[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3889":3,"related-3889":105},{"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":84,"slug":87,"view_count":62,"doi":88,"paper":89,"created_at":104},3889,"FROM FARM DATA TO MARKET VALUE: A SYSTEMATİC REVİEW OF PRECİSİON AGRİCULTURE TECHNOLOGİES AND DATA-DRİVEN MARKETİNG STRATEGİES","https:\u002F\u002Fdoi.org\u002F10.62425\u002Fdssh.1978979","This study examines, at a conceptual level, the potential contributions of the multi-layered data ecosystem generated by precision agriculture technologies to agricultural market strategies, drawing on existing literature. The study adopts a systematic review approach conducted under the guidance of a PRISMA flow diagram, with a staged elimination process narrowing 340 titles to 29 included sources, thereby establishing a transparent methodological foundation. The study's primary theoretical contribution is the Farm Data-to-Market Value Transformation Model (FDMV), which—for the first time in the precision agriculture literature—systematically bridges the technical focus of precision agriculture research with the consumer behaviour focus of marketing scholarship. The model integrates Akerlof's (1970) information asymmetry theory, Verlegh and Steenkamp's (1999) country-of-origin effect findings, Nuttavuthisit and Thøgersen's (2017) trust economy framework, and Kotler and Armstrong's (2016) value-based marketing perspective within a single four-stage conceptual structure, presenting this theoretical synthesis as an original contribution absent from existing precision agriculture literature. Findings indicate that IoT sensors, satellite imaging, unmanned aerial vehicles (UAVs), blockchain-based traceability systems, and decision support tools may exert significant influence on quality-based pricing, market segmentation, supply chain coordination, and consumer trust mechanisms. Assessment with specific reference to Türkiye highlights that structural barriers—including land fragmentation, digital literacy gaps, and the absence of data standardisation—are difficult to overcome without integrated policy intervention, while the country's agro-ecological diversity and geographical indication portfolio constitute a favourable foundation for data-driven market differentiation.","本研究在概念层面探讨了精准农业技术所生成的多层数据生态系统对农业市场策略的潜在贡献，并基于现有文献展开分析。研究采用系统综述方法，在PRISMA流程图指导下进行，通过分阶段筛选将340篇文献缩减至29篇纳入来源，从而建立了透明的方法论基础。本研究的主要理论贡献是提出了农场数据到市场价值转化模型（Farm Data-to-Market Value Transformation Model, FDMV），该模型首次在精准农业文献中系统性地将精准农业研究的技术焦点与营销学术界的消费者行为焦点相衔接。该模型将Akerlof（1970）的信息不对称理论、Verlegh和Steenkamp（1999）的原产国效应研究发现、Nuttavuthisit和Thøgersen（2017）的信任经济框架以及Kotler和Armstrong（2016）的价值导向营销视角整合于一个四阶段概念结构之中，并将这一理论综合呈现为现有精准农业文献中所缺失的原创性贡献。研究结果表明，物联网传感器、卫星成像、无人机（UAV）、基于区块链的可追溯系统以及决策支持工具可能对基于质量的定价、市场细分、供应链协调和消费者信任机制产生显著影响。以土耳其为具体参照的评估表明，土地细碎化、数字素养差距和数据标准化缺失等结构性障碍若无综合性政策干预则难以克服，而该国农业生态多样性和地理标志组合则构成了数据驱动市场差异化的有利基础。",null,"Dynamics in Social Sciences and Humanities","2026-09-30T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,13,9,1,"系统综述提出FDMV模型，将精准农业数据与市场价值衔接，理论增量明确，但属概念性研究、缺乏实证数据，产业落地影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","精准农业","区块链溯源","农业数据","农产品营销",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":48},[45,53,60,67,73,79],{"code":46,"score":47,"supported":48,"title":49,"abstract":50,"tags":52},"I1",8,true,[],[28,51],"precision agriculture",[27,28],{"code":33,"score":54,"supported":48,"title":55,"abstract":56,"tags":59},6,[],[57,58],"物联网","IoT",[],{"code":61,"score":62,"supported":48,"title":63,"abstract":64,"tags":66},"E5",3,[],[65],"地理标志",[],{"code":68,"score":62,"supported":48,"title":69,"abstract":70,"tags":72},"H6",[],[71],"农业生态",[],{"code":74,"score":62,"supported":48,"title":75,"abstract":76,"tags":78},"I2",[],[77],"decision support",[],{"code":80,"score":22,"supported":14,"title":81,"abstract":82,"tags":83},"I6",[],[],[30],[85,86],"精准农业 数据驱动 营销","FDMV 模型 农业数据","精准农业数据驱动营销-3889","10.62425\u002Fdssh.1978979",{"doi":88,"openalex_id":90,"authors":91,"venue":10,"cited_by_count":95,"oa_url":6,"card":96,"direction":102,"ingested_from":103},"W7214924809",[92],{"name":93,"orcid":94},"Bülent Şen","https:\u002F\u002Forcid.org\u002F0000-0001-8561-8173",0,{"tldr":97,"method":98,"finding":99,"direction":100,"opportunity":101},"系统综述精准农业数据生态如何转化为市场价值，提出FDMV模型。","PRISMA系统综述，340篇筛至29篇，整合多理论构建四阶段模型。","物联网、遥感、区块链等可影响定价、细分、供应链协调与消费者信任。","数字乡村与农业信息化","可实证检验FDMV模型，并研究土地碎片化与数据标准化对数据驱动营销的制约。","智慧农业 \u002F 农业物联网","openalex","2026-10-01T23:30:14.585584Z",{"total":54,"page":22,"page_size":54,"items":106},[107,220,298,361,430,497],{"id":108,"title":109,"url":110,"summary":111,"summary_zh":112,"content":9,"source_name":113,"source_url":110,"published_at":114,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":115,"score_detail":116,"sources":120,"tags":122,"primary_topic":125,"secondary_topics":129,"classification":137,"search_phrases":195,"slug":198,"view_count":95,"doi":199,"paper":200,"created_at":219},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":117,"substance":118,"depth":117,"authority":20,"freshness":21,"relevant":22,"comment":119},18,22,"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。",[121],{"name":113,"url":110},[27,123,57,28,124],"农业人工智能","气候韧性",{"code":74,"slug":126,"name":127,"path":128,"parent_code":37},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[130,131],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":132,"slug":133,"name":134,"path":135,"parent_code":136},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":138},{"candidates":139,"agricultural_evidence":48},[140,151,156,162,168,176,183,189],{"code":74,"score":117,"supported":48,"title":141,"abstract":143,"tags":150},[142],"artificial intelligence",[144,145,146,147,148,149],"深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[123],{"code":46,"score":152,"supported":48,"title":153,"abstract":154,"tags":155},16,[51],[28,51],[27,28],{"code":33,"score":13,"supported":48,"title":157,"abstract":158,"tags":161},[],[57,159,58,160],"边缘计算","edge computing",[57],{"code":132,"score":21,"supported":48,"title":163,"abstract":164,"tags":167},[],[165,166,133],"灌溉","水资源",[],{"code":169,"score":170,"supported":48,"title":171,"abstract":172,"tags":175},"H7",7,[],[173,174],"气候变化","climate change",[124],{"code":177,"score":54,"supported":48,"title":178,"abstract":179,"tags":182},"H2",[],[180,181],"土壤","soil",[],{"code":184,"score":62,"supported":48,"title":185,"abstract":186,"tags":188},"A7",[],[187],"间作",[],{"code":190,"score":62,"supported":48,"title":191,"abstract":192,"tags":194},"F3",[],[193],"养分管理",[],[196,197],"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":199,"openalex_id":201,"authors":202,"venue":113,"cited_by_count":95,"oa_url":110,"card":213,"direction":102,"ingested_from":103},"W7223121456",[203,206,208,210],{"name":204,"orcid":205},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":207,"orcid":9},"Oluwadare OYEBODE",{"name":209,"orcid":9},"Iretiayo Adelaiye",{"name":211,"orcid":212},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":214,"method":215,"finding":216,"direction":217,"opportunity":218},"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":221,"title":222,"url":223,"summary":224,"summary_zh":225,"content":9,"source_name":226,"source_url":223,"published_at":227,"category":12,"cover_url":9,"hotness":228,"is_selected":14,"score":229,"score_detail":230,"sources":233,"tags":237,"primary_topic":240,"secondary_topics":241,"classification":243,"search_phrases":276,"slug":279,"view_count":95,"doi":280,"paper":281,"created_at":297},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":118,"substance":231,"depth":19,"authority":20,"freshness":95,"relevant":22,"comment":232},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。",[234,235],{"name":226,"url":223},{"name":226,"url":236},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[27,123,238,29,239],"农业物联网","精准灌溉",{"code":132,"slug":133,"name":134,"path":135,"parent_code":136},[242],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":244},{"candidates":245,"agricultural_evidence":48},[246,251,256,261,267,272],{"code":132,"score":247,"supported":48,"title":248,"abstract":249,"tags":250},15,[133],[165,133],[239],{"code":33,"score":13,"supported":48,"title":252,"abstract":253,"tags":255},[],[57,254,58],"internet of things",[238],{"code":74,"score":257,"supported":48,"title":258,"abstract":259,"tags":260},4,[],[142],[123],{"code":262,"score":62,"supported":48,"title":263,"abstract":264,"tags":266},"G2",[],[265],"小农户",[],{"code":268,"score":22,"supported":14,"title":269,"abstract":270,"tags":271},"E4",[],[],[29],{"code":46,"score":22,"supported":14,"title":273,"abstract":274,"tags":275},[],[],[27],[277,278],"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":280,"openalex_id":282,"authors":283,"venue":226,"cited_by_count":95,"oa_url":223,"card":292,"direction":102,"ingested_from":103},"W7223034914",[284,287,289],{"name":285,"orcid":286},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":288,"orcid":9},"Dr.T.Baskar",{"name":290,"orcid":291},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":293,"method":294,"finding":295,"direction":100,"opportunity":296},"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。","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":299,"title":300,"url":301,"summary":302,"summary_zh":303,"content":9,"source_name":304,"source_url":301,"published_at":305,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":306,"score_detail":307,"sources":309,"tags":311,"primary_topic":314,"secondary_topics":315,"classification":317,"search_phrases":337,"slug":340,"view_count":95,"doi":341,"paper":342,"created_at":360},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":17,"substance":117,"depth":152,"authority":20,"freshness":47,"relevant":22,"comment":308},"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。",[310],{"name":304,"url":301},[27,57,28,312,313],"节水灌溉","LoRa",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[316],{"code":132,"slug":133,"name":134,"path":135,"parent_code":136},{"status":40,"locked":14,"version":41,"method":42,"evidence":318},{"candidates":319,"agricultural_evidence":48},[320,324,328,332],{"code":33,"score":117,"supported":48,"title":321,"abstract":322,"tags":323},[58],[57,254,58],[57],{"code":132,"score":247,"supported":48,"title":325,"abstract":326,"tags":327},[133],[165,133],[312],{"code":177,"score":54,"supported":48,"title":329,"abstract":330,"tags":331},[],[180,181],[],{"code":46,"score":333,"supported":14,"title":334,"abstract":335,"tags":336},2,[],[],[27,28],[338,339],"ESP32 LoRa 智能灌溉","SX1278 精准灌溉","ESP32LoRa智能灌溉-5378","10.3390\u002Felectronics15204831",{"doi":341,"openalex_id":343,"authors":344,"venue":304,"cited_by_count":95,"oa_url":301,"card":355,"direction":102,"ingested_from":103},"W7222959710",[345,347,349,352],{"name":346,"orcid":9},"Hanfoug Salah",{"name":348,"orcid":9},"Riad Saidi",{"name":350,"orcid":351},"Abdelaziz Aouiche","https:\u002F\u002Forcid.org\u002F0000-0003-1378-7941",{"name":353,"orcid":354},"Yazeed Alkhrijah","https:\u002F\u002Forcid.org\u002F0000-0001-7352-003X",{"tldr":356,"method":357,"finding":358,"direction":102,"opportunity":359},"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":362,"title":363,"url":364,"summary":365,"summary_zh":366,"content":9,"source_name":367,"source_url":364,"published_at":305,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":368,"score_detail":369,"sources":371,"tags":373,"primary_topic":377,"secondary_topics":383,"classification":389,"search_phrases":411,"slug":414,"view_count":22,"doi":415,"paper":416,"created_at":429},5308,"Geopolitics and food security: A comprehensive review of emerging risks and responses","https:\u002F\u002Fdoi.org\u002F10.51200\u002Fjsffs.v2i2.7666","This review paper examines the relationship between geopolitics and food security through a Systematic Literature Review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework. A total of 15 peer-reviewed journal articles published between 2015 and 2026 were reviewed to identify emerging geopolitical risks and responses affecting global food systems. The study synthesizes literature on armed conflicts, trade restrictions, climate change, supply chain disruptions, energy market instability, and technological transformation in agriculture. The findings identified four major themes. First, geopolitical conflicts and protectionist trade policies significantly disrupt global food supply chains and increase food price volatility. Second, climate-related shock such as droughts, floods, and extreme weather events intensifies food insecurity, particularly in import-dependent and developing economies. Third, weaknesses in global logistics and energy dependence expose food systems to external shocks and market instability. Fourth, technological innovations, including smart farming, precision agriculture, and digital monitoring systems, play an increasingly important role in strengthening agricultural productivity, improving resource efficiency, and enhancing resilience against geopolitical and environmental uncertainties. The policy implications of this study include strengthening regional food reserves, diversifying import sources, investing in climate-resilient agriculture, promoting the adoption of smart farming technologies, and enhancing institutional cooperation between governments and international organizations. The relevance of smart farming is increasingly evident, as precision agriculture, Internet of Things (IoT)-based monitoring systems, and data-driven farming technologies play a critical role in improving productivity, reducing resource inefficiency, and enhancing food system resilience under geopolitical stress. The study contributes to a broader understanding of how geopolitical dynamics shape food security and provides recommendations for building sustainable, resilient, and technology-driven food systems in an increasingly uncertain global environment.","本文通过遵循系统评价与荟萃分析首选报告条目（Preferred Reporting Items for Systematic Reviews and Meta-Analyses）框架的系统文献综述，考察地缘政治与粮食安全之间的关系。共 review 了2015年至2026年间发表的15篇同行评审期刊论文，以识别影响全球粮食系统的新兴地缘政治风险与应对措施。本研究综合了关于武装冲突、贸易限制、气候变化、供应链中断、能源市场不稳定以及农业技术转型的文献。研究结果识别出四大主题。第一，地缘政治冲突和保护主义贸易政策显著扰乱全球粮食供应链并加剧粮食价格波动。第二，干旱、洪水和极端天气事件等气候相关冲击加剧粮食不安全，尤其是在依赖进口的经济体和发展中经济体中。第三，全球物流的薄弱环节和能源依赖使粮食系统暴露于外部冲击和市场不稳定之中。第四，包括智慧农业、精准农业和数字监测系统在内的技术创新，在增强农业生产力、提高资源效率以及增强应对地缘政治和环境不确定性的韧性方面发挥着日益重要的作用。本研究的政策启示包括加强区域粮食储备、多元化进口来源、投资气候韧性农业、促进智慧农业技术的采用，以及加强政府与国际组织之间的制度合作。智慧农业的相关性日益明显，因为精准农业、基于物联网（IoT）的监测系统和数据驱动农业技术在提高生产力、减少资源低效以及增强地缘政治压力下粮食系统韧性方面发挥着关键作用。本研究有助于更广泛地理解地缘政治动态如何塑造粮食安全，并为在日益不确定的全球环境中构建可持续、有韧性且技术驱动的粮食系统提供建议。","Journal of Smart Farming and Food Security",77,{"impact":117,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":370},"系统综述梳理地缘政治与粮食安全风险，并强调智慧农业的韧性价值，具政策与产业参考意义，但属综述性论文，非突破性成果。",[372],{"name":367,"url":364},[27,374,375,28,376],"农业供应链","粮食安全","地缘政治",{"code":378,"slug":379,"name":380,"path":381,"parent_code":382},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[384,388],{"code":169,"slug":385,"name":386,"path":387,"parent_code":136},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":390},{"candidates":391,"agricultural_evidence":48},[392,397,402,407],{"code":378,"score":247,"supported":48,"title":393,"abstract":395,"tags":396},[394],"food security",[375,394],[375],{"code":46,"score":398,"supported":48,"title":399,"abstract":400,"tags":401},11,[],[27,28,51],[27,28],{"code":169,"score":21,"supported":48,"title":403,"abstract":404,"tags":406},[],[173,405,174],"干旱",[],{"code":33,"score":21,"supported":48,"title":408,"abstract":409,"tags":410},[],[57,254,58],[],[412,413],"地缘政治 粮食安全","智慧农业 粮食系统韧性","地缘政治粮食安全-5308","10.51200\u002Fjsffs.v2i2.7666",{"doi":415,"openalex_id":417,"authors":418,"venue":367,"cited_by_count":95,"oa_url":364,"card":424,"direction":102,"ingested_from":103},"W7222902031",[419,421],{"name":420,"orcid":9},"Parameswaran Mahendran",{"name":422,"orcid":423},"Wong Sing Yun","https:\u002F\u002Forcid.org\u002F0000-0001-5776-8852",{"tldr":425,"method":426,"finding":427,"direction":102,"opportunity":428},"系统综述地缘政治风险对粮食安全的影响及应对，强调智慧农业的韧性作用。","按PRISMA框架系统综述2015-2026年15篇同行评议文献。","冲突、贸易限制、气候与能源冲击威胁粮食安全，智慧农业可提升韧性与效率。","可量化研究物联网与精准农业在地缘政治冲击下对粮食系统韧性的实证贡献。","2026-10-19T23:30:07.850994Z",{"id":431,"title":432,"url":433,"summary":434,"summary_zh":435,"content":9,"source_name":436,"source_url":433,"published_at":437,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":438,"score_detail":439,"sources":441,"tags":443,"primary_topic":446,"secondary_topics":452,"classification":454,"search_phrases":472,"slug":475,"view_count":95,"doi":476,"paper":477,"created_at":496},5237,"Integrasi Framework Laravel dan Android pada Sistem IoT untuk Monitoring Parameter Fisika-Kimia di Greenhouse","https:\u002F\u002Fdoi.org\u002F10.36257\u002Fteknika.v20i3.12040","Pengembangan teknologi pertanian presisi (precision agriculture) pada ekosistem greenhouse menggunakan sistem hidroponik menghadapi tantangan teknis dalam menjaga stabilitas parameter lingkungan mikro secara simultan. Penelitian ini bertujuan untuk mengintegrasikan framework Laravel dan platform Android ke dalam sistem Internet of Things (IoT) untuk memantau parameter fisika-kimia lingkungan, meliputi suhu udara, kelembaban, intensitas cahaya, dan kadar karbon dioksida (CO2), pada budidaya pakcoy (Brassica rapa L.) di greenhouse hidroponik. Fokus utama penelitian ini terletak pada perancangan arsitektur backend RESTful API menggunakan Laravel untuk mengelola penyimpanan basis data riwayat jangka panjang serta menyinkronkan transmisi data dari multi-sensor (DHT22, LDR, dan sensor gas CO2) yang memiliki karakteristik sampling rate berbeda. Di sisi pengguna, aplikasi Android dan antarmuka web dikembangkan sebagai frontend yang mengonsumsi data melalui serialisasi format JSON menggunakan metode polling HTTP GET real-time, serta menyediakan tiga mode kendali aktuator, yaitu manual, otomatis berbasis ambang batas sensor, dan penjadwalan. Pengujian fungsionalitas dan kinerja sistem dilakukan melalui visualisasi antarmuka pada dasbor situs web neliti-sk.darmajaya.ac.id serta evaluasi transmisi data nirkabel. Infrastruktur backend berbasis Laravel terbukti stabil dalam menangani fluktuasi frekuensi data sensor yang kompleks. Berdasarkan evaluasi log data selama 24 jam pengujian kontinu pada rentang 14 Agustus 2026 (00.00–23.00 WIB), sistem berhasil merekam seluruh 24 titik data per jam (100%) tanpa satupun jam yang terlewat. Integrasi ini menghadirkan solusi pemantauan sekaligus pengendalian pertanian presisi yang andal, terintegrasi, dan memiliki skalabilitas tinggi bagi operator greenhouse, meskipun masih memerlukan mekanisme offline logging untuk mengantisipasi gangguan konektivitas atau pemadaman listrik di masa mendatang.","在温室生态系统中利用水培系统开发精准农业（precision agriculture）技术，面临着同时维持微环境参数稳定性的技术挑战。本研究旨在将Laravel框架和Android平台集成到物联网（IoT）系统中，以监测水培温室中小白菜（Brassica rapa L.）栽培环境的物理化学参数，包括空气温度、湿度、光照强度和二氧化碳（CO2）浓度。本研究的主要重点在于设计基于Laravel的RESTful API后端架构，以管理长期历史数据库的存储，并同步具有不同采样率特征的多传感器（DHT22、LDR和CO2气体传感器）的数据传输。在用户端，开发了Android应用和Web界面作为前端，通过HTTP GET实时轮询方法以JSON格式序列化消费数据，并提供三种执行器控制模式，即手动、基于传感器阈值的自动控制和定时调度。系统功能与性能测试通过neliti-sk.darmajaya.ac.id网站仪表板的界面可视化以及无线数据传输评估进行。基于Laravel的后端基础设施在处理复杂的传感器数据频率波动方面表现出稳定性。根据2026年8月14日（00:00–23:00 WIB）连续24小时测试期间的数据日志评估，系统成功记录了每小时全部24个数据点（100%），无一小时遗漏。该集成提供了一种可靠、集成且具有高可扩展性的精准农业监测与控制解决方案，适用于温室操作人员，但未来仍需离线日志记录机制以应对连接中断或停电情况。","TEKNIKA","2026-10-17T00:00:00Z",56,{"impact":47,"substance":117,"depth":152,"authority":54,"freshness":47,"relevant":22,"comment":440},"论文将Laravel与Android集成用于水培温室多参数监测，方法具体、数据连续24小时完整，但属单点应用研究，公共影响有限，适合作为技术案例而非每日精选头条。",[442],{"name":436,"url":433},[27,57,28,444,445],"温室大棚","水培蔬菜",{"code":447,"slug":448,"name":449,"path":450,"parent_code":451},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[453],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":455},{"candidates":456,"agricultural_evidence":48},[457,461,468],{"code":33,"score":117,"supported":48,"title":458,"abstract":459,"tags":460},[58],[57,254,58],[57],{"code":447,"score":462,"supported":48,"title":463,"abstract":465,"tags":467},14,[464],"greenhouse",[466,464],"温室",[],{"code":46,"score":47,"supported":48,"title":469,"abstract":470,"tags":471},[],[28,51],[27,28],[473,474],"Laravel Android 温室物联网","小白菜 水培 温室监测","LaravelAndroid温室物联网-5237","10.36257\u002Fteknika.v20i3.12040",{"doi":476,"openalex_id":478,"authors":479,"venue":436,"cited_by_count":95,"oa_url":490,"card":491,"direction":102,"ingested_from":103},"W7222850582",[480,482,485,488],{"name":481,"orcid":9},"Ramadanzah Ramadanzah",{"name":483,"orcid":484},"Dodi Yudo Setyawan","https:\u002F\u002Forcid.org\u002F0000-0003-2205-7617",{"name":486,"orcid":487},"Nurfiana Nurfiana","https:\u002F\u002Forcid.org\u002F0000-0002-6149-0918",{"name":489,"orcid":9},"Refi Arioen","https:\u002F\u002Fjurnal.polsri.ac.id\u002Findex.php\u002Fteknika\u002Farticle\u002Fdownload\u002F12040\u002F4368",{"tldr":492,"method":493,"finding":494,"direction":102,"opportunity":495},"集成Laravel与Android的IoT系统，实时监测温室水培小白菜的温湿度、光照和CO2。","用Laravel构建RESTful API后端，Android与Web前端通过J","后端稳定处理异构采样率数据，24小时连续测试每小时数据完整记录率100%。","可研究断网或断电下的离线缓存与数据补传机制，提升温室IoT系统鲁棒性。","2026-10-18T23:30:07.964809Z",{"id":498,"title":499,"url":500,"summary":501,"summary_zh":502,"content":9,"source_name":503,"source_url":504,"published_at":505,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":438,"score_detail":506,"sources":508,"tags":510,"primary_topic":512,"secondary_topics":513,"classification":514,"search_phrases":543,"slug":546,"view_count":62,"doi":547,"paper":548,"created_at":559},5085,"AgriSense: IoT-Driven Precision Agriculture for National Food Sustainability and Development","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23335305","Precision agriculture integrates digital technologies to improve agricultural resource management, productivity, and sustainability, while the Internet of Things (IoT) enables real-time monitoring of soil and environmental conditions through connected sensors. In Nigeria, inefficient water and fertilizer use, declining soil health, climate variability, and limited access to timely agricultural information constrain sustainable food production. This study presents AgriSense, an IoT-driven precision agriculture platform designed to support data-informed farm management under Nigerian agricultural conditions. The study adopts a mixed-method research framework incorporating descriptive, experimental, and analytical approaches, with proposed data sources including IoT sensor measurements, farmer surveys and interviews, field observations, and relevant weather information. The AgriSense platform integrates soil-moisture, nitrogen-phosphorus-potassium (N-P-K), soil-pH, temperature, and humidity monitoring with farm and field management, sensor-status tracking, automated alerts, agronomic recommendations, and reporting functions. The demonstrated interface represents agricultural fields and crops across Asaba Demonstration Farm, Jos Highland Farm, and Sokoto Smart Farm. Its threshold-based alerts identify conditions associated with low soil moisture, low nitrogen, acidic soil, high temperature, and sensor connectivity faults, linking selected conditions to recommended management actions. The available demonstration establishes the platform’s integrated monitoring and decision-support functionality. However, because the interface identifies its environment as a live simulation, the displayed information does not independently establish improvements in crop yield, input-use efficiency, soil health, or farmer income. The study provides a technological foundation for subsequent field validation of IoT-enabled precision agriculture and its potential contribution to sustainable food security in Nigeria.","精准农业（precision agriculture）融合数字技术，以改善农业资源管理、生产力和可持续性，而物联网（Internet of Things, IoT）则通过互联传感器实现对土壤和环境条件的实时监测。在尼日利亚，水资源和肥料利用效率低下、土壤健康状况下降、气候多变以及难以及时获取农业信息等因素制约了可持续粮食生产。本研究提出AgriSense——一个由物联网驱动的精准农业平台，旨在支持尼日利亚农业条件下的数据驱动型农场管理。本研究采用混合方法研究框架，融合描述性、实验性和分析性方法，拟采用的数据来源包括物联网传感器测量数据、农民问卷调查与访谈、田间观察以及相关天气信息。AgriSense平台将土壤湿度、氮磷钾（N-P-K）、土壤pH值、温度和湿度监测与农场及田块管理、传感器状态追踪、自动警报、农艺建议和报告功能集成在一起。所展示的界面呈现了阿萨巴示范农场（Asaba Demonstration Farm）、乔斯高原农场（Jos Highland Farm）和索科托智慧农场（Sokoto Smart Farm）的农田和作物。其基于阈值的警报可识别与土壤湿度低、氮含量低、土壤酸性、高温和传感器连接故障相关的情况，并将选定情况与建议的管理措施相关联。现有演示验证了该平台的集成监测与决策支持功能。然而，由于该界面将其环境标识为实时模拟，所显示的信息并不能独立证明作物产量、投入品使用效率、土壤健康或农民收入方面的改善。本研究为后续对物联网赋能精准农业及其对尼日利亚可持续粮食安全潜在贡献的田间验证提供了技术基础。","Tech-Sphere Journal for Pure and Applied Sciences","https:\u002F\u002Fstem.techspherejournals.com\u002Findex.php\u002Ftsjpas\u002Farticle\u002Fview\u002F80","2026-10-13T00:00:00Z",{"impact":47,"substance":152,"depth":462,"authority":21,"freshness":21,"relevant":22,"comment":507},"论文提出面向尼日利亚的IoT精准农业平台AgriSense，集成土壤水分、N-P-K、pH等监测与阈值预警，但界面为模拟环境，尚未验证增产增效，属技术原型阶段，参考价值有限。",[509],{"name":503,"url":504},[27,238,375,28,511],"土壤墒情监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":515},{"candidates":516,"agricultural_evidence":48},[517,521,525,529,535,539],{"code":33,"score":117,"supported":48,"title":518,"abstract":519,"tags":520},[58],[57,254,58],[238],{"code":46,"score":152,"supported":48,"title":522,"abstract":523,"tags":524},[51],[28,51],[27,28],{"code":378,"score":170,"supported":48,"title":526,"abstract":527,"tags":528},[],[375,394],[375],{"code":190,"score":54,"supported":48,"title":530,"abstract":531,"tags":534},[],[532,533],"肥料","fertilizer",[],{"code":177,"score":54,"supported":48,"title":536,"abstract":537,"tags":538},[],[180,181],[],{"code":132,"score":62,"supported":48,"title":540,"abstract":541,"tags":542},[],[166],[],[544,545],"AgriSense 物联网 精准农业","尼日利亚 智慧农业 平台","AgriSense物联网精准农业-5085","10.5281\u002Fzenodo.23335305",{"doi":547,"openalex_id":549,"authors":550,"venue":503,"cited_by_count":95,"oa_url":504,"card":554,"direction":102,"ingested_from":103},"W7221676154",[551],{"name":552,"orcid":553},"Innocent Uzougbo Onwuegbuzie","https:\u002F\u002Forcid.org\u002F0000-0002-1582-1739",{"tldr":555,"method":556,"finding":557,"direction":102,"opportunity":558},"提出AgriSense物联网精准农业平台，用于尼日利亚农田监测与决策支持。","混合方法框架，集成土壤水分、N-P-K、pH、温湿度传感器与阈值告警。","平台实现监测与决策支持功能，但界面为模拟，尚未验证产量与效益提升。","可开展真实田间验证，量化IoT精准农业对产量、投入效率和土壤健康的影响。","2026-10-16T23:30:14.088827Z"]