[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4015":3,"related-4015":96},{"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":74,"slug":77,"view_count":22,"doi":9,"paper":78,"created_at":95},4015,"Development of a Blockchain-Internet of Things (IoT) Security Framework","https:\u002F\u002Fcigrjournal.org\u002Findex.php\u002FEjounral\u002Farticle\u002Fview\u002F10447","Internet of Things (IoT) has revolutionized data connectivity, fostering advancements across various sectors enabling real-time monitoring. The integration of blockchain technology to enhance the security of agricultural IoT data presents significant opportunities for improving data integrity and confidentiality in farming operations. The challenge lies in developing reliable framework that ensures securing of data transmission and storage in agricultural IoT networks. This study aims to develop and evaluate a blockchain-IoT framework to enhance security in agricultural data management systems, specifically designed for soil moisture sensors and weather stations. The method adopted involved developing a proof-of-concept mechanisms using JavaScript, Web3.js, and Ethereum smart contracts with Advanced Encryption Standard (AES-256) symmetric encryption cryptography. Results show that the framework maintained unbreachable encryption in man-in-the-middle (MITM) and replay attack simulations, while achieving transaction throughput of up to 100 transactions per second (TPS) with an average confirmation time of under 10 seconds. Network performance remained stable up to 500 simultaneous device connections, with only minor delays noted at 600 devices and significant delays at 1,000, where confirmation times increased to 25-30 seconds. Blockchain framework successfully enhances the security and integrity of agricultural data collected from IoT devices, providing a practical solution to secure farm data management and monitoring systems","物联网（IoT）彻底改变了数据连接方式，推动了各行业的进步，实现了实时监测。将区块链技术集成以增强农业物联网数据的安全性，为提升农业运营中的数据完整性和保密性提供了重要机遇。当前的挑战在于开发一个可靠的框架，确保农业物联网网络中数据传输和存储的安全性。本研究旨在开发并评估一个区块链-物联网框架，以增强农业数据管理系统的安全性，该框架专门针对土壤湿度传感器和气象站设计。所采用的方法包括使用JavaScript、Web3.js和以太坊智能合约，结合高级加密标准（AES-256）对称加密密码学，开发概念验证机制。结果表明，该框架在中间人攻击（MITM）和重放攻击模拟中保持了不可突破的加密性，同时实现了高达每秒100笔交易（TPS）的交易吞吐量，平均确认时间低于10秒。网络性能在多达500个同时设备连接的情况下保持稳定，仅在600个设备时出现轻微延迟，在1，000个设备时出现显著延迟，确认时间增加至25-30秒。该区块链框架成功增强了从物联网设备收集的农业数据的安全性和完整性，为保障农场数据管理和监测系统提供了一种实用的解决方案。",null,"Agricultural Engineering International: CIGR Journal","2026-09-29T00:00:00Z","论文",10,false,73,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,20,17,13,8,1,"该论文提出并验证了面向农业物联网的区块链安全框架，方法具体、数据翔实，对智慧农业数据可信管理有参考价值，但属细分技术进展，影响层级有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业物联网","区块链","数据安全","土壤墒情监测",{"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":47},[45,54,62,69],{"code":33,"score":46,"supported":47,"title":48,"abstract":51,"tags":53},26,true,[49,50],"internet of things","IoT",[52,49,50],"物联网",[28],{"code":55,"score":56,"supported":47,"title":57,"abstract":58,"tags":61},"H2",6,[],[59,60],"土壤","soil",[],{"code":63,"score":64,"supported":47,"title":65,"abstract":66,"tags":68},"I6",4,[],[67],"agricultural data",[30],{"code":70,"score":22,"supported":14,"title":71,"abstract":72,"tags":73},"I1",[],[],[27],[75,76],"区块链 农业物联网 数据安全","土壤湿度传感器 气象站 区块链","区块链农业物联网数据安全-4015",{"doi":9,"openalex_id":79,"authors":80,"venue":10,"cited_by_count":87,"oa_url":9,"card":88,"direction":92,"ingested_from":94},"W7215549195",[81,83,85],{"name":82,"orcid":9},"Opeyemi Abisoye",{"name":84,"orcid":9},"Kiddams JOSHUA",{"name":86,"orcid":9},"Blessing Abisoye",0,{"tldr":89,"method":90,"finding":91,"direction":92,"opportunity":93},"开发并评估基于区块链与物联网的农业数据安全框架，保障土壤湿度与气象站数据。","用JavaScript、Web3.js、以太坊智能合约及AES-256加密构建概","框架可抵御中间人与重放攻击，达100 TPS，500设备连接稳定，1000设备时延迟显著。","智慧农业 \u002F 农业物联网","可研究轻量级共识与边缘计算结合，提升大规模农业物联网下的吞吐与实时性。","openalex","2026-10-02T23:30:15.081491Z",{"total":56,"page":22,"page_size":56,"items":97},[98,175,252,307,388,453],{"id":99,"title":100,"url":101,"summary":102,"summary_zh":103,"content":9,"source_name":104,"source_url":105,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":107,"score_detail":108,"sources":113,"tags":115,"primary_topic":118,"secondary_topics":119,"classification":120,"search_phrases":158,"slug":161,"view_count":153,"doi":162,"paper":163,"created_at":174},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",56,{"impact":21,"substance":109,"depth":110,"authority":111,"freshness":111,"relevant":22,"comment":112},16,14,9,"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。",[114],{"name":104,"url":105},[27,28,116,117,31],"粮食安全","精准农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":121},{"candidates":122,"agricultural_evidence":47},[123,128,133,140,147,151],{"code":33,"score":124,"supported":47,"title":125,"abstract":126,"tags":127},18,[50],[52,49,50],[28],{"code":70,"score":109,"supported":47,"title":129,"abstract":131,"tags":132},[130],"precision agriculture",[117,130],[27,117],{"code":134,"score":135,"supported":47,"title":136,"abstract":137,"tags":139},"A8",7,[],[116,138],"food security",[116],{"code":141,"score":56,"supported":47,"title":142,"abstract":143,"tags":146},"F3",[],[144,145],"肥料","fertilizer",[],{"code":55,"score":56,"supported":47,"title":148,"abstract":149,"tags":150},[],[59,60],[],{"code":152,"score":153,"supported":47,"title":154,"abstract":155,"tags":157},"H3",3,[],[156],"水资源",[],[159,160],"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":162,"openalex_id":164,"authors":165,"venue":104,"cited_by_count":87,"oa_url":105,"card":169,"direction":92,"ingested_from":94},"W7221676154",[166],{"name":167,"orcid":168},"Innocent Uzougbo Onwuegbuzie","https:\u002F\u002Forcid.org\u002F0000-0002-1582-1739",{"tldr":170,"method":171,"finding":172,"direction":92,"opportunity":173},"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",{"id":176,"title":177,"url":178,"summary":179,"summary_zh":180,"content":9,"source_name":181,"source_url":178,"published_at":182,"category":12,"cover_url":9,"hotness":13,"is_selected":47,"score":183,"score_detail":184,"sources":188,"tags":190,"primary_topic":193,"secondary_topics":194,"classification":195,"search_phrases":217,"slug":220,"view_count":221,"doi":222,"paper":223,"created_at":251},3548,"Cybersecurity and Privacy in AI-Enabled Agricultural IoT Ecosystems: A Systematic Review of Threats, Safeguards, and Resilience Gaps","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fa19100827","Agricultural Internet of Things (IoT) ecosystems increasingly connect sensors, drones, edge devices, and cloud platforms to support precision farming, yet cybersecurity, privacy, and the real-world readiness of proposed safeguards remain fragmented across the literature. This study systematically reviewed cybersecurity threats, privacy concerns, AI-driven and traditional safeguards, and evidence gaps in agricultural IoT research published between 2015 and 2025. Following the Kitchenham and Charters methodology, 103 studies were selected from 2535 records retrieved across five databases. STRIDE and LINDDUN were retrospectively applied as complementary frameworks for threat and privacy classification. Because the coding scheme was multi-label, reliability was assessed at the category level using presence\u002Fabsence decisions on a 20-study sample and observed agreement ranged from 75% to 95% for STRIDE and 95% to 100% for LINDDUN, with interpretable Cohen’s κ values ranging from 0.348 to 0.794 and 0.875 to 1.000, respectively. All included studies also underwent quality appraisal and a supplementary ecological-validity assessment. Denial-of-service, tampering, and spoofing were the most frequently reported threats, concentrated at the device, network, and cloud layers, while the edge layer remained underexamined. AI- and machine-learning-based intrusion detection and privacy-preserving methods such as federated learning emerged as prominent safeguards, but adversarial manipulation of agricultural AI models received limited attention. Privacy research remained oriented toward confidentiality, with 90.3% of studies referencing no applicable regulatory framework. Most importantly, only 8 of 103 studies (7.8%) received a High ecological-validity rating, showing how rarely the evidence base is grounded in real agricultural field conditions. The review identifies field-grounded evaluation, adversarially robust AI, privacy governance, and cyber resilience as priorities for future agricultural IoT security research.","农业物联网（IoT）生态系统日益将传感器、无人机、边缘设备和云平台连接起来，以支持精准农业，然而网络安全、隐私以及所提出保障措施的现实适用性在文献中仍呈现碎片化状态。本研究系统综述了2015年至2025年间发表的农业物联网研究中的网络安全威胁、隐私问题、人工智能驱动及传统保障措施以及证据缺口。遵循Kitchenham和Charters方法论，从五个数据库检索到的2535条记录中筛选出103项研究。STRIDE和LINDDUN被回溯性应用为威胁与隐私分类的互补框架。由于编码方案为多标签，可靠性在类别层面通过20项研究样本的存在\u002F缺失判定进行评估，STRIDE的观察一致率为75%至95%，LINDDUN为95%至100%，可解释的Cohen's κ值分别为0.348至0.794和0.875至1.000。所有纳入研究还接受了质量评价和补充性生态效度评估。拒绝服务、篡改和欺骗是报告最频繁的威胁，集中在设备层、网络层和云层，而边缘层仍未被充分考察。基于人工智能和机器学习的入侵检测以及联邦学习等隐私保护方法成为突出的保障措施，但农业人工智能模型的对抗性操纵受到的关注有限。隐私研究仍以保密性为导向，90.3%的研究未引用任何适用的监管框架。最重要的是，103项研究中仅有8项（7.8%）获得高生态效度评级，表明证据基础鲜有扎根于真实农业田间条件。本综述将田间实证评估、对抗鲁棒人工智能、隐私治理和网络韧性确定为未来农业物联网安全研究的优先事项。","Algorithms","2026-09-25T00:00:00Z",82,{"impact":124,"substance":185,"depth":186,"authority":20,"freshness":111,"relevant":22,"comment":187},23,19,"系统综述103项研究，揭示农业物联网安全证据多脱离田间实际，对智慧农业安全研究有较高参考价值。",[189],{"name":181,"url":178},[27,191,28,192,30],"农业人工智能","隐私保护",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":196},{"candidates":197,"agricultural_evidence":47},[198,202,208,212],{"code":33,"score":124,"supported":47,"title":199,"abstract":200,"tags":201},[50],[52,49,50],[28],{"code":203,"score":135,"supported":47,"title":204,"abstract":205,"tags":207},"I2",[],[191,206],"机器学习",[191],{"code":70,"score":64,"supported":47,"title":209,"abstract":210,"tags":211},[],[117],[27],{"code":63,"score":213,"supported":14,"title":214,"abstract":215,"tags":216},2,[],[],[30,192],[218,219],"农业物联网 网络安全","农业AI 隐私保护 联邦学习","农业物联网网络安全-3548",5,"10.3390\u002Fa19100827",{"doi":222,"openalex_id":224,"authors":225,"venue":181,"cited_by_count":87,"oa_url":178,"card":246,"direction":92,"ingested_from":94},"W7214403401",[226,229,232,235,238,241,243],{"name":227,"orcid":228},"Emmanuel Kojo Gyamfi","https:\u002F\u002Forcid.org\u002F0009-0002-0441-2830",{"name":230,"orcid":231},"Jess Kropczynski","https:\u002F\u002Forcid.org\u002F0000-0002-7458-6003",{"name":233,"orcid":234},"Jacques Bou Abdo","https:\u002F\u002Forcid.org\u002F0000-0002-3482-9154",{"name":236,"orcid":237},"Joseph Samuel Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-2555-8142",{"name":239,"orcid":240},"Mustapha Awinsongya Yakubu","https:\u002F\u002Forcid.org\u002F0009-0005-6623-0858",{"name":242,"orcid":9},"Anthony Tsetse",{"name":244,"orcid":245},"Gertrude Kaneah Abagale","https:\u002F\u002Forcid.org\u002F0009-0009-1502-3188",{"tldr":247,"method":248,"finding":249,"direction":92,"opportunity":250},"系统综述2015-2025年农业物联网的网络安全、隐私威胁与防护措施及证据缺口。","Kitchenham系统综述法，筛选103项研究，用STRIDE与LINDDUN","拒绝服务、篡改、欺骗威胁最多，边缘层研究不足，仅7.8%研究具高生态效度。","农业AI模型的对抗鲁棒性、边缘层安全、隐私治理与真实田间条件下的韧性评估是明显空白。","2026-09-26T23:30:15.415998Z",{"id":253,"title":254,"url":255,"summary":256,"summary_zh":257,"content":9,"source_name":258,"source_url":255,"published_at":259,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":260,"score_detail":261,"sources":264,"tags":266,"primary_topic":269,"secondary_topics":270,"classification":271,"search_phrases":291,"slug":294,"view_count":87,"doi":295,"paper":296,"created_at":306},5554,"Smart Agriculture using IoT","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531185","The Smart Agriculture Using IoT system uses different sensors installed in the agricultural field to collect data about soil and environmental conditions. The collected data is sent to a microcontroller, such as an ESP32 or Arduino with a suitable communication module. When the soil moisture level becomes lower than the required level, the system can automatically switch on the water pump. When sufficient moisture is detected, the pump switches off. Farmers can also monitor field conditions through a mobile application or web dashboard. This system reduces manual work and helps farmers use water and other resources efficiently.","基于物联网的智慧农业系统利用安装在农田中的各类传感器采集土壤与环境状况数据。采集到的数据被传送至微控制器，如ESP32或配备合适通信模块的Arduino。当土壤湿度低于所需水平时，系统可自动开启水泵；当检测到湿度充足时，水泵则关闭。农民还可通过移动应用程序或网页仪表盘监测田间状况。该系统减少了人工劳作，帮助农民高效利用水资源及其他资源。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",49,{"impact":21,"substance":262,"depth":13,"authority":111,"freshness":13,"relevant":22,"comment":263},12,"论文介绍基于ESP32\u002FArduino的土壤湿度自动灌溉与远程监测方案，属常规技术实现，方法新颖性有限，但主题贴合智慧农业且时效性强，可作为一般资讯收录。",[265],{"name":258,"url":255},[27,28,267,268],"智能灌溉","土壤墒情",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":272},{"candidates":273,"agricultural_evidence":47},[274,279,283,287],{"code":70,"score":17,"supported":47,"title":275,"abstract":277,"tags":278},[276],"smart agriculture",[27,276],[27],{"code":33,"score":17,"supported":47,"title":280,"abstract":281,"tags":282},[50],[52,50],[28],{"code":55,"score":135,"supported":47,"title":284,"abstract":285,"tags":286},[],[59,60],[268],{"code":152,"score":64,"supported":47,"title":288,"abstract":289,"tags":290},[],[156],[267],[292,293],"ESP32 土壤湿度 自动灌溉","农业物联网 移动端监测","ESP32土壤湿度自动灌溉-5554","10.5281\u002Fzenodo.23531185",{"doi":295,"openalex_id":297,"authors":298,"venue":258,"cited_by_count":87,"oa_url":255,"card":301,"direction":92,"ingested_from":94},"W7223137846",[299],{"name":300,"orcid":9},"Prachi Khairnar",{"tldr":302,"method":303,"finding":304,"direction":92,"opportunity":305},"基于物联网传感器实现农田土壤湿度自动监测与灌溉控制。","部署土壤与环境传感器，经ESP32\u002FArduino采集数据并联动水泵。","系统可自动启停水泵并通过手机或网页端远程监控，节水省工。","可引入多源传感融合与预测性灌溉决策，弥补仅靠阈值控制的局限。","2026-10-22T11:20:18.299106Z",{"id":308,"title":309,"url":310,"summary":311,"summary_zh":312,"content":9,"source_name":258,"source_url":310,"published_at":313,"category":12,"cover_url":9,"hotness":314,"is_selected":14,"score":15,"score_detail":315,"sources":319,"tags":323,"primary_topic":326,"secondary_topics":331,"classification":333,"search_phrases":365,"slug":368,"view_count":87,"doi":369,"paper":370,"created_at":387},5474,"Real-World Impact: Transforming Agriculture with Technology A Multi-Stakeholder Review of Smart Monitoring, Precision Irrigation, and Digital Value-Chain Integration","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23510500","There's a deliberate and significant transition from input to information. This article combines peer-reviewed evidence and public-sector data on six interrelated technology clusters that are fundamentally changing on-farm and off-farm decision making artificial intelligence (AI) based Farm Management Platforms, Internet of Things (IoT) smart irrigation, aerial and satellite crop monitoring, blockchain-enabled supply-chain digitisation, sustainability and carbon-tracking instruments, and farm data analytics dashboards. In the literature examined, precision tools have been correlated with 15% to 30% gains in crop yields, 30% to 55% of water saved during irrigation, and 10% to 20% of operational costs reduced, all with a range of variability based on the crop, the geographical region, and the size of the farm. It examines the mechanisms of these effects on the one hand and the differential rates of benefit, on the other, between large commercial farms and smallholders, and the policy and infrastructural conditions that drive actual adoption. It also highlights areas of current evidence which are lacking, such as limited long horizon field trials, poor interoperability standards, and lack of social outcome data. The article ends with a discussion on how to turn pilots into lasting, system-level change in food production, both from the government and enterprise side, as well as from the perspective of small and medium producers.","从投入要素到信息的转变是有意为之且意义重大的。本文结合经同行评审的证据与公共部门数据，聚焦六个相互关联、正在从根本上改变农场内外决策方式的技术集群：基于人工智能（AI）的农场管理平台、物联网（IoT）智能灌溉、航空与卫星作物监测、区块链赋能的供应链数字化、可持续性与碳追踪工具，以及农场数据分析仪表盘。在所考察的文献中，精准工具与作物产量提升15%至30%、灌溉用水节约30%至55%、运营成本降低10%至20%相关联，且这些效果因作物、地理区域和农场规模而存在不同程度的差异。本文一方面考察这些效应的作用机制，另一方面分析大型商业农场与小农户之间受益程度的差异，以及驱动实际采用的政策与基础设施条件。文章还指出了当前证据的薄弱之处，如缺乏长期田间试验、互操作性标准不完善以及社会成效数据缺失。文章最后讨论了如何从政府与企业两侧，以及中小生产者视角出发，将试点项目转化为粮食生产中持久、系统层面的变革。","2026-11-25T00:00:00Z",25,{"impact":316,"substance":317,"depth":19,"authority":20,"freshness":87,"relevant":22,"comment":318},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。",[320,321],{"name":258,"url":310},{"name":258,"url":322},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[27,191,28,324,325],"区块链溯源","精准灌溉",{"code":152,"slug":327,"name":328,"path":329,"parent_code":330},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[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":47},[336,341,345,350,356,361],{"code":152,"score":17,"supported":47,"title":337,"abstract":338,"tags":340},[327],[339,327],"灌溉",[325],{"code":33,"score":13,"supported":47,"title":342,"abstract":343,"tags":344},[],[52,49,50],[28],{"code":203,"score":64,"supported":47,"title":346,"abstract":347,"tags":349},[],[348],"artificial intelligence",[191],{"code":351,"score":153,"supported":47,"title":352,"abstract":353,"tags":355},"G2",[],[354],"小农户",[],{"code":357,"score":22,"supported":14,"title":358,"abstract":359,"tags":360},"E4",[],[],[324],{"code":70,"score":22,"supported":14,"title":362,"abstract":363,"tags":364},[],[],[27],[366,367],"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":369,"openalex_id":371,"authors":372,"venue":258,"cited_by_count":87,"oa_url":310,"card":381,"direction":92,"ingested_from":94},"W7223034914",[373,376,378],{"name":374,"orcid":375},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":377,"orcid":9},"Dr.T.Baskar",{"name":379,"orcid":380},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":382,"method":383,"finding":384,"direction":385,"opportunity":386},"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":389,"title":390,"url":391,"summary":392,"summary_zh":393,"content":9,"source_name":394,"source_url":391,"published_at":395,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":396,"score_detail":397,"sources":399,"tags":401,"primary_topic":404,"secondary_topics":405,"classification":410,"search_phrases":427,"slug":430,"view_count":87,"doi":431,"paper":432,"created_at":452},5376,"Agri-Synergy: A Hardware–Software Co-Designed Hybrid IoT System for Sensor Interface Standardization and LLM-Driven Interactive Decision Support","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriengineering8100446","This paper presents Agri-Synergy, a hybrid Internet of Things (IoT) system featuring a hardware–software co-design to tackle sensor data heterogeneity and the barriers to effective AI-driven decision support in smart agriculture. Unlike software-based data harmonization, we propose a universal sensor printed circuit board (PCB) with RS485\u002FModbus interface for hardware unification, complemented by on-node edge processing that ensures standardized data from source. To lower the entry barrier for farmers, the system integrates the DeepSeek large language model (LLM) API, combining static agricultural knowledge with real-time sensor data, for context-aware, natural language-based decision support, functioning without local model deployment. Field deployment showed reference-verified sensor accuracy predominantly within manufacturer specifications, stable 4G connectivity during the 4-h benchmark but intermittent dropouts during the 20-day deployment, and remote-control success rates of 96% (normal) and 80% (weak signal at 300 m). Multi-node concurrent transmission resulted in average latency of 238.0 ± 27.1 ms and packet loss below 0.5%. The system achieves a 3–5× cost reduction compared to commercial platforms. These results demonstrate that addressing data heterogeneity from source through hardware unification and software edge processing, while leveraging AI to lower the barrier to system adoption, provides a more reliable foundation for data synchronization and accessible AI-driven analytics.","本文提出Agri-Synergy，一种采用软硬件协同设计的混合物联网（IoT）系统，旨在解决智慧农业中传感器数据异构性问题以及有效AI驱动决策支持的障碍。与基于软件的数据协调方式不同，我们提出了一种带有RS485\u002FModbus接口的通用传感器印刷电路板（PCB）以实现硬件统一，并辅以节点端边缘处理，确保从源头获得标准化数据。为降低农民的使用门槛，该系统集成了DeepSeek大语言模型（LLM）API，将静态农业知识与实时传感器数据相结合，提供上下文感知的、基于自然语言的决策支持，且无需本地部署模型即可运行。现场部署表明，经参考验证的传感器精度基本处于制造商规格范围内；在4小时基准测试期间4G连接稳定，但在20天部署期间出现间歇性断连；远程控制成功率分别为96%（正常信号）和80%（300 m处弱信号）。多节点并发传输的平均延迟为238.0 ± 27.1 ms，丢包率低于0.5%。与商业平台相比，该系统实现了3–5倍的成本降低。这些结果表明，通过硬件统一和软件边缘处理从源头解决数据异构性问题，同时利用AI降低系统采用门槛，可为数据同步和可及的AI驱动分析提供更可靠的基础。","AgriEngineering","2026-10-19T00:00:00Z",76,{"impact":109,"substance":317,"depth":124,"authority":262,"freshness":111,"relevant":22,"comment":398},"硬件统一接口与边缘处理结合大模型决策支持的农业物联网系统论文，方法新颖、有实地部署数据，但属细分技术进展，公共影响有限。",[400],{"name":394,"url":391},[27,191,28,402,403],"大语言模型","传感器标准化",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[406],{"code":203,"slug":407,"name":408,"path":409,"parent_code":37},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":40,"locked":14,"version":41,"method":42,"evidence":411},{"candidates":412,"agricultural_evidence":47},[413,417,423],{"code":33,"score":124,"supported":47,"title":414,"abstract":415,"tags":416},[50],[52,49,50],[28],{"code":203,"score":109,"supported":47,"title":418,"abstract":420,"tags":422},[419],"decision support",[421,419],"large language model",[191,402],{"code":70,"score":135,"supported":47,"title":424,"abstract":425,"tags":426},[],[27,276],[27],[428,429],"Agri-Synergy 物联网 传感器","DeepSeek 决策支持","Agri-Synergy物联网传感器-5376","10.3390\u002Fagriengineering8100446",{"doi":431,"openalex_id":433,"authors":434,"venue":394,"cited_by_count":87,"oa_url":391,"card":447,"direction":92,"ingested_from":94},"W7222920424",[435,437,439,441,443,445],{"name":436,"orcid":9},"Leqi Lai",{"name":438,"orcid":9},"Langtao Duan",{"name":440,"orcid":9},"Yuhao Chen",{"name":442,"orcid":9},"Yuhang Zheng",{"name":444,"orcid":9},"Jinyu Yang",{"name":446,"orcid":9},"Mingyao Wen",{"tldr":448,"method":449,"finding":450,"direction":92,"opportunity":451},"提出软硬件协同的混合物联网系统，统一传感器接口并用大模型提供交互式决策支持。","通用RS485\u002FModbus传感器PCB、节点边缘处理、DeepSeek大模型A","传感器精度达标，多节点延迟238ms、丢包低于0.5%，成本降低3-5倍。","可研究弱信号下通信可靠性提升与边缘-云端LLM协同的长期田间鲁棒性。","2026-10-20T23:30:08.319698Z",{"id":454,"title":455,"url":456,"summary":457,"summary_zh":458,"content":9,"source_name":459,"source_url":456,"published_at":460,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":461,"score_detail":462,"sources":464,"tags":466,"primary_topic":469,"secondary_topics":475,"classification":482,"search_phrases":522,"slug":525,"view_count":87,"doi":9,"paper":526,"created_at":544},5313,"Real-Time IoT-Integrated Ensemble LSTM–Bi-LSTM Framework for Greenhouse Microclimate Forecasting","https:\u002F\u002Fwasrpublication.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F512","Accurate prediction of greenhouse microclimates is essential for optimizing crop growth and resource utilization in precision agriculture. The research presents an IoT-integrated ensemble deep learning framework for real-time greenhouse microclimate forecasting using Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. Environmental variables, including temperature, light intensity, soil moisture, and humidity, were acquired through IoT sensor nodes deployed in a controlled greenhouse environment. A benchmark greenhouse dataset was first used for model development and hyperparameter optimization, while an original IoT-based dataset collected during cucumber, tomato, and pepper cultivation at the Federal University Wukari, Nigeria, was used for validation and real-world deployment. The data were preprocessed through noise filtering, missing value imputation, Z-score-based outlier removal, and Min–Max normalization. A weighted ensemble strategy was then applied to combine LSTM and Bi-LSTM predictions for improved robustness. Experimental results show that the Bi-LSTM model outperformed the LSTM model, achieving R² = 0.9981, MAE = 0.0088, RMSE = 0.0110, and MSE = 0.0001, compared to LSTM results of R² = 0.9956, MAE = 0.0133, RMSE = 0.0167, and MSE = 0.0003. Statistical validation using paired t-test, Wilcoxon signed-rank test, confidence intervals, and multi-run analysis confirmed that the performance improvement is statistically significant and robust. The proposed framework enables accurate real-time microclimate forecasting and intelligent environmental monitoring, providing a scalable solution for precision greenhouse management, improved crop productivity, optimized energy utilization, and efficient irrigation scheduling.","准确预测温室微气候对于精准农业中优化作物生长和资源利用至关重要。本研究提出了一种集成物联网的集成深度学习框架，利用长短期记忆网络（LSTM）和双向长短期记忆网络（Bi-LSTM）进行实时温室微气候预测。温度、光照强度、土壤湿度和湿度等环境变量通过部署在受控温室环境中的物联网传感器节点采集。研究首先使用基准温室数据集进行模型开发和超参数优化，随后使用在尼日利亚武卡里联邦大学黄瓜、番茄和辣椒种植期间采集的原始物联网数据集进行验证和实际部署。数据经过噪声滤波、缺失值插补、基于Z分数的异常值去除和最小—最大归一化预处理。然后采用加权集成策略融合LSTM和Bi-LSTM的预测结果以提高鲁棒性。实验结果表明，Bi-LSTM模型优于LSTM模型，其R² = 0.9981、MAE = 0.0088、RMSE = 0.0110、MSE = 0.0001，而LSTM的结果为R² = 0.9956、MAE = 0.0133、RMSE = 0.0167、MSE = 0.0003。采用配对t检验、Wilcoxon符号秩检验、置信区间和多轮运行分析进行的统计验证证实，性能提升具有统计显著性和鲁棒性。所提出的框架能够实现准确的实时微气候预测和智能环境监测，为精准温室管理、提高作物生产力、优化能源利用和高效灌溉调度提供了可扩展的解决方案。","World Journal of Multidisciplinary Studies","2026-10-16T00:00:00Z",67,{"impact":262,"substance":317,"depth":19,"authority":111,"freshness":21,"relevant":22,"comment":463},"方法扎实、验证充分，但属单点应用研究，公共影响有限，适合作为技术前沿条目而非头条精选。",[465],{"name":459,"url":456},[27,191,28,467,468],"温室环境","小气候预测",{"code":470,"slug":471,"name":472,"path":473,"parent_code":474},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[476,477],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":478,"slug":479,"name":480,"path":481,"parent_code":474},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",{"status":40,"locked":14,"version":41,"method":42,"evidence":483},{"candidates":484,"agricultural_evidence":47},[485,489,495,504,508,514,518],{"code":33,"score":17,"supported":47,"title":486,"abstract":487,"tags":488},[50],[52,50],[28],{"code":470,"score":110,"supported":47,"title":490,"abstract":492,"tags":494},[491],"greenhouse",[493,491],"温室",[],{"code":478,"score":111,"supported":47,"title":496,"abstract":497,"tags":503},[],[498,499,500,501,502],"番茄","辣椒","黄瓜","tomato","cucumber",[],{"code":70,"score":135,"supported":47,"title":505,"abstract":506,"tags":507},[],[117,130],[27],{"code":203,"score":135,"supported":47,"title":509,"abstract":510,"tags":513},[],[511,512],"深度学习","deep learning",[191],{"code":55,"score":56,"supported":47,"title":515,"abstract":516,"tags":517},[],[59,60],[],{"code":152,"score":56,"supported":47,"title":519,"abstract":520,"tags":521},[],[339,327],[],[523,524],"温室 小气候 LSTM 预测","尼日利亚 温室 IoT 黄瓜 番茄","温室小气候LSTM预测-5313",{"doi":9,"openalex_id":527,"authors":528,"venue":459,"cited_by_count":87,"oa_url":9,"card":539,"direction":92,"ingested_from":94},"W7222909837",[529,531,533,535,537],{"name":530,"orcid":9},"Siman Emmanuel*",{"name":532,"orcid":9},"Edward Onyebueke Agu",{"name":534,"orcid":9},"Musa Gani",{"name":536,"orcid":9},"Oladunjoye John Abiodun",{"name":538,"orcid":9},"Anagu Emmanuel John",{"tldr":540,"method":541,"finding":542,"direction":92,"opportunity":543},"提出物联网集成LSTM与Bi-LSTM加权集成框架，实时预测温室微气候。","IoT传感器采集温光湿数据，经预处理后用LSTM-Bi-LSTM加权集成建模。","Bi-LSTM优于LSTM，R²达0.9981，统计检验证实性能提升显著稳健。","可探索边缘端轻量化部署与多作物跨温室迁移学习，提升实时预测泛化性。","2026-10-19T23:30:13.295236Z"]