[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2144":3,"related-2144":117},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":75,"slug":78,"view_count":79,"doi":80,"paper":81,"created_at":116},2144,"Smart Farming Cybersecurity: Key Risks and Security Principles","https:\u002F\u002Fdoi.org\u002F10.3390\u002Felectronics15184087","By combining digital sensing, network connectivity, data-driven analyses, cloud services, and automated controls, smart farming has been increasingly adopted in agricultural production. Although these technologies have improved the precision and efficiency of farm management, they also increase cybersecurity exposure as agricultural facilities are connected to external networks, platforms, and remote-control environments. This review seeks to clarify why cybersecurity should be considered a fundamental requirement in smart farming and details the major system components, cybersecurity risks, and network design considerations required for secure operation. This review first explains the concept and application scope of smart farming, and then examines how sensors, communication networks, gateways, control systems, data platforms, user interfaces, cloud infrastructure, and physical support systems contribute to farm management and cybersecurity exposure. The review also emphasizes that smart farming differs from ordinary information systems because digital data and control commands can directly affect physical processes, such as irrigation, ventilation, heating, nutrient supply, and livestock management. Based on these cyber-physical characteristics, the review summarizes the key architectural considerations for reducing cybersecurity risks, including network segmentation, data and command flow mapping, gateway and wireless security, remote access management, cloud access control, device inventory, logging, monitoring, resilience, and local fail-safe operation. Overall, ensuring cybersecurity in smart farming requires an integrated approach that protects not only data and accounts but also the reliability and continuity of agricultural production.","通过融合数字传感、网络连接、数据驱动分析、云服务和自动化控制，智慧农业（smart farming）在农业生产中日益得到采用。尽管这些技术提高了农场管理的精准性和效率，但随着农业设施与外部网络、平台和远程控制环境相连，它们也增加了网络安全暴露风险。本综述旨在阐明为何网络安全应被视为智慧农业的基本要求，并详细阐述安全运行所需的主要系统组件、网络安全风险和网络设计考量。本综述首先阐释智慧农业的概念和应用范围，继而考察传感器、通信网络、网关、控制系统、数据平台、用户界面、云基础设施和物理支撑系统如何共同作用于农场管理和网络安全暴露。本综述还强调，智慧农业不同于普通信息系统，因为数字数据和控制指令能够直接影响物理过程，如灌溉、通风、加热、养分供给和畜牧管理。基于这些信息物理特征，本综述总结了降低网络安全风险的关键架构考量，包括网络分段、数据和指令流映射、网关与无线安全、远程访问管理、云访问控制、设备清单、日志记录、监控、韧性和本地故障安全运行。总体而言，确保智慧农业的网络安全需要一种综合性方法，不仅保护数据和账户，还要保护农业生产的可靠性和连续性。",null,"Electronics","2026-09-10T00:00:00Z","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,"系统梳理智慧农业网络安全的架构性风险与防护原则，对农业信息化建设有实质参考价值，但属综述性论文，产业影响偏间接。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业物联网","精准农业","农业网络安全","数据安全",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":48},[45,54,59,65,70],{"code":46,"score":47,"supported":48,"title":49,"abstract":50,"tags":53},"C",6,true,[],[51,52],"畜牧","livestock",[],{"code":33,"score":47,"supported":48,"title":55,"abstract":56,"tags":58},[],[57,34],"灌溉",[],{"code":60,"score":61,"supported":48,"title":62,"abstract":63,"tags":64},"I1",5,[],[27],[27,29],{"code":66,"score":22,"supported":14,"title":67,"abstract":68,"tags":69},"I5",[],[],[28],{"code":71,"score":22,"supported":14,"title":72,"abstract":73,"tags":74},"I6",[],[],[31],[76,77],"农业网络安全 农业物联网 数据安全 智慧农业","农业网络安全 农业物联网","农业网络安全农业物联网数据安全智慧农业-2144",2,"10.3390\u002Felectronics15184087",{"doi":80,"openalex_id":82,"authors":83,"venue":10,"cited_by_count":108,"oa_url":6,"card":109,"direction":113,"ingested_from":115},"W7212146111",[84,86,88,91,94,97,99,101,103,105],{"name":85,"orcid":9},"Sunmi Kong",{"name":87,"orcid":9},"Chang Ha Park",{"name":89,"orcid":90},"Kyung Jun Lee","https:\u002F\u002Forcid.org\u002F0000-0002-8972-0391",{"name":92,"orcid":93},"Tae‐Su Kim","https:\u002F\u002Forcid.org\u002F0000-0002-6402-8139",{"name":95,"orcid":96},"Yeong‐Seon Won","https:\u002F\u002Forcid.org\u002F0009-0000-0950-7834",{"name":98,"orcid":9},"Min-Ho Jo",{"name":100,"orcid":9},"SongYi Han",{"name":102,"orcid":9},"Ju Eun Ko",{"name":104,"orcid":9},"Hyeon Ju Nam",{"name":106,"orcid":107},"Hyeon Ji Yeo","https:\u002F\u002Forcid.org\u002F0000-0002-5474-8204",0,{"tldr":110,"method":111,"finding":112,"direction":113,"opportunity":114},"综述智慧农业网络安全风险，提出保障生产可靠性的架构原则。","文献综述，分析传感器、网关、云平台等系统组件与网络设计。","智慧农业网络安全需保护物理生产过程，而非仅数据与账户。","智慧农业 \u002F 农业物联网","可研究面向灌溉、通风等物理过程的轻量级入侵检测与本地失效安全机制。","openalex","2026-09-11T23:30:10.445737Z",{"total":47,"page":22,"page_size":47,"items":118},[119,235,314,376,460,557],{"id":120,"title":121,"url":122,"summary":123,"summary_zh":124,"content":9,"source_name":125,"source_url":122,"published_at":126,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":127,"score_detail":128,"sources":132,"tags":134,"primary_topic":138,"secondary_topics":144,"classification":150,"search_phrases":210,"slug":213,"view_count":108,"doi":214,"paper":215,"created_at":234},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)","2026-10-21T00:00:00Z",80,{"impact":17,"substance":129,"depth":17,"authority":20,"freshness":130,"relevant":22,"comment":131},22,9,"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[133],{"name":125,"url":122},[27,135,136,29,137],"农业人工智能","物联网","气候韧性",{"code":139,"slug":140,"name":141,"path":142,"parent_code":143},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[145,149],{"code":66,"slug":146,"name":147,"path":148,"parent_code":143},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":151},{"candidates":152,"agricultural_evidence":48},[153,164,170,177,182,190,197,204],{"code":139,"score":17,"supported":48,"title":154,"abstract":156,"tags":163},[155],"artificial intelligence",[157,158,159,160,161,162],"深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[135],{"code":60,"score":165,"supported":48,"title":166,"abstract":168,"tags":169},16,[167],"precision agriculture",[29,167],[27,29],{"code":66,"score":13,"supported":48,"title":171,"abstract":172,"tags":176},[],[136,173,174,175],"边缘计算","IoT","edge computing",[136],{"code":33,"score":130,"supported":48,"title":178,"abstract":179,"tags":181},[],[57,180,34],"水资源",[],{"code":183,"score":184,"supported":48,"title":185,"abstract":186,"tags":189},"H7",7,[],[187,188],"气候变化","climate change",[137],{"code":191,"score":47,"supported":48,"title":192,"abstract":193,"tags":196},"H2",[],[194,195],"土壤","soil",[],{"code":198,"score":199,"supported":48,"title":200,"abstract":201,"tags":203},"A7",3,[],[202],"间作",[],{"code":205,"score":199,"supported":48,"title":206,"abstract":207,"tags":209},"F3",[],[208],"养分管理",[],[211,212],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":214,"openalex_id":216,"authors":217,"venue":125,"cited_by_count":108,"oa_url":122,"card":228,"direction":113,"ingested_from":115},"W7223121456",[218,221,223,225],{"name":219,"orcid":220},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":222,"orcid":9},"Oluwadare OYEBODE",{"name":224,"orcid":9},"Iretiayo Adelaiye",{"name":226,"orcid":227},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":229,"method":230,"finding":231,"direction":232,"opportunity":233},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","农业人工智能与决策模型","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z",{"id":236,"title":237,"url":238,"summary":239,"summary_zh":240,"content":9,"source_name":241,"source_url":238,"published_at":242,"category":12,"cover_url":9,"hotness":243,"is_selected":14,"score":244,"score_detail":245,"sources":248,"tags":252,"primary_topic":255,"secondary_topics":256,"classification":258,"search_phrases":291,"slug":294,"view_count":108,"doi":295,"paper":296,"created_at":313},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":129,"substance":246,"depth":19,"authority":20,"freshness":108,"relevant":22,"comment":247},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。",[249,250],{"name":241,"url":238},{"name":241,"url":251},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[27,135,28,253,254],"区块链溯源","精准灌溉",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[257],{"code":66,"slug":146,"name":147,"path":148,"parent_code":143},{"status":40,"locked":14,"version":41,"method":42,"evidence":259},{"candidates":260,"agricultural_evidence":48},[261,266,271,276,282,287],{"code":33,"score":262,"supported":48,"title":263,"abstract":264,"tags":265},15,[34],[57,34],[254],{"code":66,"score":13,"supported":48,"title":267,"abstract":268,"tags":270},[],[136,269,174],"internet of things",[28],{"code":139,"score":272,"supported":48,"title":273,"abstract":274,"tags":275},4,[],[155],[135],{"code":277,"score":199,"supported":48,"title":278,"abstract":279,"tags":281},"G2",[],[280],"小农户",[],{"code":283,"score":22,"supported":14,"title":284,"abstract":285,"tags":286},"E4",[],[],[253],{"code":60,"score":22,"supported":14,"title":288,"abstract":289,"tags":290},[],[],[27],[292,293],"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":295,"openalex_id":297,"authors":298,"venue":241,"cited_by_count":108,"oa_url":238,"card":307,"direction":113,"ingested_from":115},"W7223034914",[299,302,304],{"name":300,"orcid":301},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":303,"orcid":9},"Dr.T.Baskar",{"name":305,"orcid":306},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":308,"method":309,"finding":310,"direction":311,"opportunity":312},"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":315,"title":316,"url":317,"summary":318,"summary_zh":319,"content":9,"source_name":10,"source_url":317,"published_at":320,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":321,"score_detail":322,"sources":325,"tags":327,"primary_topic":330,"secondary_topics":331,"classification":333,"search_phrases":352,"slug":355,"view_count":108,"doi":356,"paper":357,"created_at":375},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。","2026-10-19T00:00:00Z",67,{"impact":323,"substance":17,"depth":165,"authority":20,"freshness":21,"relevant":22,"comment":324},12,"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。",[326],{"name":10,"url":317},[27,136,29,328,329],"节水灌溉","LoRa",{"code":66,"slug":146,"name":147,"path":148,"parent_code":143},[332],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":334},{"candidates":335,"agricultural_evidence":48},[336,340,344,348],{"code":66,"score":17,"supported":48,"title":337,"abstract":338,"tags":339},[174],[136,269,174],[136],{"code":33,"score":262,"supported":48,"title":341,"abstract":342,"tags":343},[34],[57,34],[328],{"code":191,"score":47,"supported":48,"title":345,"abstract":346,"tags":347},[],[194,195],[],{"code":60,"score":79,"supported":14,"title":349,"abstract":350,"tags":351},[],[],[27,29],[353,354],"ESP32 LoRa 智能灌溉","SX1278 精准灌溉","ESP32LoRa智能灌溉-5378","10.3390\u002Felectronics15204831",{"doi":356,"openalex_id":358,"authors":359,"venue":10,"cited_by_count":108,"oa_url":317,"card":370,"direction":113,"ingested_from":115},"W7222959710",[360,362,364,367],{"name":361,"orcid":9},"Hanfoug Salah",{"name":363,"orcid":9},"Riad Saidi",{"name":365,"orcid":366},"Abdelaziz Aouiche","https:\u002F\u002Forcid.org\u002F0000-0003-1378-7941",{"name":368,"orcid":369},"Yazeed Alkhrijah","https:\u002F\u002Forcid.org\u002F0000-0001-7352-003X",{"tldr":371,"method":372,"finding":373,"direction":113,"opportunity":374},"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":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":388,"tags":390,"primary_topic":394,"secondary_topics":395,"classification":397,"search_phrases":431,"slug":434,"view_count":108,"doi":435,"paper":436,"created_at":459},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":323,"substance":129,"depth":17,"authority":386,"freshness":130,"relevant":22,"comment":387},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。",[389],{"name":382,"url":379},[391,27,28,392,393],"数字乡村","智能灌溉","网络安全",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[396],{"code":66,"slug":146,"name":147,"path":148,"parent_code":143},{"status":40,"locked":14,"version":41,"method":42,"evidence":398},{"candidates":399,"agricultural_evidence":48},[400,404,408,414,418,423,427],{"code":33,"score":262,"supported":48,"title":401,"abstract":402,"tags":403},[34],[57,34],[392],{"code":66,"score":13,"supported":48,"title":405,"abstract":406,"tags":407},[],[136,269,174],[28],{"code":409,"score":199,"supported":48,"title":410,"abstract":411,"tags":413},"B7",[],[412],"greenhouse",[],{"code":46,"score":199,"supported":48,"title":415,"abstract":416,"tags":417},[],[52],[],{"code":205,"score":199,"supported":48,"title":419,"abstract":420,"tags":422},[],[421],"fertigation",[],{"code":71,"score":79,"supported":14,"title":424,"abstract":425,"tags":426},[],[],[391,393],{"code":60,"score":22,"supported":14,"title":428,"abstract":429,"tags":430},[],[],[27],[432,433],"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":435,"openalex_id":437,"authors":438,"venue":382,"cited_by_count":108,"oa_url":379,"card":454,"direction":113,"ingested_from":115},"W7222165876",[439,442,445,448,451],{"name":440,"orcid":441},"Alvertos Ioannis Mourikis","https:\u002F\u002Forcid.org\u002F0009-0009-2386-2803",{"name":443,"orcid":444},"Iosif Polenakis","https:\u002F\u002Forcid.org\u002F0000-0002-6427-5519",{"name":446,"orcid":447},"Christos Sarantidis","https:\u002F\u002Forcid.org\u002F0009-0003-9486-8483",{"name":449,"orcid":450},"Romanos Kalamatianos","https:\u002F\u002Forcid.org\u002F0009-0000-6939-5424",{"name":452,"orcid":453},"Ioannis Karydis","https:\u002F\u002Forcid.org\u002F0000-0002-9470-2729",{"tldr":455,"method":456,"finding":457,"direction":113,"opportunity":458},"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":461,"title":462,"url":463,"summary":464,"summary_zh":465,"content":9,"source_name":466,"source_url":463,"published_at":467,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":468,"sources":470,"tags":472,"primary_topic":475,"secondary_topics":476,"classification":482,"search_phrases":527,"slug":530,"view_count":108,"doi":531,"paper":532,"created_at":556},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。","Plant Science Today","2026-10-14T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":469},"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。",[471],{"name":466,"url":463},[27,28,473,328,474],"旱作农业","谷子高粱",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[477,478],{"code":66,"slug":146,"name":147,"path":148,"parent_code":143},{"code":183,"slug":479,"name":480,"path":481,"parent_code":37},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":40,"locked":14,"version":41,"method":42,"evidence":483},{"candidates":484,"agricultural_evidence":48},[485,491,495,502,507,511,517,523],{"code":33,"score":486,"supported":48,"title":487,"abstract":488,"tags":490},19,[34],[57,180,34,489],"evapotranspiration",[473,328],{"code":66,"score":13,"supported":48,"title":492,"abstract":493,"tags":494},[],[136,269,174],[28],{"code":183,"score":130,"supported":48,"title":496,"abstract":497,"tags":501},[],[187,498,499,188,500],"气候适应","干旱","drought",[],{"code":205,"score":47,"supported":48,"title":503,"abstract":504,"tags":506},[],[505,421],"水肥一体化",[],{"code":191,"score":47,"supported":48,"title":508,"abstract":509,"tags":510},[],[194,195],[],{"code":512,"score":199,"supported":48,"title":513,"abstract":514,"tags":516},"A5",[],[515],"millet",[],{"code":518,"score":199,"supported":48,"title":519,"abstract":520,"tags":522},"H6",[],[521],"农业生态",[],{"code":60,"score":22,"supported":14,"title":524,"abstract":525,"tags":526},[],[],[27],[528,529],"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","10.14719\u002Fpst.16312",{"doi":531,"openalex_id":533,"authors":534,"venue":466,"cited_by_count":108,"oa_url":550,"card":551,"direction":113,"ingested_from":115},"W7221796668",[535,538,541,544,547],{"name":536,"orcid":537},"M Megha","https:\u002F\u002Forcid.org\u002F0009-0006-7303-6946",{"name":539,"orcid":540},"N Vadivel","https:\u002F\u002Forcid.org\u002F0000-0002-5301-5324",{"name":542,"orcid":543},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":545,"orcid":546},"P. Ayyadurai","https:\u002F\u002Forcid.org\u002F0000-0003-1889-6047",{"name":548,"orcid":549},"PD Rathinasamy","https:\u002F\u002Forcid.org\u002F0000-0001-8793-3513","https:\u002F\u002Fhorizonepublishing.com\u002Fjournals\u002Findex.php\u002FPST\u002Farticle\u002Fdownload\u002F16312\u002F16748",{"tldr":552,"method":553,"finding":554,"direction":113,"opportunity":555},"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%。","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。","2026-10-15T23:30:08.971713Z",{"id":558,"title":559,"url":560,"summary":561,"summary_zh":562,"content":9,"source_name":563,"source_url":560,"published_at":467,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":127,"score_detail":564,"sources":566,"tags":568,"primary_topic":571,"secondary_topics":572,"classification":573,"search_phrases":629,"slug":632,"view_count":108,"doi":633,"paper":634,"created_at":654},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。","Plants",{"impact":17,"substance":129,"depth":17,"authority":386,"freshness":21,"relevant":22,"comment":565},"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。",[567],{"name":563,"url":560},[27,135,569,28,570],"农业遥感","枣椰树",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":574},{"candidates":575,"agricultural_evidence":48},[576,584,589,593,597,604,608,615,619,623],{"code":577,"score":19,"supported":48,"title":578,"abstract":580,"tags":583},"I7",[579],"bibliometric",[581,582,579],"文献计量","研究方法",[],{"code":60,"score":262,"supported":48,"title":585,"abstract":587,"tags":588},[586],"smart agriculture",[27,586],[27],{"code":33,"score":130,"supported":48,"title":590,"abstract":591,"tags":592},[],[57,180,34],[],{"code":139,"score":184,"supported":48,"title":594,"abstract":595,"tags":596},[],[158,160],[135],{"code":598,"score":184,"supported":48,"title":599,"abstract":600,"tags":603},"I3",[],[601,602],"遥感","remote sensing",[569],{"code":66,"score":184,"supported":48,"title":605,"abstract":606,"tags":607},[],[136,174],[28],{"code":609,"score":47,"supported":48,"title":610,"abstract":611,"tags":614},"A8",[],[612,613],"粮食安全","food security",[],{"code":183,"score":47,"supported":48,"title":616,"abstract":617,"tags":618},[],[187,188],[],{"code":518,"score":199,"supported":48,"title":620,"abstract":621,"tags":622},[],[521],[],{"code":624,"score":199,"supported":48,"title":625,"abstract":626,"tags":628},"I4",[],[627],"GIS",[],[630,631],"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","10.3390\u002Fplants15203137",{"doi":633,"openalex_id":635,"authors":636,"venue":563,"cited_by_count":108,"oa_url":560,"card":649,"direction":113,"ingested_from":115},"W7221786893",[637,640,643,646],{"name":638,"orcid":639},"Sajid Safeer","https:\u002F\u002Forcid.org\u002F0000-0002-0120-5900",{"name":641,"orcid":642},"Adnan Nadeem","https:\u002F\u002Forcid.org\u002F0000-0001-9622-150X",{"name":644,"orcid":645},"Sajid Maqsood","https:\u002F\u002Forcid.org\u002F0000-0003-2099-8392",{"name":647,"orcid":648},"Cataldo Pulvento","https:\u002F\u002Forcid.org\u002F0000-0002-2650-1204",{"tldr":650,"method":651,"finding":652,"direction":113,"opportunity":653},"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。","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。","2026-10-15T23:30:08.558630Z"]