[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-477":3,"related-477":77},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":71,"slug":74,"view_count":75,"doi":8,"paper":8,"created_at":76},477,"设施农业鼓起农民\"钱袋子\"","https:\u002F\u002Fso.html5.qq.com\u002Fpage\u002Freal\u002Fsearch_news?docid=70000021_7226a7cff5843652","国务院《加快农业农村现代化\"十五五\"规划》明确构建多元化食物供给体系、深入实施设施农业现代化提升行动。设施农业突破时空与资源限制,加速物联网、大数据、人工智能技术集成,延伸产业链价值链。甘肃建成全球最大集中连片日光温室生产区,河北设施果蔬产业成为京津\"菜篮子\"压舱石。文章提出从政策、科技、产业维度协同发力、加强核心技术研发与运用。",null,"国务院印发的《加快农业农村现代化“十五五”规划》，明确了构建多元化食物供给体系，提出深入实施设施农业现代化提升行动。设施农业的独特优势，可以有效弥补我国地少人多、水资源短缺的农业生产短板，对于构建多元化食物供给体系、形成农业新质生产力具有重要战略意义。具体来讲，主要体现在三方面。\n\n一是突破时空与资源限制。设施农业有助于从根本上改变传统农业“靠天吃饭”的局面，它通过创新装备设施改造农业生产环境，使农业生产场域拓展到戈壁、盐碱地、丘陵、海洋甚至楼宇，既有效缓解了“粮菜争地”的矛盾，更在确保18亿亩耕地红线不动摇的前提下，开辟保障食物安全的“第二战场”。例如，甘肃省以戈壁农业闻名，充分利用戈壁滩丰富的太阳能资源，建成全球最大的集中连片日光温室生产区，使得戈壁设施农业面积达到空前规模。\n\n二是加速技术集成与创新应用。设施农业促使物联网、大数据、人工智能等前沿技术与农业生产实现深度融合，使农业从能够自动调控水肥、光照、温度和湿度的智慧温室，到垂直布局、无土栽培的植物工厂，再到能够精准饲喂的集约化养殖场，进而推动生产要素创新性配置，实现劳动生产率、土地产出率和资源利用率的大幅提升。\n\n三是延伸产业链与价值链。设施农业的推广加速了上下游产业协同并进。上游的种苗繁育、装备制造、新型农资等，中游的标准化生产等，下游的精深加工、冷链物流、品牌营销、休闲观光等，共同构成庞大产业集群，极大地提升农产品附加值，为农民提供了更多元的就业和增收渠道。比如，河北省的设施农业不仅是保障京津“菜篮子”的“压舱石”，更是富民兴村的支柱产业，有的地区通过发展高附加值设施果蔬产业，有效带动当地数万名农民“钱袋子”鼓起来，推动乡村全面振兴落地见效。\n\n目前，我国设施农业的发展仍存在总体规模不足、区域发展不平衡、技术装备水平参差不齐等问题。面向未来，应从政策、科技、产业等维度协同发力。\n\n加强各类政策支持。在严守耕地红线和生态保护红线的前提下，不断完善与设施农业相关的用地政策，持续优化用地分类管理和审批备案制度，保障设施农业发展的合理空间。优化财政补贴和金融配套服务，针对不同水平的设施、技术以及节能环保效益等调整补贴资金，引导更多社会资本在这一重点领域投资。\n\n加强核心技术研发与运用。不断完善产、学、研、用深度融合的技术创新体系，着力攻克农业机器人、高端传感器等“卡脖子”技术，大力推进数字技术与农艺深度融合。因地制宜加快建设能够融合数据采集、智能决策和精准作业的智慧农业示范区，促使更多创新成果转化为现实生产力。\n\n加强产业体系与企业品牌建设。引导设施农业从较多集中于生产环节向构建完整的现代产业体系转变，以推进标准化生产健全从田头到餐桌的全过程质量安全追溯体系。孵化一批具有高影响力的企业品牌，通过品牌溢价提升市场竞争力，鼓励这些企业以农产品深加工、休闲采摘、科普教育等实现价值链的持续攀升。\n\n加强经营主体的多元化和专业化。积极培育家庭农场、农民合作社等新型农业经营主体，探索发展多种形式的规模经营。提升对“新农人”的专业培训，打造更多爱农业、懂技术、善经营的专业化人才队伍，为设施农业发展提供优质人才保障。（作者：吕耀龙 来源：经济日报）","农民日报 2026-08-13","2026-08-12T16:00:00Z","报道",10,true,84,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},25,20,18,12,9,1,"全国性设施农业政策解读，内容详实，来源权威，时效性好，具有较高行业价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"十五五","设施农业","物联网","日光温室","菜篮子",{"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":41,"version":42,"method":43,"evidence":44},"classified",false,"2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":14},[46,54,59,66],{"code":47,"score":48,"supported":14,"title":49,"abstract":50,"tags":53},"J2",6,[],[51,52],"农业现代化","农业农村现代化",[],{"code":33,"score":55,"supported":14,"title":56,"abstract":57,"tags":58},4,[],[29],[29],{"code":60,"score":61,"supported":14,"title":62,"abstract":63,"tags":65},"B7",3,[],[64],"温室",[],{"code":67,"score":22,"supported":41,"title":68,"abstract":69,"tags":70},"J1",[],[],[27],[72,73],"日光温室 设施农业 十五五 物联网","日光温室 设施农业","日光温室设施农业十五五物联网-477",11,"2026-08-13T23:53:43.993976Z",{"total":48,"page":22,"page_size":48,"items":78},[79,156,269,333,389,453],{"id":80,"title":81,"url":82,"summary":83,"summary_zh":84,"content":8,"source_name":85,"source_url":82,"published_at":86,"category":87,"cover_url":8,"hotness":13,"is_selected":41,"score":88,"score_detail":89,"sources":95,"tags":97,"primary_topic":101,"secondary_topics":102,"classification":103,"search_phrases":126,"slug":129,"view_count":22,"doi":130,"paper":131,"created_at":155},2118,"IoT-Enhanced forecasting and early warning system for whitefly incidence in greenhouses","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112420","IoT-Enhanced forecasting and early warning system for whitefly incidence in greenhouses。Computers and Electronics in Agriculture","物联网增强的温室烟粉虱发生预测与预警系统","Computers and Electronics in Agriculture","2026-09-10T00:00:00Z","论文",73,{"impact":90,"substance":19,"depth":91,"authority":92,"freshness":93,"relevant":22,"comment":94},16,17,14,8,"核心期刊发表的温室白粉虱IoT预测预警研究，方法新颖且面向设施农业实际防控需求，具备专业参考价值。",[96],{"name":85,"url":82},[98,99,28,29,100],"智慧农业","农业人工智能","病虫害预警",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":41,"version":42,"method":43,"evidence":104},{"candidates":105,"agricultural_evidence":14},[106,112,116,121],{"code":33,"score":107,"supported":14,"title":108,"abstract":110,"tags":111},15,[109],"IoT",[29,109],[29],{"code":60,"score":61,"supported":14,"title":113,"abstract":114,"tags":115},[],[64],[],{"code":117,"score":22,"supported":41,"title":118,"abstract":119,"tags":120},"I1",[],[],[98],{"code":122,"score":22,"supported":41,"title":123,"abstract":124,"tags":125},"I2",[],[],[99],[127,128],"农业人工智能 病虫害预警 智慧农业 设施农业","农业人工智能 病虫害预警","农业人工智能病虫害预警智慧农业设施农业-2118","10.1016\u002Fj.compag.2026.112420",{"doi":130,"openalex_id":132,"authors":133,"venue":85,"cited_by_count":147,"oa_url":82,"card":148,"direction":152,"ingested_from":154},"W7212169080",[134,137,140,142,144],{"name":135,"orcid":136},"Lin‐Ya Chiu","https:\u002F\u002Forcid.org\u002F0000-0003-1691-5582",{"name":138,"orcid":139},"Dan Jeric Arcega Rustia","https:\u002F\u002Forcid.org\u002F0000-0002-5855-8109",{"name":141,"orcid":8},"Ya‐Fang Wu",{"name":143,"orcid":8},"Jui‐Yung Chung",{"name":145,"orcid":146},"Ta‐Te Lin","https:\u002F\u002Forcid.org\u002F0000-0003-0852-1372",0,{"tldr":149,"method":150,"finding":151,"direction":152,"opportunity":153},"构建物联网增强的温室白粉虱发生预测预警系统。","物联网传感器采集温室环境数据，结合预测模型实现白粉虱预警。","物联网数据可提升温室白粉虱发生预测与早期预警能力。","智慧农业 \u002F 农业物联网","可探索多模态数据融合与迁移学习，提升不同温室场景下虫害预警泛化性。","openalex","2026-09-11T23:30:01.850931Z",{"id":157,"title":158,"url":159,"summary":160,"summary_zh":161,"content":8,"source_name":162,"source_url":159,"published_at":163,"category":87,"cover_url":8,"hotness":13,"is_selected":41,"score":164,"score_detail":165,"sources":169,"tags":171,"primary_topic":174,"secondary_topics":178,"classification":186,"search_phrases":244,"slug":247,"view_count":147,"doi":248,"paper":249,"created_at":268},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":19,"substance":166,"depth":19,"authority":167,"freshness":21,"relevant":22,"comment":168},22,13,"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。",[170],{"name":162,"url":159},[98,99,29,172,173],"精准农业","气候韧性",{"code":122,"slug":175,"name":176,"path":177,"parent_code":37},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[179,180],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":181,"slug":182,"name":183,"path":184,"parent_code":185},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":40,"locked":41,"version":42,"method":43,"evidence":187},{"candidates":188,"agricultural_evidence":14},[189,200,205,211,217,225,232,238],{"code":122,"score":19,"supported":14,"title":190,"abstract":192,"tags":199},[191],"artificial intelligence",[193,194,195,196,197,198],"深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[99],{"code":117,"score":90,"supported":14,"title":201,"abstract":203,"tags":204},[202],"precision agriculture",[172,202],[98,172],{"code":33,"score":13,"supported":14,"title":206,"abstract":207,"tags":210},[],[29,208,109,209],"边缘计算","edge computing",[29],{"code":181,"score":21,"supported":14,"title":212,"abstract":213,"tags":216},[],[214,215,182],"灌溉","水资源",[],{"code":218,"score":219,"supported":14,"title":220,"abstract":221,"tags":224},"H7",7,[],[222,223],"气候变化","climate change",[173],{"code":226,"score":48,"supported":14,"title":227,"abstract":228,"tags":231},"H2",[],[229,230],"土壤","soil",[],{"code":233,"score":61,"supported":14,"title":234,"abstract":235,"tags":237},"A7",[],[236],"间作",[],{"code":239,"score":61,"supported":14,"title":240,"abstract":241,"tags":243},"F3",[],[242],"养分管理",[],[245,246],"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":248,"openalex_id":250,"authors":251,"venue":162,"cited_by_count":147,"oa_url":159,"card":262,"direction":152,"ingested_from":154},"W7223121456",[252,255,257,259],{"name":253,"orcid":254},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":256,"orcid":8},"Oluwadare OYEBODE",{"name":258,"orcid":8},"Iretiayo Adelaiye",{"name":260,"orcid":261},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":263,"method":264,"finding":265,"direction":266,"opportunity":267},"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":270,"title":271,"url":272,"summary":273,"summary_zh":274,"content":8,"source_name":275,"source_url":272,"published_at":276,"category":87,"cover_url":8,"hotness":13,"is_selected":41,"score":277,"score_detail":278,"sources":280,"tags":282,"primary_topic":285,"secondary_topics":286,"classification":288,"search_phrases":309,"slug":312,"view_count":147,"doi":313,"paper":314,"created_at":332},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":20,"substance":19,"depth":90,"authority":167,"freshness":93,"relevant":22,"comment":279},"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。",[281],{"name":275,"url":272},[98,29,172,283,284],"节水灌溉","LoRa",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[287],{"code":181,"slug":182,"name":183,"path":184,"parent_code":185},{"status":40,"locked":41,"version":42,"method":43,"evidence":289},{"candidates":290,"agricultural_evidence":14},[291,296,300,304],{"code":33,"score":19,"supported":14,"title":292,"abstract":293,"tags":295},[109],[29,294,109],"internet of things",[29],{"code":181,"score":107,"supported":14,"title":297,"abstract":298,"tags":299},[182],[214,182],[283],{"code":226,"score":48,"supported":14,"title":301,"abstract":302,"tags":303},[],[229,230],[],{"code":117,"score":305,"supported":41,"title":306,"abstract":307,"tags":308},2,[],[],[98,172],[310,311],"ESP32 LoRa 智能灌溉","SX1278 精准灌溉","ESP32LoRa智能灌溉-5378","10.3390\u002Felectronics15204831",{"doi":313,"openalex_id":315,"authors":316,"venue":275,"cited_by_count":147,"oa_url":272,"card":327,"direction":152,"ingested_from":154},"W7222959710",[317,319,321,324],{"name":318,"orcid":8},"Hanfoug Salah",{"name":320,"orcid":8},"Riad Saidi",{"name":322,"orcid":323},"Abdelaziz Aouiche","https:\u002F\u002Forcid.org\u002F0000-0003-1378-7941",{"name":325,"orcid":326},"Yazeed Alkhrijah","https:\u002F\u002Forcid.org\u002F0000-0001-7352-003X",{"tldr":328,"method":329,"finding":330,"direction":152,"opportunity":331},"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":334,"title":335,"url":336,"summary":337,"summary_zh":338,"content":8,"source_name":339,"source_url":336,"published_at":340,"category":87,"cover_url":8,"hotness":13,"is_selected":41,"score":341,"score_detail":342,"sources":344,"tags":346,"primary_topic":350,"secondary_topics":351,"classification":352,"search_phrases":369,"slug":372,"view_count":147,"doi":373,"paper":374,"created_at":388},5309,"A Multi-Layer Contextual Model for Managing Data Overload in Smart Hydroponics","https:\u002F\u002Fdoi.org\u002F10.32890\u002Fjict2026.25.4.4","The widespread use of the Internet of Things (IoT) in hydroponic agriculture allows continuous monitoring of environmental conditions and provides real-time information to support crop management. However, the increasing amount of sensor data generated makes the monitoring process more complex as users need to interpret multiple environmental parameters simultaneously. Existing hydroponic monitoring systems focus on data collection and visualization, while the management and information delivery aspects to reduce data overload are still limited. This study proposes a multi-layer contextual model to manage environmental information in an intelligent hydroponic monitoring system. The model consists of five interconnected layers (Power, Sensor, Communication, Database and Visualization), which support the process of acquisition, validation, management and contextual data delivery. To validate the proposed model, a solar-powered IoT hydroponic monitoring system integrated with an Augmented Reality (AR) application was developed based on Situation Awareness Theory. Using the System Usability Scale (SUS), the prototype was assessed by three domain experts. The results achieved an average usability score of 85.83, which reflects excellent usability. The results indicate that the proposed model improves organisation and presentation of environmental information by prioritising contextually relevant data rather than displaying all available sensor readings at once. This enables users to identify important environmental conditions efficiently and supports more effective monitoring. This approach helps to reduce data overload and provides a practical framework that can support the development of IoT-based smart agriculture applications in the future through the integration of anomaly detection techniques and predictive analytics.","物联网（IoT）在水培农业中的广泛使用使得环境条件的持续监测成为可能，并提供实时信息以支持作物管理。然而，传感器数据量的不断增加使监测过程更加复杂，因为用户需要同时解读多个环境参数。现有的水培监测系统侧重于数据采集与可视化，而在减少数据过载的管理与信息传递方面仍然有限。本研究提出一种多层上下文模型，用于管理智能水培监测系统中的环境信息。该模型由五个相互关联的层级（供电、传感器、通信、数据库和可视化）组成，支持数据的采集、验证、管理和上下文传递过程。为验证所提出的模型，基于态势感知理论开发了一套集成了增强现实（AR）应用的太阳能物联网水培监测系统。采用系统可用性量表（SUS）由三位领域专家对原型进行评估，结果获得平均可用性得分85.83，反映出优异的可用性。结果表明，所提出的模型通过优先呈现上下文相关的数据而非一次性显示所有可用的传感器读数，改善了环境信息的组织与呈现方式。这使用户能够高效识别重要的环境条件，并支持更有效的监测。该方法有助于减少数据过载，并提供了一个实用框架，未来可通过集成异常检测技术和预测分析来支持基于物联网的智慧农业应用的开发。","Journal of Information and Communication Technology","2026-10-18T00:00:00Z",70,{"impact":20,"substance":18,"depth":90,"authority":167,"freshness":21,"relevant":22,"comment":343},"提出五层情境化模型，并用太阳能物联网与AR原型验证，SUS 85.83，方法新颖、结论可靠，但属单点原型研究，产业影响有限。",[345],{"name":339,"url":336},[98,29,347,348,349],"水培","增强现实","数据过载",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":41,"version":42,"method":43,"evidence":353},{"candidates":354,"agricultural_evidence":14},[355,359,364],{"code":33,"score":13,"supported":14,"title":356,"abstract":357,"tags":358},[],[29,294,109],[29],{"code":117,"score":219,"supported":14,"title":360,"abstract":361,"tags":363},[],[98,362],"smart agriculture",[98],{"code":60,"score":55,"supported":14,"title":365,"abstract":366,"tags":368},[],[367],"hydroponic",[347],[370,371],"智能水培 物联网 监测系统","增强现实 水培 数据过载","智能水培物联网监测系统-5309","10.32890\u002Fjict2026.25.4.4",{"doi":373,"openalex_id":375,"authors":376,"venue":339,"cited_by_count":147,"oa_url":336,"card":383,"direction":152,"ingested_from":154},"W7222865525",[377,380],{"name":378,"orcid":379},"Ruziana Mohamad Rasli","https:\u002F\u002Forcid.org\u002F0009-0006-7187-513X",{"name":381,"orcid":382},"Sobihatun Nur Abdul Salam","https:\u002F\u002Forcid.org\u002F0000-0002-6751-9187",{"tldr":384,"method":385,"finding":386,"direction":152,"opportunity":387},"提出五层情境模型管理水培IoT环境数据，结合AR应用减少数据过载。","五层情境模型+太阳能IoT水培监测系统+AR应用，SUS量表由3位专家评估。","原型SUS均分85.83，情境化优先展示关键数据可有效缓解数据过载。","可延伸至异常检测与预测分析融合，并开展多用户大规模实证验证。","2026-10-19T23:30:07.917393Z",{"id":390,"title":391,"url":392,"summary":393,"summary_zh":394,"content":8,"source_name":395,"source_url":392,"published_at":396,"category":87,"cover_url":8,"hotness":13,"is_selected":41,"score":397,"score_detail":398,"sources":400,"tags":402,"primary_topic":405,"secondary_topics":410,"classification":412,"search_phrases":428,"slug":431,"view_count":147,"doi":432,"paper":433,"created_at":452},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":93,"substance":19,"depth":90,"authority":48,"freshness":93,"relevant":22,"comment":399},"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。",[401],{"name":395,"url":392},[98,29,172,403,404],"温室大棚","水培蔬菜",{"code":60,"slug":406,"name":407,"path":408,"parent_code":409},"protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[411],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":41,"version":42,"method":43,"evidence":413},{"candidates":414,"agricultural_evidence":14},[415,419,424],{"code":33,"score":19,"supported":14,"title":416,"abstract":417,"tags":418},[109],[29,294,109],[29],{"code":60,"score":92,"supported":14,"title":420,"abstract":422,"tags":423},[421],"greenhouse",[64,421],[],{"code":117,"score":93,"supported":14,"title":425,"abstract":426,"tags":427},[],[172,202],[98,172],[429,430],"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":432,"openalex_id":434,"authors":435,"venue":395,"cited_by_count":147,"oa_url":446,"card":447,"direction":152,"ingested_from":154},"W7222850582",[436,438,441,444],{"name":437,"orcid":8},"Ramadanzah Ramadanzah",{"name":439,"orcid":440},"Dodi Yudo Setyawan","https:\u002F\u002Forcid.org\u002F0000-0003-2205-7617",{"name":442,"orcid":443},"Nurfiana Nurfiana","https:\u002F\u002Forcid.org\u002F0000-0002-6149-0918",{"name":445,"orcid":8},"Refi Arioen","https:\u002F\u002Fjurnal.polsri.ac.id\u002Findex.php\u002Fteknika\u002Farticle\u002Fdownload\u002F12040\u002F4368",{"tldr":448,"method":449,"finding":450,"direction":152,"opportunity":451},"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":454,"title":455,"url":456,"summary":457,"summary_zh":458,"content":8,"source_name":459,"source_url":456,"published_at":460,"category":87,"cover_url":8,"hotness":13,"is_selected":41,"score":461,"score_detail":462,"sources":465,"tags":467,"primary_topic":470,"secondary_topics":471,"classification":472,"search_phrases":502,"slug":505,"view_count":147,"doi":506,"paper":507,"created_at":517},5159,"Integration of IoT And Satellite Data for Real-Time Agricultural Crop Stress Monitoring Using Spatio-Temporal Transformers","https:\u002F\u002Fdoi.org\u002F10.31127\u002Ftuje.1871227","This research work presents an integrated framework for real-time crop stress monitoring that combines ground-based Internet of Things (IoT) sensors and multi-temporal satellite remote sensing data. Due to growing environmental uncertainty and limited resources, the early identification of crop stress is crucial for maximizing agricultural productivity. IoT sensors yield localized, real-time measurements of key variables like soil moisture, temperature, and humidity, whereas satellite images yield wide-area vegetation and thermal indicators (e.g., NDVI, EVI, LST). The solution utilizes a Spatio-Temporal Transformer (ST-Transformer) model to integrate these heterogeneous data streams to make early crop disease and irrigation stress detection. By integrating spatial satellite imagery with temporal IoT sequences, STT leverages attention mechanisms to offer a more holistic analysis, which translates to an impressive accuracy of 88% and a solid F1-Score of 0.855. This dual capacity to concurrently handle spatial and temporal dimensions allows STT to effectively generalize across diverse crop stress categories, providing insights that are both timely and actionable","本研究工作提出了一种集成框架，用于结合地面物联网（IoT）传感器与多时相卫星遥感数据实现作物胁迫的实时监测。由于环境不确定性日益加剧且资源有限，作物胁迫的早期识别对于最大化农业生产力至关重要。物联网传感器可提供土壤湿度、温度和湿度等关键变量的局部实时测量，而卫星图像则可提供广域植被与热指标（如NDVI、EVI、LST）。该方案利用时空Transformer（ST-Transformer）模型整合这些异构数据流，以实现作物病害与灌溉胁迫的早期检测。通过将空间卫星影像与时间物联网序列相结合，STT借助注意力机制提供更全面的分析，从而实现了88%的显著准确率和0.855的稳健F1分数。这种同时处理空间与时间维度的双重能力使STT能够有效泛化于多种作物胁迫类别，提供既及时又可操作的洞察。","Turkish Journal of Engineering","2026-10-16T00:00:00Z",75,{"impact":90,"substance":463,"depth":91,"authority":20,"freshness":21,"relevant":22,"comment":464},21,"该研究将物联网与多时相卫星数据融合，用时空Transformer实现作物胁迫早期识别，方法新颖、指标明确，属智慧农业细分领域有实质增量的技术进展，值得入选。",[466],{"name":459,"url":456},[98,99,29,468,469],"作物胁迫","遥感监测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":41,"version":42,"method":43,"evidence":473},{"candidates":474,"agricultural_evidence":14},[475,479,486,490,494,498],{"code":33,"score":19,"supported":14,"title":476,"abstract":477,"tags":478},[109],[29,294,109],[29],{"code":480,"score":219,"supported":14,"title":481,"abstract":482,"tags":485},"I3",[],[483,484],"遥感","remote sensing",[469],{"code":226,"score":48,"supported":14,"title":487,"abstract":488,"tags":489},[],[229,230],[],{"code":181,"score":48,"supported":14,"title":491,"abstract":492,"tags":493},[],[214,182],[],{"code":117,"score":22,"supported":41,"title":495,"abstract":496,"tags":497},[],[],[98],{"code":122,"score":22,"supported":41,"title":499,"abstract":500,"tags":501},[],[],[99],[503,504],"ST-Transformer 作物胁迫","IoT 卫星 遥感 作物监测","ST-Transformer作物胁迫-5159","10.31127\u002Ftuje.1871227",{"doi":506,"openalex_id":508,"authors":509,"venue":459,"cited_by_count":147,"oa_url":456,"card":512,"direction":152,"ingested_from":154},"W7222730658",[510],{"name":511,"orcid":8},"Sunitha Thankappan",{"tldr":513,"method":514,"finding":515,"direction":152,"opportunity":516},"融合IoT传感器与多时相卫星数据，用时空Transformer实现作物胁迫实时监测。","IoT土壤温湿度数据+卫星NDVI\u002FEVI\u002FLST，构建ST-Transform","模型准确率达88%、F1为0.855，可早期识别病害与灌溉胁迫。","可探索多源异构数据在真实田块尺度上的轻量化部署与跨区域迁移泛化。","2026-10-17T23:30:13.663599Z"]