[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4989":3,"related-4989":148},{"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":50,"search_phrases":115,"slug":118,"view_count":119,"doi":120,"paper":121,"created_at":147},4989,"Advances in micro-irrigation and supplementary irrigation in millets of dryland ecosystems","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.16312","Dryland ecosystems cover approximately 41 % of the Earth's terrestrial surface and support nearly two billion people. This review synthesises recent advances in micro-irrigation and supplementary irrigation strategies for enhancing millet productivity and resilience in dryland ecosystems. Drylands are characterised by low and erratic rainfall, high evapotranspiration, poor soil fertility and frequent drought stress, making agricultural production vulnerable to climate variability and water scarcity. Millets are drought-tolerant, climate-resilient cereals cultivated in dryland agroecosystems. Despite their resilience, millet productivity remains constrained by increasing rainfall variability, prolonged intra-seasonal dry spells and rising atmospheric aridity associated with climate change. Evidence from semi-arid and arid regions indicates that micro-irrigation systems, including drip, subsurface drip and micro-sprinkler irrigation, significantly improve yield and biomass accumulation compared to conventional surface irrigation methods. These irrigation systems also enhance water use efficiency (WUE) and nutrient use efficiency (NUE), making them highly suitable for millet production in dryland ecosystems. The integration of fertigation further improves nutrient synchronisation with crop demand, reduces leaching losses and supports soil fertility. The review also highlights optimised irrigation scheduling based on evapotranspiration, soil moisture thresholds, deficit irrigation principles and sensor-based automation. Smart irrigation technologies incorporating soil moisture sensors and Internet of Things (IoT)-based control systems show strong potential to enhance precision water management and reduce water consumption by up to 30–35 %. Their adoption, supported by technological innovation, policy frameworks and location-specific research, will promote sustainable water management and resource-use efficiency in millet-based dryland ecosystems. Future research should focus on optimising irrigation scheduling, smart technologies and cost-effective solutions for water-limited environments.","旱地生态系统约占地球陆地表面的41%，支撑着近20亿人口。本文综述了微灌与补充灌溉策略在提升旱地生态系统中小米生产力与恢复力方面的最新进展。旱地降水少且不稳定、蒸散量大、土壤肥力低、干旱胁迫频繁，使农业生产易受气候变率和缺水的影响。小米是种植于旱地农业生态系统中的耐旱、气候适应型谷物。尽管具有恢复力，小米生产力仍受降雨变率增大、季节性持续干旱期延长以及气候变化相关的大气干燥度上升的制约。来自半干旱和干旱地区的证据表明，与传统地面灌溉方式相比，微灌系统（包括滴灌、地下滴灌和微喷灌）显著提高了产量和生物量积累。这些灌溉系统还提高了水分利用效率（WUE）和养分利用效率（NUE），使其非常适合旱地生态系统中的小米生产。水肥一体化（fertigation）的整合进一步改善了养分与作物需求的同步性，减少了淋失损失，并有助于维持土壤肥力。本文还重点介绍了基于蒸散量、土壤水分阈值、亏缺灌溉原则和基于传感器的自动化的优化灌溉制度。结合土壤水分传感器和基于物联网（IoT）控制系统的智能灌溉技术，在提升精准水分管理和减少高达30%–35%的用水量方面展现出巨大潜力。在技术创新、政策框架和因地制宜研究的支持下，这些技术的采用将促进以小米为基础的旱地生态系统中的可持续水分管理和资源利用效率。未来研究应聚焦于优化灌溉制度、智能技术以及面向水资源受限环境的成本效益解决方案。",null,"Plant Science Today","2026-10-14T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,13,8,1,"系统综述微灌与补充灌溉提升旱地谷子产量与水肥效率，含物联网智能灌溉节水30-35%等实质结论，对旱作智慧农业有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业物联网","旱作农业","节水灌溉","谷子高粱",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[39,44],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"status":51,"locked":14,"version":52,"method":53,"evidence":54},"classified","2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":58},[56,65,72,82,90,97,104,110],{"code":33,"score":57,"supported":58,"title":59,"abstract":60,"tags":64},19,true,[34],[61,62,34,63],"灌溉","水资源","evapotranspiration",[29,30],{"code":45,"score":13,"supported":58,"title":66,"abstract":67,"tags":71},[],[68,69,70],"物联网","internet of things","IoT",[28],{"code":40,"score":73,"supported":58,"title":74,"abstract":75,"tags":81},9,[],[76,77,78,79,80],"气候变化","气候适应","干旱","climate change","drought",[],{"code":83,"score":84,"supported":58,"title":85,"abstract":86,"tags":89},"F3",6,[],[87,88],"水肥一体化","fertigation",[],{"code":91,"score":84,"supported":58,"title":92,"abstract":93,"tags":96},"H2",[],[94,95],"土壤","soil",[],{"code":98,"score":99,"supported":58,"title":100,"abstract":101,"tags":103},"A5",3,[],[102],"millet",[],{"code":105,"score":99,"supported":58,"title":106,"abstract":107,"tags":109},"H6",[],[108],"农业生态",[],{"code":111,"score":22,"supported":14,"title":112,"abstract":113,"tags":114},"I1",[],[],[27],[116,117],"微灌 补充灌溉 旱地 谷子","智慧灌溉 水分利用效率 谷子","微灌补充灌溉旱地谷子-4989",0,"10.14719\u002Fpst.16312",{"doi":120,"openalex_id":122,"authors":123,"venue":10,"cited_by_count":119,"oa_url":139,"card":140,"direction":144,"ingested_from":146},"W7221796668",[124,127,130,133,136],{"name":125,"orcid":126},"M Megha","https:\u002F\u002Forcid.org\u002F0009-0006-7303-6946",{"name":128,"orcid":129},"N Vadivel","https:\u002F\u002Forcid.org\u002F0000-0002-5301-5324",{"name":131,"orcid":132},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":134,"orcid":135},"P. Ayyadurai","https:\u002F\u002Forcid.org\u002F0000-0003-1889-6047",{"name":137,"orcid":138},"PD Rathinasamy","https:\u002F\u002Forcid.org\u002F0000-0001-8793-3513","https:\u002F\u002Fhorizonepublishing.com\u002Fjournals\u002Findex.php\u002FPST\u002Farticle\u002Fdownload\u002F16312\u002F16748",{"tldr":141,"method":142,"finding":143,"direction":144,"opportunity":145},"综述旱地微灌与补充灌溉提升小米产量与水分利用效率的研究进展。","综述滴灌、地下滴灌、微喷及水肥一体化、传感器与IoT智能灌溉技术。","微灌显著提高小米产量、WUE和NUE，智能灌溉可节水30–35%。","智慧农业 \u002F 农业物联网","可研究低成本IoT灌溉决策模型在小米旱地的本地化适配与长期效应。","openalex","2026-10-15T23:30:08.971713Z",{"total":84,"page":22,"page_size":84,"items":149},[150,232,297,383,452,521],{"id":151,"title":152,"url":153,"summary":154,"summary_zh":155,"content":9,"source_name":156,"source_url":153,"published_at":157,"category":12,"cover_url":9,"hotness":158,"is_selected":14,"score":159,"score_detail":160,"sources":164,"tags":168,"primary_topic":172,"secondary_topics":173,"classification":175,"search_phrases":209,"slug":212,"view_count":119,"doi":213,"paper":214,"created_at":231},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":161,"substance":162,"depth":19,"authority":20,"freshness":119,"relevant":22,"comment":163},22,21,"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。",[165,166],{"name":156,"url":153},{"name":156,"url":167},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[27,169,28,170,171],"农业人工智能","区块链溯源","精准灌溉",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[174],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":176},{"candidates":177,"agricultural_evidence":58},[178,183,187,194,200,205],{"code":33,"score":179,"supported":58,"title":180,"abstract":181,"tags":182},15,[34],[61,34],[171],{"code":45,"score":13,"supported":58,"title":184,"abstract":185,"tags":186},[],[68,69,70],[28],{"code":188,"score":189,"supported":58,"title":190,"abstract":191,"tags":193},"I2",4,[],[192],"artificial intelligence",[169],{"code":195,"score":99,"supported":58,"title":196,"abstract":197,"tags":199},"G2",[],[198],"小农户",[],{"code":201,"score":22,"supported":14,"title":202,"abstract":203,"tags":204},"E4",[],[],[170],{"code":111,"score":22,"supported":14,"title":206,"abstract":207,"tags":208},[],[],[27],[210,211],"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":213,"openalex_id":215,"authors":216,"venue":156,"cited_by_count":119,"oa_url":153,"card":225,"direction":144,"ingested_from":146},"W7223034914",[217,220,222],{"name":218,"orcid":219},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":221,"orcid":9},"Dr.T.Baskar",{"name":223,"orcid":224},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":226,"method":227,"finding":228,"direction":229,"opportunity":230},"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":233,"title":234,"url":235,"summary":236,"summary_zh":237,"content":9,"source_name":238,"source_url":235,"published_at":239,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":240,"score_detail":241,"sources":245,"tags":247,"primary_topic":250,"secondary_topics":251,"classification":253,"search_phrases":273,"slug":276,"view_count":119,"doi":277,"paper":278,"created_at":296},5378,"IoT-Enabled Smart Irrigation Using ESP32 and LoRa for Precision Water Management in Connectivity-Limited Regions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Felectronics15204831","In this paper, a new method for irrigation systems in agriculture has been reported. This work aims to design and implement a smart irrigation system based on the ESP32 platform and Internet of Things (IoT) technologies for optimizing water usage management. With the rapid growth of Internet of Things (IoT) technologies, where devices are connected and can be controlled through the internet, it is now feasible to design smart irrigation systems based on real-time environmental data to deliver optimal water use with minimal human intervention. In Sahara areas, where communication networks and internet connections are lacking, alternative communication protocols are necessary for data transfer. LoRa technology based on the SX1278 transceiver provides long-range, low-power communication that can achieve multi-kilometer ranges (up to 10–15 km in rural areas) under ideal conditions: line-of-sight, high antenna height, low data rate, and open rural environments between distributed sensor nodes and a central gateway. However, this theoretical range cannot be assumed in practice. Field tests under standard farm deployment conditions (ground-level placement and vegetation interference) showed that the link was lost at approximately 120 m, which validates the system at the scale of a field plot. This project used Arduino (version 2.3.7) software to program the NodeMCU (ESP32) board to link the board with the Blynk cloud application. The project is used to monitor the temperature, humidity, and soil moisture and do the command for the irrigation system, with master and slave cards using LoRa technology as a communication tool to replace communication systems like WiFi or cellular networks in deserted or remote areas, which might not offer sufficient coverage or could be too expensive to build. To address this challenge, this paper presents the design and implementation of a smart irrigation system based on the ESP32 microcontroller and LoRa (SX1278) technology, with the long-term objective of helping in the modernization of traditional agriculture and environmental sustainability. Extending the coverage to larger fields and Sahara areas would require elevated antennas, optimized LoRa parameters (spreading factor and bandwidth), multi-hop relay nodes, or multiple gateways; these strategies are proposed as future work.","本文报道了一种农业灌溉系统的新方法。本工作旨在设计和实现一种基于ESP32平台和物联网（Internet of Things, IoT）技术的智能灌溉系统，以优化用水管理。随着物联网技术的快速发展，设备得以互联并可通过互联网进行控制，如今基于实时环境数据设计智能灌溉系统，以在最少人工干预下实现最优用水已成为可能。在撒哈拉地区，由于缺乏通信网络和互联网连接，数据传输需要采用替代通信协议。基于SX1278收发器的LoRa技术可提供远距离、低功耗通信，在理想条件下（视距传输、天线高度较高、低数据速率以及分布式传感器节点与中心网关之间开阔的农村环境），其通信距离可达数公里（农村地区可达10–15 km）。然而，这一理论距离在实际中并不能假定成立。在标准农田部署条件下（地面放置和植被干扰）进行的现场测试表明，链路在约120 m处即发生中断，这验证了系统在田块尺度上的可行性。本项目使用Arduino（版本2.3.7）软件对NodeMCU（ESP32）开发板进行编程，将开发板与Blynk云应用相连接。该项目用于监测温度、湿度和土壤水分，并向灌溉系统发送指令，主从卡采用LoRa技术作为通信工具，以替代WiFi或蜂窝网络等通信系统——这些系统在荒漠或偏远地区可能覆盖不足，或建设成本过高。为应对这一挑战，本文提出了基于ESP32微控制器和LoRa（SX1278）技术的智能灌溉系统的设计与实现，其长期目标是助力传统农业的现代化和环境可持续性。要将覆盖范围扩展至更大田块和撒哈拉地区，需要架高天线、优化LoRa参数（扩频因子和带宽）、采用多跳中继节点或部署多个网关；这些策略被提出作为未来工作。","Electronics","2026-10-19T00:00:00Z",67,{"impact":242,"substance":17,"depth":243,"authority":20,"freshness":21,"relevant":22,"comment":244},12,16,"面向无网络覆盖地区的ESP32+LoRa智能灌溉系统，含实地链路测试数据，方法有参考价值但属细分技术进展，未达每日精选门槛。",[246],{"name":238,"url":235},[27,68,248,30,249],"精准农业","LoRa",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},[252],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":254},{"candidates":255,"agricultural_evidence":58},[256,260,264,268],{"code":45,"score":17,"supported":58,"title":257,"abstract":258,"tags":259},[70],[68,69,70],[68],{"code":33,"score":179,"supported":58,"title":261,"abstract":262,"tags":263},[34],[61,34],[30],{"code":91,"score":84,"supported":58,"title":265,"abstract":266,"tags":267},[],[94,95],[],{"code":111,"score":269,"supported":14,"title":270,"abstract":271,"tags":272},2,[],[],[27,248],[274,275],"ESP32 LoRa 智能灌溉","SX1278 精准灌溉","ESP32LoRa智能灌溉-5378","10.3390\u002Felectronics15204831",{"doi":277,"openalex_id":279,"authors":280,"venue":238,"cited_by_count":119,"oa_url":235,"card":291,"direction":144,"ingested_from":146},"W7222959710",[281,283,285,288],{"name":282,"orcid":9},"Hanfoug Salah",{"name":284,"orcid":9},"Riad Saidi",{"name":286,"orcid":287},"Abdelaziz Aouiche","https:\u002F\u002Forcid.org\u002F0000-0003-1378-7941",{"name":289,"orcid":290},"Yazeed Alkhrijah","https:\u002F\u002Forcid.org\u002F0000-0001-7352-003X",{"tldr":292,"method":293,"finding":294,"direction":144,"opportunity":295},"基于ESP32与LoRa构建智能灌溉系统，在无网络覆盖区实现精准用水管理。","ESP32采集温湿度和土壤水分，经LoRa(SX1278)传输至网关并接入Bly","实际农田条件下LoRa链路仅约120米即中断，远低于理论公里级。","可研究多跳中继、天线高度与扩频参数优化，提升偏远农田LoRa组网覆盖与可靠性。","2026-10-20T23:30:08.464791Z",{"id":298,"title":299,"url":300,"summary":301,"summary_zh":302,"content":9,"source_name":303,"source_url":300,"published_at":304,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":305,"score_detail":306,"sources":309,"tags":311,"primary_topic":315,"secondary_topics":316,"classification":318,"search_phrases":354,"slug":357,"view_count":119,"doi":358,"paper":359,"created_at":382},5077,"A Trust-Boundary Reference Architecture for Secure Cyber-Physical Agriculture: Design and Software-Based Proof-of-Concept for Smart Irrigation","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206545","Smart irrigation is an operational technology (OT) problem as much as an Internet of Things (IoT) one: software decisions act directly on valves, pumps, pressure and water delivery, so a spoofed reading or a replayed command has physical consequences. This paper presents a technology-neutral reference architecture that makes those consequences controllable. It is organised into seven functional layers and three cross-cutting planes, six trust-boundary classes that fix which properties must be re-established at each crossing, and an auditable crown-asset inventory with an explicit trust-level rubric. Two governing execution principles enforce this structure: local safety authority, which keeps final actuation permission at the field gateway, remote terminal unit (RTU) or programmable logic controller (PLC), and feedback-aware execution, which treats a command as complete only when independent physical feedback confirms it. Building on process-aware and safety-supervision ideas from industrial control, the architecture makes physical confirmation a mandatory completion condition at the local enforcement point, which catches a fully authorised command that is nonetheless physically unsafe, a failure mode that authorisation checks alone cannot detect. These properties are examined through a deterministic, openly available software proof-of-concept that verifies the design against its own acceptance criteria; it is not a field validation. The software executes six attack and failure scenarios plus one nominal-operation control against an intentionally unsecured negative control and a secure controller, under identical, controlled conditions: the negative control is compromised in all 6 attack scenarios and the secure controller in 0\u002F6, with 0\u002F1 false positives on legitimate operation. Analytically, the design costs 21 bytes and 50–60% additional Long Range Wide Area Network (LoRaWAN) time-on-air per record, and 78% of single-step perturbations of the trust-scoring inputs leave asset classification unchanged: the actuator class (A2) is completely stable, and the gateway\u002FRTU\u002FPLC class (A3) is stable under 6 of 7 admissible perturbations (86%), changing only when its credential\u002Fupdate authority reaches the trust-anchor threshold. A sensor-level mapping from sampling rate, measurement drift and physical fault modes to the edge-verification controls they dictate, provisions for secure time, counter recovery and the choice between fail-closed and fail-open safe states, an illustrative hypothetical worked deployment example, and a transfer of the model to greenhouse, fertigation, livestock-watering and cold-chain settings show how the architecture could be applied in practice.","智能灌溉既是一个物联网(IoT)问题，也是一个运营技术(OT)问题：软件决策直接作用于阀门、水泵、压力和水流输送，因此被欺骗的读数或被重放的命令会产生物理后果。本文提出了一种技术中立的参考架构，使这些后果可控。该架构由七个功能层和三个横切面、六类信任边界(规定每次跨越边界时必须重新建立哪些属性)，以及一份可审计的核心资产清单(配有明确的信任等级评定标准)组成。两条主导性执行原则保障了这一结构：本地安全授权，即将最终执行许可保留在田间网关、远程终端单元(RTU)或可编程逻辑控制器(PLC)处；以及反馈感知执行，即只有当独立的物理反馈予以确认时，才将命令视为完成。该架构以工业控制中的过程感知和安全监督思想为基础，将物理确认设为本地执行点的强制性完成条件，从而能够捕获完全获得授权但在物理上仍不安全的命令——这是仅靠授权检查无法检测到的失效模式。这些特性通过一个确定性、公开可用的软件概念验证进行了检验，该验证依据其自身的验收标准对设计进行评估；这不是现场验证。该软件在相同且受控的条件下，针对一个故意不设防护的阴性对照和一个安全控制器，执行了六种攻击与故障场景以及一项标称运行对照：阴性对照在所有6种攻击场景中均被攻破，而安全控制器为0\u002F6，对合法运行的误报为0\u002F1。分析表明，该设计每条记录额外增加21字节和50–60%的LoRaWAN(LoRaWAN)空中时间，且信任评分输入的78%单步扰动不会改变资产分类：执行器类(A2)完全稳定，网关\u002FRTU\u002FPLC类(A3)在7种可允许扰动中有6种保持稳定(86%)，仅当其凭证\u002F更新权限达到信任锚阈值时才会改变。还提出了从采样率、测量漂移和物理故障模式到其所决定的边缘验证控制的传感器级映射，以及安全时间、计数器恢复和故障关闭与故障打开安全状态之间选择的保障措施。","Sensors","2026-10-15T00:00:00Z",75,{"impact":242,"substance":161,"depth":17,"authority":307,"freshness":73,"relevant":22,"comment":308},14,"面向智慧灌溉的信任边界参考架构论文，方法新颖、含软件验证与量化开销数据，但属概念验证、非田间实证，产业影响有限，可作为技术前沿精选。",[310],{"name":303,"url":300},[312,27,28,313,314],"数字乡村","智能灌溉","网络安全",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[317],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":319},{"candidates":320,"agricultural_evidence":58},[321,325,329,335,341,345,350],{"code":33,"score":179,"supported":58,"title":322,"abstract":323,"tags":324},[34],[61,34],[313],{"code":45,"score":13,"supported":58,"title":326,"abstract":327,"tags":328},[],[68,69,70],[28],{"code":330,"score":99,"supported":58,"title":331,"abstract":332,"tags":334},"B7",[],[333],"greenhouse",[],{"code":336,"score":99,"supported":58,"title":337,"abstract":338,"tags":340},"C",[],[339],"livestock",[],{"code":83,"score":99,"supported":58,"title":342,"abstract":343,"tags":344},[],[88],[],{"code":346,"score":269,"supported":14,"title":347,"abstract":348,"tags":349},"I6",[],[],[312,314],{"code":111,"score":22,"supported":14,"title":351,"abstract":352,"tags":353},[],[],[27],[355,356],"智能灌溉 信任边界 参考架构","LoRaWAN 灌溉 安全","智能灌溉信任边界参考架构-5077","10.3390\u002Fs26206545",{"doi":358,"openalex_id":360,"authors":361,"venue":303,"cited_by_count":119,"oa_url":300,"card":377,"direction":144,"ingested_from":146},"W7222165876",[362,365,368,371,374],{"name":363,"orcid":364},"Alvertos Ioannis Mourikis","https:\u002F\u002Forcid.org\u002F0009-0009-2386-2803",{"name":366,"orcid":367},"Iosif Polenakis","https:\u002F\u002Forcid.org\u002F0000-0002-6427-5519",{"name":369,"orcid":370},"Christos Sarantidis","https:\u002F\u002Forcid.org\u002F0009-0003-9486-8483",{"name":372,"orcid":373},"Romanos Kalamatianos","https:\u002F\u002Forcid.org\u002F0009-0000-6939-5424",{"name":375,"orcid":376},"Ioannis Karydis","https:\u002F\u002Forcid.org\u002F0000-0002-9470-2729",{"tldr":378,"method":379,"finding":380,"direction":144,"opportunity":381},"提出面向智慧灌溉的安全参考架构，用软件验证可抵御重放与欺骗攻击。","七层三面参考架构、信任边界分类、确定性软件概念验证与LoRaWAN开销分析。","安全控制器在6种攻击场景下0次被攻破，负控制全部被攻破，且误报率为0。","可研究边缘验证控制与采样率、漂移、故障模式的映射，以及安全时间与失效安全状态选择。","2026-10-16T23:30:08.098924Z",{"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":158,"is_selected":14,"score":391,"score_detail":392,"sources":394,"tags":398,"primary_topic":400,"secondary_topics":401,"classification":403,"search_phrases":430,"slug":433,"view_count":22,"doi":9,"paper":434,"created_at":451},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":242,"substance":307,"depth":20,"authority":84,"freshness":21,"relevant":22,"comment":393},"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。",[395,396],{"name":389,"url":386},{"name":389,"url":397},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F315",[27,169,28,399,171],"边缘计算",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},[402],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":404},{"candidates":405,"agricultural_evidence":58},[406,410,415,419,426],{"code":45,"score":57,"supported":58,"title":407,"abstract":408,"tags":409},[70],[68,69,70],[28,399],{"code":111,"score":179,"supported":58,"title":411,"abstract":413,"tags":414},[412],"precision agriculture",[248,412],[27],{"code":33,"score":13,"supported":58,"title":416,"abstract":417,"tags":418},[],[61,62,34],[171],{"code":188,"score":420,"supported":58,"title":421,"abstract":422,"tags":425},7,[],[423,424],"机器学习","machine learning",[169],{"code":91,"score":84,"supported":58,"title":427,"abstract":428,"tags":429},[],[94,95],[],[431,432],"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":435,"authors":436,"venue":389,"cited_by_count":119,"oa_url":386,"card":446,"direction":144,"ingested_from":146},"W7221411136",[437,439,441,443],{"name":438,"orcid":9},"Om Gaikwad",{"name":440,"orcid":9},"Shriraj Chavan",{"name":442,"orcid":9},"Krushnadev Gaikwad",{"name":444,"orcid":445},"Swapnil R. Takale","https:\u002F\u002Forcid.org\u002F0009-0001-3636-4450",{"tldr":447,"method":448,"finding":449,"direction":144,"opportunity":450},"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":453,"title":454,"url":455,"summary":456,"summary_zh":457,"content":9,"source_name":458,"source_url":455,"published_at":459,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":460,"score_detail":461,"sources":463,"tags":465,"primary_topic":468,"secondary_topics":469,"classification":471,"search_phrases":501,"slug":504,"view_count":269,"doi":505,"paper":506,"created_at":520},4561,"Smart Agriculture and Environmental Monitoring: An Edge-AI, IoT, and Federated Learning Framework for Climate-Resilient Farming","https:\u002F\u002Fdoi.org\u002F10.11648\u002Fj.ajese.20261004.11","This study presents a system architecture, algorithm, and evaluation protocol for smart agriculture and environmental monitoring. It is a method\u002Farchitecture contribution and does not report field trial results. World food production is still under stress from climate variability, the availability of freshwater resources, and the cost of farm inputs, offering an incentive to transition to data-driven and sensor-based farm management practices. Internet of Things (IoT)-based agricultural monitoring is well developed, but most systems deployed focus on a single parameter (e.g., soil moisture) or the parameter is inferred solely in the cloud, adding latency and fragility to the deployment of systems in rural areas. In this study, we propose an integrated architecture for low-power multiparameter environmental sensing, LoRaWAN\u002FNB-IoT connectivity, edge-based lightweight inference, and privacy-preserving federated learning with cross-farm model aggregation, which is further complemented by a digital twin layer for scenario simulation. The decision layer of the framework can combine data from ground sensors with UAV and satellite images for the multi-scale monitoring of crop health and environmental quality (air, water, and GHG proxies). We contribute the full system design, an algorithmic pipeline, a sensor and model configuration specification, and a pre-registered evaluation protocol intended to guide future field deployment; no field data have yet been collected and clearly labelled non-empirical templates showing the expected reporting format only, rather than measured outcomes. The architecture and protocol are instead grounded in, and cross-checked against, published state-of-the-art results, which report an accuracy range of 82–97% for federated crop-yield prediction and 20–30% water savings from sensor-controlled irrigation.","本研究提出了一套面向智慧农业与环境监测的系统架构、算法与评估协议。这是一项方法\u002F架构层面的贡献，不报告田间试验结果。世界粮食生产仍面临气候变率、淡水资源可得性以及农业投入成本带来的压力，这促使农业管理实践向数据驱动和基于传感器的方向转型。基于物联网（IoT）的农业监测已较为成熟，但大多数已部署系统仅关注单一参数（如土壤水分），或仅在云端推断参数，这增加了农村地区系统部署的延迟和脆弱性。在本研究中，我们提出了一种集成架构，涵盖低功耗多参数环境感知、LoRaWAN\u002FNB-IoT连接、基于边缘的轻量级推理，以及具有跨农场模型聚合的隐私保护联邦学习，并进一步辅以用于情景模拟的数字孪生层。该框架的决策层可将地面传感器数据与无人机和卫星图像相结合，用于作物健康和环境质量（空气、水和温室气体代理指标）的多尺度监测。我们贡献了完整的系统设计、算法流程、传感器与模型配置规范，以及一份预注册的评估协议，旨在指导未来的田间部署；目前尚未收集田间数据，并明确标注了非经验性模板，仅展示预期报告格式，而非实测结果。该架构和协议以已发表的最先进成果为依据并与之交叉验证，这些成果报告联邦作物产量预测的准确率范围为82–97%，传感器控制灌溉可节水20–30%。","American Journal of Environmental Science and Engineering","2026-10-09T00:00:00Z",66,{"impact":242,"substance":243,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":462},"提出边缘AI+IoT+联邦学习的抗气候风险农业监测架构与评估协议，方法设计完整但尚无田间实测数据，属有参考价值的预研性论文。",[464],{"name":458,"url":455},[27,28,399,466,467],"数字孪生","联邦学习",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},[470],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":472},{"candidates":473,"agricultural_evidence":58},[474,480,484,488,492,497],{"code":111,"score":57,"supported":58,"title":475,"abstract":477,"tags":479},[476],"smart agriculture",[27,466,476,478],"digital twin",[466,27],{"code":45,"score":57,"supported":58,"title":481,"abstract":482,"tags":483},[70],[68,69,70],[28,399],{"code":33,"score":73,"supported":58,"title":485,"abstract":486,"tags":487},[],[61,62,34],[],{"code":91,"score":84,"supported":58,"title":489,"abstract":490,"tags":491},[],[94,95],[],{"code":330,"score":99,"supported":58,"title":493,"abstract":494,"tags":496},[],[495],"温室",[],{"code":188,"score":22,"supported":14,"title":498,"abstract":499,"tags":500},[],[],[467],[502,503],"LoRaWAN NB-IoT 农业监测","联邦学习 作物产量预测","LoRaWANNB-IoT农业监测-4561","10.11648\u002Fj.ajese.20261004.11",{"doi":505,"openalex_id":507,"authors":508,"venue":458,"cited_by_count":119,"oa_url":455,"card":515,"direction":144,"ingested_from":146},"W7221105407",[509,512],{"name":510,"orcid":511},"Zarin Progga","https:\u002F\u002Forcid.org\u002F0009-0004-2311-1210",{"name":513,"orcid":514},"Mohammad Ali","https:\u002F\u002Forcid.org\u002F0009-0009-3725-8535",{"tldr":516,"method":517,"finding":518,"direction":144,"opportunity":519},"提出边缘AI、物联网与联邦学习集成的智慧农业环境监测架构与评估协议，未开展实地试验。","LoRaWAN\u002FNB-IoT多参数传感、边缘轻量推理、跨农场联邦学习、数字孪生仿","架构基于已发表结果交叉验证，联邦产量预测精度82-97%，传感器灌溉节水20-30%。","该框架仅停留在设计阶段，未来可开展真实农田部署，验证边缘联邦学习在异构农场环境下的精度与通信开销。","2026-10-10T23:30:07.789940Z",{"id":522,"title":523,"url":524,"summary":525,"summary_zh":526,"content":9,"source_name":527,"source_url":524,"published_at":528,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":305,"score_detail":529,"sources":531,"tags":533,"primary_topic":535,"secondary_topics":536,"classification":542,"search_phrases":570,"slug":573,"view_count":189,"doi":9,"paper":574,"created_at":588},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",{"impact":243,"substance":161,"depth":17,"authority":20,"freshness":84,"relevant":22,"comment":530},"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。",[532],{"name":527,"url":524},[27,169,28,171,534],"土壤墒情",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},[537,541],{"code":188,"slug":538,"name":539,"path":540,"parent_code":49},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":51,"locked":14,"version":52,"method":53,"evidence":543},{"candidates":544,"agricultural_evidence":58},[545,549,553,557,561,565],{"code":45,"score":17,"supported":58,"title":546,"abstract":547,"tags":548},[70],[68,69,70],[28],{"code":188,"score":179,"supported":58,"title":550,"abstract":551,"tags":552},[424],[423,424],[169],{"code":33,"score":13,"supported":58,"title":554,"abstract":555,"tags":556},[],[61,34,63],[171],{"code":91,"score":420,"supported":58,"title":558,"abstract":559,"tags":560},[],[94,95],[534],{"code":111,"score":420,"supported":58,"title":562,"abstract":563,"tags":564},[],[248,412],[27],{"code":195,"score":84,"supported":58,"title":566,"abstract":567,"tags":569},[],[198,568],"smallholder",[],[571,572],"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":575,"authors":576,"venue":527,"cited_by_count":119,"oa_url":524,"card":583,"direction":144,"ingested_from":146},"W7217836036",[577,579,581],{"name":578,"orcid":9},"Christian Athanas Mwakyusa",{"name":580,"orcid":9},"Allen Deogratius Mwalekwa",{"name":582,"orcid":9},"Gift Geofrey Mushi",{"tldr":584,"method":585,"finding":586,"direction":144,"opportunity":587},"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"]