化。","边缘AI可实时预测灌溉需求，减少水浪费并适应网络受限农村环境。","可探索边缘AI模型轻量化与多源数据融合，提升复杂农田环境下的灌溉决策精度。","2026-10-11T23:30:16.298334Z",{"id":306,"title":307,"url":308,"summary":309,"summary_zh":310,"content":9,"source_name":311,"source_url":308,"published_at":240,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":312,"score_detail":313,"sources":316,"tags":318,"primary_topic":321,"secondary_topics":322,"classification":327,"search_phrases":353,"slug":356,"view_count":92,"doi":357,"paper":358,"created_at":375},4567,"An embedded deep learning–fuzzy logic framework for intelligent and automated irrigation in smart farming","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-72706-5","Water scarcity and the need to improve resource efficiency have made intelligent and automated irrigation management an important research challenge in smart farming. This study proposes IrrigaDL-FL, a low-cost automated smart irrigation framework based on the internet of things, integrating intelligent data routing with adaptive irrigation decision-making. The proposed framework consists of two complementary components. First, a Deep Learning–Fuzzy Logic (DL-FL) framework is integrated with the Open Shortest Path First (OSPF) protocol to enhance data routing efficiency in the network. The DL-FL framework combines these heterogeneous characteristics to identify suitable next-hop nodes and establish energy-efficient routing paths. Second, irrigation decision-making is performed using a lightweight Convolutional Neural Network–Fuzzy Logic Controller (CNN-FLC). The CNN-FLC processes sensor-derived and crop-related information, including air temperature, relative humidity, soil moisture, evapotranspiration rate, crop growth coefficient, crop water requirement, planting date, cumulative rainfall, recent rainfall, irrigation history, crop growth stage, and the difference between actual and target soil moisture. These inputs are used to characterize crop hydration stress and dynamically determine the appropriate irrigation program and sprinkler activation duration. The proposed IrrigaDL-FL framework is evaluated through simulations with different numbers of sensor nodes over predefined routing rounds and is compared with several existing approaches. The results demonstrate that IrrigaDL-FL achieves superior performance in terms of network lifetime, residual energy, energy variance, number of transmitted packets, and average path length.","水资源短缺与提高资源效率的需求，使智能化和自动化灌溉管理成为智慧农业中的重要研究挑战。本研究提出IrrigaDL-FL，一种基于物联网的低成本自动化智能灌溉框架，将智能数据路由与自适应灌溉决策相结合。该框架由两个互补部分组成。首先，将深度学习-模糊逻辑（DL-FL）框架与开放最短路径优先（OSPF）协议集成，以提高网络中的数据路由效率。DL-FL框架结合这些异构特征来识别合适的下一跳节点并建立节能路由路径。其次，灌溉决策通过轻量级卷积神经网络-模糊逻辑控制器（CNN-FLC）执行。CNN-FLC处理来自传感器的信息和作物相关信息，包括空气温度、相对湿度、土壤湿度、蒸散速率、作物生长系数、作物需水量、种植日期、累计降雨量、近期降雨量、灌溉历史、作物生长阶段以及实际土壤湿度与目标土壤湿度之间的差值。这些输入用于表征作物水分胁迫，并动态确定合适的灌溉方案和喷灌器启动时长。所提出的IrrigaDL-FL框架通过在预定义路由轮次下对不同数量传感器节点进行仿真评估，并与若干现有方法进行比较。结果表明，IrrigaDL-FL在网络寿命、剩余能量、能量方差、传输数据包数量和平均路径长度方面均取得了更优性能。","Scientific Reports",77,{"impact":314,"substance":160,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":315},16,"提出低成本物联网智能灌溉框架，融合深度学习与模糊逻辑优化路由与灌溉决策，方法新颖、仿真验证充分，对智慧农业节水研究有参考价值。",[317],{"name":311,"url":308},[26,27,29,319,320],"智能灌溉","节水农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[323],{"code":69,"slug":324,"name":325,"path":326,"parent_code":176},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":39,"locked":14,"version":40,"method":41,"evidence":328},{"candidates":329,"agricultural_evidence":47},[330,335,341,345,349],{"code":32,"score":258,"supported":47,"title":331,"abstract":332,"tags":334},[33],[65,66,33,333],"evapotranspiration",[319,320],{"code":69,"score":56,"supported":47,"title":336,"abstract":338,"tags":340},[337],"deep learning",[339,337],"深度学习",[27],{"code":84,"score":70,"supported":47,"title":342,"abstract":343,"tags":344},[],[87,187],[29],{"code":277,"score":92,"supported":47,"title":346,"abstract":347,"tags":348},[],[280,281],[],{"code":55,"score":190,"supported":47,"title":350,"abstract":351,"tags":352},[],[26],[26],[354,355],"IrrigaDL-FL 智能灌溉","CNN-FLC 灌溉决策","IrrigaDL-FL智能灌溉-4567","10.1038\u002Fs41598-026-72706-5",{"doi":357,"openalex_id":359,"authors":360,"venue":311,"cited_by_count":122,"oa_url":369,"card":370,"direction":143,"ingested_from":145},"W7221040139",[361,363,365,367],{"name":362,"orcid":9},"Ze Luo",{"name":364,"orcid":9},"Zhuo Yang",{"name":366,"orcid":9},"Yi Hu",{"name":368,"orcid":9},"Jun Wang","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-72706-5_reference.pdf",{"tldr":371,"method":372,"finding":373,"direction":143,"opportunity":374},"提出IrrigaDL-FL低成本物联网智能灌溉框架，融合深度学习与模糊逻辑优化路由和灌溉决策。","用CNN-模糊逻辑控制器处理多源传感器与作物数据，结合OSPF协议做节能路由。","在仿真中网络寿命、剩余能量、能耗方差、传输包数和平均路径长度均优于现有方法。","可探索真实农田部署下多作物、多气候场景的泛化能力与边缘计算资源约束优化。","2026-10-10T23:30:11.306685Z",{"id":377,"title":378,"url":379,"summary":380,"summary_zh":381,"content":9,"source_name":382,"source_url":379,"published_at":383,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":384,"score_detail":385,"sources":387,"tags":389,"primary_topic":391,"secondary_topics":392,"classification":395,"search_phrases":423,"slug":426,"view_count":190,"doi":9,"paper":427,"created_at":441},4076,"Automated System for Plant Health Monitoring and Stress Diagnosis Using IoT and Machine Learning","https:\u002F\u002Fwajes.isroset.org\u002Findex.php\u002Fj\u002Farticle\u002Fview\u002F208","Irrigation decisions on smallholder farms are still taken subjectively, and both root-zone dehydration and over-saturation measurably depress yield, making low-cost automated plant-stress diagnosis an urgent requirement in water-scarce agricultural regions. This study designs, implements and validates a decentralized system that closes the control loop between field sensing and irrigation actuation by combining Internet of Things (IoT) hardware with a cloud-hosted predictive Machine Learning (ML) model. The edge node is built on an ESP32 system-on-chip integrated with a calibrated capacitive soil-moisture probe and a DHT22 temperature and humidity array. Serialized telemetry is transmitted over a non-blocking, encrypted wireless channel to a Flask web service, where a 150-tree Random Forest classifier trained on 5,000 labelled multi-sensor records assigns the plant to one of three physiological states Optimal, Temperature Stress or Dehydration Stress and returns an actuator control bit that drives a micro-irrigation pump. An engineered Evapotranspiration Proxy Index couples ambient temperature and relative humidity into a single transpiration-sensitive feature. Capacitive calibration was validated against the gravimetric standard on ten soil cores, yielding a Pearson correlation coefficient of 0.948. Over a continuous forty-eight-hour open-air evaluation the classifier achieved an overall accuracy of 96.8%, with 98.2% precision and 97.9% recall on the critical Dehydration Stress class, and a mean end-to-end round-trip latency of 1.84 seconds. Deep-sleep duty cycling reduced the mean node current to 0.65 mA, corresponding to an estimated 159 days of unattended operation on a single 2,500 mAh cell. The results demonstrate that a low-cost edge–cloud architecture can deliver dependable, timely and energy-sustainable irrigation control for micro-precision agriculture without server-grade hardware.","小农户的灌溉决策仍依赖主观判断，而根区脱水和过度饱和均会显著降低产量，这使得低成本自动化植物胁迫诊断成为缺水农业地区的迫切需求。本研究设计、实现并验证了一种去中心化系统，通过将物联网（IoT）硬件与云端预测机器学习（ML）模型相结合，闭合了田间传感与灌溉执行之间的控制回路。边缘节点基于ESP32片上系统构建，集成了经过校准的电容式土壤水分探头和DHT22温湿度传感器阵列。序列化遥测数据通过非阻塞加密无线信道传输至Flask Web服务，在此处，一个基于5,000条带标签多传感器记录训练的150棵树的随机森林分类器将植物判定为三种生理状态之一——最优、温度胁迫或脱水胁迫——并返回驱动微灌泵的执行器控制位。所设计的蒸散代理指数将环境温度和相对湿度耦合为单一蒸腾敏感特征。电容校准在十个土芯上与重量法标准进行验证，得到皮尔逊相关系数为0.948。在连续四十八小时的户外评估中，分类器总体准确率达到96.8%，在关键的脱水胁迫类别上精确率为98.2%、召回率为97.9%，平均端到端往返延迟为1.84秒。深度睡眠占空比控制将节点平均电流降至0.65 mA，相当于在单节2,500 mAh电池上估计可无人值守运行159天。结果表明，低成本边缘—云架构无需服务器级硬件即可为微精准农业提供可靠、及时且能源可持续的灌溉控制。","World Academics Journal of Engineering Sciences","2026-09-30T00:00:00Z",75,{"impact":314,"substance":18,"depth":17,"authority":161,"freshness":92,"relevant":21,"comment":386},"低成本边缘-云架构实现植物胁迫自动诊断与灌溉闭环，数据扎实、结论可靠，对智慧农业节水灌溉有参考价值。",[388],{"name":382,"url":379},[26,27,29,169,390],"土壤墒情",{"code":84,"slug":173,"name":174,"path":175,"parent_code":176},[393,394],{"code":69,"slug":324,"name":325,"path":326,"parent_code":176},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":396},{"candidates":397,"agricultural_evidence":47},[398,402,406,410,414,418],{"code":84,"score":17,"supported":47,"title":399,"abstract":400,"tags":401},[88],[87,187,88],[29],{"code":69,"score":56,"supported":47,"title":403,"abstract":404,"tags":405},[74],[73,74],[27],{"code":32,"score":13,"supported":47,"title":407,"abstract":408,"tags":409},[],[65,33,333],[169],{"code":277,"score":70,"supported":47,"title":411,"abstract":412,"tags":413},[],[280,281],[390],{"code":55,"score":70,"supported":47,"title":415,"abstract":416,"tags":417},[],[266,264],[26],{"code":196,"score":92,"supported":47,"title":419,"abstract":420,"tags":422},[],[199,421],"smallholder",[],[424,425],"ESP32 土壤湿度 灌溉 机器学习","随机森林 植物胁迫 诊断","ESP32土壤湿度灌溉机器学习-4076",{"doi":9,"openalex_id":428,"authors":429,"venue":382,"cited_by_count":122,"oa_url":379,"card":436,"direction":143,"ingested_from":145},"W7217836036",[430,432,434],{"name":431,"orcid":9},"Christian Athanas Mwakyusa",{"name":433,"orcid":9},"Allen Deogratius Mwalekwa",{"name":435,"orcid":9},"Gift Geofrey Mushi",{"tldr":437,"method":438,"finding":439,"direction":143,"opportunity":440},"设计并验证了基于ESP32与随机森林的物联网闭环灌溉系统，实现植物胁迫自动诊断。","ESP32、电容式土壤湿度传感器、DHT22、随机森林分类器、Flask云服务。","48小时测试分类准确率96.8%，脱水胁迫召回率97.9%，节点续航约159天。","可探索多作物泛化、极端环境鲁棒性及边缘端轻量化模型部署以降低云依赖。","2026-10-03T23:30:18.857006Z",{"id":443,"title":444,"url":445,"summary":446,"summary_zh":447,"content":9,"source_name":155,"source_url":445,"published_at":448,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":449,"score_detail":450,"sources":452,"tags":454,"primary_topic":458,"secondary_topics":459,"classification":461,"search_phrases":481,"slug":484,"view_count":122,"doi":485,"paper":486,"created_at":499},5591,"Hierarchical Conformal Risk Control for Artificial Intelligence Agriculture Irrigation Scheduling Under Unmodeled Infiltration: A Computer Safety-Guarantee Evaluation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23543665","Irrigation scheduling algorithms that rely on soil-moisture forecasts are vulnerable to unmodeled infiltration, the process by which water bypasses the root zone through preferential pathways that resistance-based soil models cannot represent. The framework is evaluated through a replication-based design study that characterises the operating characteristics of the before-after-control-impact estimator used to audit scheduler performance, under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64. Power was estimated across 2000 design cells spanning cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 54 clusters are required to detect a 5-percentage-point effect at 80% power, and that the minimum detectable effect declines steeply as cluster count rises from 8 to 32. The contribution is a formal safety guarantee for irrigation decision support that remains valid when the infiltration model is wrong, together with an audit design that makes the guarantee empirically checkable.","依赖土壤湿度预报的灌溉调度算法容易受到未建模入渗的影响，即水分通过优先路径绕过根区的过程，而基于阻力的土壤模型无法表征这一过程。该框架通过一项基于重复的设计研究进行评估，该研究刻画了用于审计调度器性能的前后对照影响估计量的运行特性，其所依据的声明方差结构为：组间标准差6.0、残差标准差4.5、组内相关系数0.64。功效估计覆盖2000个设计单元，涵盖聚类数8、16、24和32以及效应量3、5和8个百分点。分析表明，在80%功效下检测5个百分点效应需要54个聚类，且最小可检测效应随聚类数从8增至32而急剧下降。本文的贡献在于为灌溉决策支持提供了一种正式的安全保证，该保证在入渗模型错误时仍然有效，同时提出了一种使该保证可经实证检验的审计设计。","2026-10-21T00:00:00Z",69,{"impact":20,"substance":18,"depth":17,"authority":243,"freshness":62,"relevant":21,"comment":451},"该研究提出在入渗模型失配条件下仍有效的灌溉决策安全保证框架，并给出可审计的试验设计，方法新颖、结论可靠，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[453],{"name":155,"url":445},[26,27,455,456,457],"灌溉决策","安全保证","试验设计",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[460],{"code":69,"slug":324,"name":325,"path":326,"parent_code":176},{"status":39,"locked":14,"version":40,"method":41,"evidence":462},{"candidates":463,"agricultural_evidence":47},[464,468,473,477],{"code":32,"score":56,"supported":47,"title":465,"abstract":466,"tags":467},[33],[65,33],[455],{"code":69,"score":243,"supported":47,"title":469,"abstract":470,"tags":472},[193],[471],"decision support",[27],{"code":277,"score":92,"supported":47,"title":474,"abstract":475,"tags":476},[],[280,281],[],{"code":55,"score":21,"supported":14,"title":478,"abstract":479,"tags":480},[],[],[26],[482,483],"灌溉调度 安全保证","农业人工智能 安全保证 智慧农业 灌溉决策","灌溉调度安全保证-5591","10.5281\u002Fzenodo.23543665",{"doi":485,"openalex_id":487,"authors":488,"venue":155,"cited_by_count":122,"oa_url":445,"card":493,"direction":497,"ingested_from":145},"W7223131205",[489,491],{"name":490,"orcid":9},"Nassima Benkhelifa",{"name":492,"orcid":9},"Yacine Aït Ahmed",{"tldr":494,"method":495,"finding":496,"direction":497,"opportunity":498},"提出分层保形风险控制，为未建模入渗下的灌溉调度提供安全保证。","分层保形风险控制与基于重复设计的BACI审计，2000个设计单元功效分析。","检测5个百分点效应需54个集群，最小可检测效应随集群数增加陡降。","农业人工智能与决策模型","可将保形风险控制扩展到其他未建模扰动下的农业决策安全保证与审计设计。","2026-10-22T11:21:10.283561Z",{"id":501,"title":502,"url":503,"summary":504,"summary_zh":505,"content":9,"source_name":506,"source_url":503,"published_at":448,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":507,"score_detail":508,"sources":510,"tags":512,"primary_topic":514,"secondary_topics":515,"classification":518,"search_phrases":570,"slug":573,"view_count":122,"doi":574,"paper":575,"created_at":599},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",79,{"impact":17,"substance":18,"depth":17,"authority":243,"freshness":62,"relevant":21,"comment":509},"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。",[511],{"name":506,"url":503},[26,27,80,513,169],"作物水分胁迫",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[516,517],{"code":69,"slug":324,"name":325,"path":326,"parent_code":176},{"code":84,"slug":173,"name":174,"path":175,"parent_code":176},{"status":39,"locked":14,"version":40,"method":41,"evidence":519},{"candidates":520,"agricultural_evidence":47},[521,525,529,533,539,543,550,557,564],{"code":32,"score":56,"supported":47,"title":522,"abstract":523,"tags":524},[33],[65,33],[169],{"code":69,"score":13,"supported":47,"title":526,"abstract":527,"tags":528},[],[73,74,193],[27],{"code":84,"score":62,"supported":47,"title":530,"abstract":531,"tags":532},[],[87,187,88],[],{"code":55,"score":70,"supported":47,"title":534,"abstract":535,"tags":538},[],[536,537],"数字孪生","digital twin",[26],{"code":77,"score":70,"supported":47,"title":540,"abstract":541,"tags":542},[],[80,81],[80],{"code":544,"score":92,"supported":47,"title":545,"abstract":546,"tags":549},"A2",[],[547,548],"小麦","wheat",[],{"code":551,"score":92,"supported":47,"title":552,"abstract":553,"tags":556},"A3",[],[554,555],"玉米","maize",[],{"code":558,"score":92,"supported":47,"title":559,"abstract":560,"tags":563},"A5",[],[561,562],"马铃薯","potato",[],{"code":565,"score":107,"supported":47,"title":566,"abstract":567,"tags":569},"B2",[],[568],"葡萄",[],[571,572],"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":574,"openalex_id":576,"authors":577,"venue":506,"cited_by_count":122,"oa_url":503,"card":593,"direction":143,"ingested_from":145},"W7223088921",[578,581,584,587,590],{"name":579,"orcid":580},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":582,"orcid":583},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":585,"orcid":586},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":588,"orcid":589},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":591,"orcid":592},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":594,"method":595,"finding":596,"direction":597,"opportunity":598},"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"]