[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2652":3,"related-2652":114},{"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":44,"search_phrases":94,"slug":97,"view_count":98,"doi":99,"paper":100,"created_at":113},2652,"ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION: TRANSFORMING TEACHING, LEARNING, AND STUDENT ENGAGEMENT","https:\u002F\u002Fdoi.org\u002F10.65725\u002Fijhlt\u002F1\u002F2\u002F001","Efficient irrigation management is essential for sustainable agriculture, particularly in the context of increasing freshwater scarcity and the growing imperative to optimize crop productivity. Conventional irrigation practices rely predominantly on fixed time schedules or manual field assessments, which frequently induce over-irrigation, root-zone nutrient leaching, under-irrigation water stress, and substantial resource inefficiency. This paper proposes an Intelligent Irrigation Management System that integrates Internet of Things (IoT) sensing architectures, multi-parameter environmental telemetry, and supervised machine learning (ML) algorithms to facilitate dynamic, data-driven, and automated irrigation control. The proposed system continuously acquires real-time field data—including soil moisture, ambient temperature, relative humidity, soil temperature, and rainfall—via deployed sensor nodes managed by an ESP32 microcontroller pipeline. The telemetry stream is transmitted through low-power communication channels to a centralized processing engine, where a Random Forest classification model evaluates multidimensional soil-environmental interactions to predict immediate irrigation requirements. The predicted states feed into an automated actuation layer that directly modulates a solenoid-valve and water-pump relay, forming a closed-loop feedback pipeline. Evaluated against traditional threshold-based and schedule-driven approaches, the proposed IoT-ML framework demonstrates superior operational responsiveness, minimizes unnecessary water application, and offers a robust, scalable architectural template for modern precision agriculture.","高效灌溉管理对可持续农业至关重要，尤其是在淡水日益稀缺、优化作物生产力需求不断增长的背景下。传统灌溉实践主要依赖固定时间表或人工田间评估，这常常导致过度灌溉、根区养分淋失、灌溉不足引起的水分胁迫以及严重的资源低效。本文提出了一种智能灌溉管理系统，该系统集成了物联网（IoT）感知架构、多参数环境遥测以及监督式机器学习（ML）算法，以实现动态、数据驱动和自动化的灌溉控制。所提出的系统通过由ESP32微控制器管道管理的部署传感器节点，持续采集实时田间数据——包括土壤湿度、环境温度、相对湿度、土壤温度和降雨量。遥测数据流通过低功耗通信信道传输至集中处理引擎，其中随机森林分类模型评估多维土壤-环境相互作用，以预测即时灌溉需求。预测状态输入自动执行层，直接调节电磁阀和水泵继电器，形成闭环反馈管道。与传统基于阈值和时间表驱动的方法相比，所提出的IoT-ML框架展现出更优的运行响应能力，最大限度地减少了不必要的灌溉用水，并为现代精准农业提供了一种稳健、可扩展的架构模板。",null,"INTERNATIONAL JOURNAL OF HUMANITIES AND LEARNING TECHNOLOGY INNOVATION (IJHLT)","2026-09-15T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,18,17,13,8,1,"论文提出IoT与随机森林融合的闭环智能灌溉系统，方法完整、数据驱动，对节水农业有参考价值，但标题与摘要主题不符需核实。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业物联网","机器学习","智能灌溉","精准农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,58,65,71,79,87],{"code":33,"score":51,"supported":52,"title":53,"abstract":55,"tags":57},15,true,[54],"artificial intelligence",[29,56],"machine learning",[29],{"code":40,"score":13,"supported":52,"title":59,"abstract":60,"tags":64},[],[61,62,63],"物联网","internet of things","IoT",[28],{"code":66,"score":21,"supported":52,"title":67,"abstract":68,"tags":70},"I1",[],[31,69],"precision agriculture",[27,31],{"code":72,"score":73,"supported":52,"title":74,"abstract":75,"tags":78},"H3",7,[],[76,77],"灌溉","irrigation",[30],{"code":80,"score":81,"supported":52,"title":82,"abstract":83,"tags":86},"H2",6,[],[84,85],"土壤","soil",[],{"code":88,"score":81,"supported":52,"title":89,"abstract":90,"tags":93},"H8",[],[91,92],"可持续农业","sustainable agriculture",[],[95,96],"农业物联网 智慧农业 智能灌溉 机器学习","农业物联网 智慧农业","农业物联网智慧农业智能灌溉机器学习-2652",2,"10.65725\u002Fijhlt\u002F1\u002F2\u002F001",{"doi":99,"openalex_id":101,"authors":102,"venue":10,"cited_by_count":105,"oa_url":9,"card":106,"direction":110,"ingested_from":112},"W7213277724",[103],{"name":104,"orcid":9},"M. Rathamani",0,{"tldr":107,"method":108,"finding":109,"direction":110,"opportunity":111},"提出融合物联网传感与随机森林的智能灌溉系统，实现数据驱动的自动灌溉控制。","ESP32传感器节点采集土壤温湿度等数据，随机森林分类预测灌溉需求。","相比传统定时或阈值方法，该系统响应更优、减少不必要灌溉，可扩展性强。","智慧农业 \u002F 农业物联网","可探索多模态数据融合与边缘智能，提升灌溉决策的实时性与泛化能力。","openalex","2026-09-16T23:30:16.194086Z",{"total":81,"page":22,"page_size":81,"items":115},[116,177,267,365,436,513],{"id":117,"title":118,"url":119,"summary":120,"summary_zh":121,"content":9,"source_name":122,"source_url":119,"published_at":123,"category":12,"cover_url":9,"hotness":124,"is_selected":14,"score":125,"score_detail":126,"sources":129,"tags":133,"primary_topic":136,"secondary_topics":137,"classification":139,"search_phrases":156,"slug":159,"view_count":22,"doi":9,"paper":160,"created_at":176},4672,"AI-BASED ENERGY EFFICIENT COMMUNICATION IN IOT SYSTEM","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F309","The rapid growth of the Internet of Things (IoT) has led to a massive increase in the number of connected devices across various domains such as smart homes, healthcare, agriculture, and industrial automation. As these devices are often battery-powered and deployed in remote or large scale environments, energy efficiency has become a critical concern in IoT communication systems. Traditional IoT networks typically operate using fixed transmission power levels, which do not adapt to changing environmental or network conditions, resulting in unnecessary energy consumption and reduced battery life.To address these limitations, there is a growing need for intelligent and adaptive communication mechanisms that can dynamically optimize power usage without compromising reliability. In this context, Artificial Intelligence (AI)-based adaptive transmission power control has emerged as a promising solution. This review paper presents an analysis of AI-driven techniques that enable IoT devices to adjust their transmission power based on real-time conditions and network requirements.The study explores approaches such as machine learning-based prediction models, which utilize parameters like Received Signal Strength Indicator (RSSI), distance, interference levels, and packet loss rates to determine optimal transmission power. Additionally, the role of edge computing is examined, enabling faster decision-making with reduced latency. The integration of Low Power Wide Area Network (LPWAN) technologies is also discussed for supporting long-range and energy-efficient communication.Furthermore, the paper highlights how adaptive power control improves network performance by reducing energy wastage, minimizing interference, and extending device lifetime. These benefits are especially useful in applications like smart cities, precision agriculture, and industrial automation.Despite these advantages, challenges such as implementation complexity, resource constraints, and data privacy concerns remain. The paper also outlines future research directions, including the development of lightweight AI models and improved integration with emerging technologies.In conclusion, AIbased adaptive transmission power control significantly enhances energy efficiency and communication reliability, making it essential for the sustainable development of next- generation IoT systems. It also supports scalable and cost-effective deployment of IoT networks in diverse real- world environments.","物联网（IoT）的快速发展使得智能家居、医疗健康、农业和工业自动化等各个领域的联网设备数量大幅增加。由于这些设备通常由电池供电，且部署在偏远或大规模环境中，能效已成为物联网通信系统中的关键问题。传统物联网网络通常采用固定传输功率运行，无法适应不断变化的环境或网络条件，导致不必要的能耗和电池寿命缩短。为解决这些局限，人们日益需要能够在不影响可靠性的前提下动态优化功率使用的智能自适应通信机制。在此背景下，基于人工智能（AI）的自适应传输功率控制已成为一种有前景的解决方案。本综述论文分析了使物联网设备能够根据实时条件和网络需求调整传输功率的AI驱动技术。研究探讨了基于机器学习的预测模型等方法，这些方法利用接收信号强度指示（RSSI）、距离、干扰水平和丢包率等参数来确定最优传输功率。此外，还考察了边缘计算的作用，其能够以更低延迟实现更快决策。论文还讨论了低功耗广域网（LPWAN）技术的集成，以支持长距离和节能通信。进一步地，本文强调了自适应功率控制如何通过减少能量浪费、最小化干扰和延长设备寿命来改善网络性能。这些优势在智慧城市、精准农业和工业自动化等应用中尤为有用。尽管具有这些优势，实施复杂性、资源约束和数据隐私问题等挑战仍然存在。论文还概述了未来研究方向，包括开发轻量级AI模型以及与新兴技术的改进集成。总之，基于AI的自适应传输功率控制显著提升了能效和通信可靠性，使其成为下一代物联网系统可持续发展的关键。它还支持物联网网络在多样化现实环境中的可扩展且具有成本效益的部署。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",25,47,{"impact":21,"substance":127,"depth":20,"authority":81,"freshness":81,"relevant":22,"comment":128},14,"综述性论文，方法层面有参考价值，但非农业专属突破，且期刊权威性一般，仅作主题聚合素材。",[130,131],{"name":122,"url":119},{"name":122,"url":132},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F310",[27,134,28,31,135],"农业人工智能","低功耗通信",{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},[138],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":140},{"candidates":141,"agricultural_evidence":52},[142,148,152],{"code":40,"score":18,"supported":52,"title":143,"abstract":144,"tags":147},[63],[61,145,62,63,146],"边缘计算","edge computing",[28],{"code":33,"score":13,"supported":52,"title":149,"abstract":150,"tags":151},[],[29,56,54],[134],{"code":66,"score":21,"supported":52,"title":153,"abstract":154,"tags":155},[],[31,69],[27,31],[157,158],"AI 物联网 传输功率控制","LPWAN 精准农业","AI物联网传输功率控制-4672",{"doi":9,"openalex_id":161,"authors":162,"venue":122,"cited_by_count":105,"oa_url":119,"card":171,"direction":110,"ingested_from":112},"W7221414266",[163,165,167,169],{"name":164,"orcid":9},"Shital Chavan",{"name":166,"orcid":9},"Anam Bedrekar",{"name":168,"orcid":9},"Sanika Babar",{"name":170,"orcid":9},"Prof. M. V. Naiknavare",{"tldr":172,"method":173,"finding":174,"direction":110,"opportunity":175},"综述AI自适应发射功率控制提升IoT通信能效，面向智慧农业等场景。","综述机器学习预测模型、边缘计算与LPWAN技术，基于RSSI等参数。","AI自适应功率控制可降低能耗、减少干扰并延长设备寿命，但存在实现复杂与隐私挑战。","可研究面向农田的轻量级AI功率控制模型，结合土壤墒情与作物需水优化能耗。","2026-10-11T23:30:16.397155Z",{"id":178,"title":179,"url":180,"summary":181,"summary_zh":182,"content":9,"source_name":183,"source_url":180,"published_at":184,"category":12,"cover_url":9,"hotness":124,"is_selected":14,"score":185,"score_detail":186,"sources":190,"tags":195,"primary_topic":198,"secondary_topics":199,"classification":207,"search_phrases":251,"slug":254,"view_count":22,"doi":255,"paper":256,"created_at":266},4573,"APPLICATION OF ARTIFICIAL INTELLIGENCE AND AUTOMATION TECHNOLOGIES IN MODERN AGRICULTURAL MACHINERY","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23255982","The rapid development of artificial intelligence (AI), robotics, computer vision, Internet of Things (IoT), satellite navigation, remote sensing, and autonomous control systems is transforming the design and operation of modern agricultural machinery. Agricultural machinery is increasingly evolving from conventional mechanical equipment into intelligent cyber-physical systems capable of sensing field conditions, processing large volumes of data, making operational decisions, and executing agricultural tasks with limited human intervention. This article examines the application of artificial intelligence and automation technologies in tractors, harvesters, sprayers, robotic platforms, unmanned aerial vehicles, and other agricultural machinery. The study is based on a systematic analysis of scientific publications, international institutional reports, and documented agricultural technology adoption data. Particular attention is given to autonomous navigation, machine vision, precision spraying, variable-rate application, crop monitoring, yield prediction, robotic weeding, harvesting, and safety systems. Evidence from the United States demonstrates substantial growth in precision-agriculture adoption: automated guidance systems were used on more than half of the acreage planted to several major crops, while in 2023 guidance autosteering was used by 52 percent of midsize farms and 70 percent of large-scale crop-producing farms. Scientific literature also demonstrates the growing role of machine learning in yield estimation, nitrogen management, disease detection, and robotic perception. However, high acquisition costs, inadequate rural connectivity, data governance issues, cybersecurity, technical complexity, and safety risks remain significant barriers. The study concludes that AI-enabled agricultural machinery can contribute to higher operational efficiency, more precise input application, reduced operator fatigue, improved resource management, and greater agricultural sustainability, provided that technological development is accompanied by appropriate infrastructure, human-capacity development, interoperability standards, and safety regulation.","人工智能（AI）、机器人技术、计算机视觉、物联网（IoT）、卫星导航、遥感及自主控制系统的快速发展，正在深刻改变现代农业机械的设计与运行方式。农业机械正日益从传统机械设备演变为智能信息物理系统，能够感知田间条件、处理海量数据、做出作业决策，并在有限人工干预下执行农业任务。本文探讨了人工智能与自动化技术在拖拉机、联合收割机、喷雾机、机器人平台、无人机及其他农业机械中的应用。研究基于对科学文献、国际机构报告及已记录的农业技术采用数据的系统分析。重点关注自主导航、机器视觉、精准喷雾、变量施用、作物监测、产量预测、机器人除草、收获及安全系统。来自美国的证据表明，精准农业的采用率大幅增长：自动导航系统已应用于多种主要作物一半以上的种植面积，而2023年，52%的中型农场和70%的大型作物生产农场使用了导航自动转向技术。科学文献也表明，机器学习在产量估算、氮素管理、病害检测和机器人感知中的作用日益增强。然而，高昂的购置成本、农村网络连接不足、数据治理问题、网络安全、技术复杂性及安全风险仍是重要障碍。研究结论认为，在技术发展的同时配套适当的基础设施、人力能力建设、互操作性标准和安全监管，人工智能赋能的农业机械能够有助于提高作业效率、实现更精准的投入施用、减轻操作人员疲劳、改善资源管理并增强农业可持续性。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-09T00:00:00Z",81,{"impact":187,"substance":188,"depth":18,"authority":20,"freshness":21,"relevant":22,"comment":189},22,20,"系统梳理AI与自动化在农机领域应用的综述性论文，含美国精准农业采纳率等实证数据，对智慧农业主题聚合有较高参考价值。",[191,192],{"name":183,"url":180},{"name":193,"url":194},"International Bulletin of Engineering and Technology","https:\u002F\u002Finternationalbulletins.com\u002Fintjour\u002Findex.php\u002Fibet\u002Farticle\u002Fview\u002F2468",[27,134,196,28,31,197],"农业机器人","自动驾驶农机",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[200,206],{"code":201,"slug":202,"name":203,"path":204,"parent_code":205},"F5","machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery","F",{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":208},{"candidates":209,"agricultural_evidence":52},[210,216,223,227,234,239,246],{"code":33,"score":18,"supported":52,"title":211,"abstract":212,"tags":215},[54],[29,213,56,54,214],"计算机视觉","computer vision",[134],{"code":201,"score":19,"supported":52,"title":217,"abstract":219,"tags":222},[218],"agricultural machinery",[220,221,218],"农业机械","收割机",[],{"code":40,"score":13,"supported":52,"title":224,"abstract":225,"tags":226},[],[61,62,63],[28],{"code":228,"score":81,"supported":52,"title":229,"abstract":230,"tags":233},"I3",[],[231,232],"遥感","remote sensing",[],{"code":66,"score":235,"supported":52,"title":236,"abstract":237,"tags":238},5,[],[31],[27,31],{"code":240,"score":241,"supported":52,"title":242,"abstract":243,"tags":245},"F4",3,[],[244],"除草",[],{"code":247,"score":22,"supported":14,"title":248,"abstract":249,"tags":250},"F6",[],[],[196],[252,253],"农业机械 人工智能 自动化","精准农业 自动驾驶 农机","农业机械人工智能自动化-4573","10.5281\u002Fzenodo.23255982",{"doi":255,"openalex_id":257,"authors":258,"venue":183,"cited_by_count":105,"oa_url":180,"card":261,"direction":110,"ingested_from":112},"W7221138004",[259],{"name":260,"orcid":9},"Rahimjonov Shoxrux Shuhratjon o'g'li",{"tldr":262,"method":263,"finding":264,"direction":110,"opportunity":265},"系统综述AI与自动化技术在现代农机中的应用现状、效益与障碍。","系统分析科学文献、国际机构报告及美国精准农业采用数据。","AI农机可提升效率与可持续性，但成本、连接性、数据治理与安全仍是主要障碍。","可研究低成本AI农机方案、农村连接与数据治理标准，及人机协同安全监管框架。","2026-10-10T23:30:13.826410Z",{"id":268,"title":269,"url":270,"summary":271,"summary_zh":272,"content":9,"source_name":273,"source_url":270,"published_at":274,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":275,"score_detail":276,"sources":278,"tags":280,"primary_topic":283,"secondary_topics":284,"classification":286,"search_phrases":346,"slug":349,"view_count":21,"doi":350,"paper":351,"created_at":364},3536,"Functional photonic and optoelectronic materials and devices for climate-resilient smart agriculture: a review","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffmats.2026.1943334","The variation in the temperature, precipitation, and extreme events has rendered climate change very threatening to crop production and the overall world food security. The prospects of sustainable and climate resilient agricultural production is brought up with the use of photonics and optoelectronics coupled with smart farming technology. The paper offers an overall foundation of smart agricultural systems that are assisted by photonics and optoelectronics that combine the state-of-the-art optical sensors, Internet of Things (IoT) communications, and machine learning algorithms in the crop and soil monitoring. Due to optical sensing technologies including fiber optic sensors, LiDAR, fluorescence spectroscopy, and hyperspectral imaging, optical sensors can also be used in high resolution and non-destructive measurement of physiological properties of plants, nutrient status, water stress and disease incidence. With the help of optoelectronic devices, it is possible to carry out the accurate signal processing, data collection, automated control of irrigation, fertigation and microclimate regulating systems. When sensor-based information is incorporated with any decision support systems the quality of early stress detection is improved, wastage of resources is minimized and the effect on the environment is minimized. Multispectral and temporal data based on machine learning models enhance forecasting of climate risk, diseases, and pest outbreaks forecasting, and crop yield forecasting. The system will aid in making the contemporary crop production systems more sustainable and resilient in the long-term due to the fact that it will provide scalable and flexible solutions across the agroecological regions.","温度、降水及极端事件的变化使气候变化对作物生产和全球粮食安全构成严重威胁。光子学与光电子学结合智慧农业技术，为实现可持续且气候韧性的农业生产带来了前景。本文提供了由光子学和光电子学辅助的智慧农业系统的总体基础，该系统将先进的光学传感器、物联网（IoT）通信和机器学习算法结合应用于作物与土壤监测。借助光纤传感器、激光雷达（LiDAR）、荧光光谱和高光谱成像等光学传感技术，光学传感器还可用于高分辨率、非破坏性地测量植物生理特性、养分状况、水分胁迫和病害发生情况。借助光电子器件，可以实现精确的信号处理、数据采集以及灌溉、施肥和微气候调节系统的自动控制。当基于传感器的信息与决策支持系统相结合时，早期胁迫检测的质量得以提高，资源浪费降至最低，对环境的影响也降至最小。基于机器学习模型的多光谱和时间序列数据增强了气候风险、病虫害暴发预测以及作物产量预测的能力。该系统将有助于使当代作物生产系统在长期内更具可持续性和韧性，因为它将为各农业生态区域提供可扩展且灵活的解决方案。","Frontiers in Materials","2026-09-25T00:00:00Z",76,{"impact":18,"substance":188,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":277},"系统综述光子与光电子技术在气候韧性智慧农业中的应用，方法覆盖全面、结论可靠，对农业信息化领域有较高参考价值。",[279],{"name":273,"url":270},[27,28,29,281,282],"农业传感器","光谱遥感",{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},[285],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":287},{"candidates":288,"agricultural_evidence":52},[289,295,300,305,312,319,323,327,334,340],{"code":66,"score":290,"supported":52,"title":291,"abstract":293,"tags":294},12,[292],"smart agriculture",[27],[27],{"code":40,"score":296,"supported":52,"title":297,"abstract":298,"tags":299},11,[],[61,62,63],[281,28],{"code":33,"score":13,"supported":52,"title":301,"abstract":302,"tags":304},[],[29,56,303],"decision support",[29],{"code":306,"score":81,"supported":52,"title":307,"abstract":308,"tags":311},"A8",[],[309,310],"粮食安全","food security",[],{"code":313,"score":81,"supported":52,"title":314,"abstract":315,"tags":318},"F3",[],[316,317],"施肥","fertigation",[],{"code":80,"score":81,"supported":52,"title":320,"abstract":321,"tags":322},[],[84,85],[],{"code":72,"score":81,"supported":52,"title":324,"abstract":325,"tags":326},[],[76,77],[],{"code":328,"score":81,"supported":52,"title":329,"abstract":330,"tags":333},"H7",[],[331,332],"气候变化","climate change",[],{"code":228,"score":81,"supported":52,"title":335,"abstract":336,"tags":339},[],[337,338],"multispectral","hyperspectral",[],{"code":341,"score":241,"supported":52,"title":342,"abstract":343,"tags":345},"H6",[],[344],"农业生态",[],[347,348],"光子学 光电材料 智慧农业","光纤传感 高光谱成像 作物监测","光子学光电材料智慧农业-3536","10.3389\u002Ffmats.2026.1943334",{"doi":350,"openalex_id":352,"authors":353,"venue":273,"cited_by_count":105,"oa_url":270,"card":359,"direction":110,"ingested_from":112},"W7214293156",[354,356],{"name":355,"orcid":9},"Karthika Vishnu Priya Kathula",{"name":357,"orcid":358},"Murugesan Mohana Keerthi","https:\u002F\u002Forcid.org\u002F0000-0002-2018-0200",{"tldr":360,"method":361,"finding":362,"direction":110,"opportunity":363},"综述光子与光电子材料器件结合物联网和机器学习，支撑气候韧性智慧农业的作物与土壤监测。","综述光纤传感、LiDAR、荧光光谱、高光谱成像与IoT、机器学习融合方案。","光学传感与光电器件可实现无损高分辨监测，提升早期胁迫检测并减少资源浪费。","可探索低成本光子传感器与轻量ML模型在田间边缘端的集成及跨生态区泛化验证。","2026-09-26T23:30:09.644423Z",{"id":366,"title":367,"url":368,"summary":369,"summary_zh":370,"content":9,"source_name":371,"source_url":368,"published_at":372,"category":12,"cover_url":9,"hotness":124,"is_selected":14,"score":373,"score_detail":374,"sources":378,"tags":383,"primary_topic":385,"secondary_topics":386,"classification":388,"search_phrases":403,"slug":406,"view_count":375,"doi":407,"paper":408,"created_at":435},3358,"One Toolchain, Six Domains: A Multiple-Case, Document-Based Study of Rapid IoT Prototypes Built in a One-Week Immersive Course on a Master’s Program in Applied Artificial Intelligence","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202609.2011.v1","This paper reports a document-based, multiple-case study of six Internet-of-Things (IoT) prototypes designed and simulated during a one-week immersive course, “IoT for Data Intelligence,” delivered in July 2026 within the professional Master in Applied Artificial Intelligence (Maestría en Inteligencia Artificial Aplicada, MNA) at Tecnológico de Monterrey. Six teams followed the same five-day toolchain IoT theory; Oracle Application Express (APEX), SQL, and REST service design; MIT App Inventor; ESP32\u002FWokwi simulation; and generative-AI integration and produced Wokwi-simulated prototypes spanning industrial energy monitoring, agricultural hazard response, residential automation, cardiovascular telemonitoring, industrial waste reduction, and precision agriculture. A fixed coding framework was applied across architecture, AI-integration pattern, platform-level failure modes, security debt, and Sustainable Development Goal alignment, distinguishing findings that the course structure itself prescribes from findings the teams introduced independently. The six cases converged on a shared five-layer architecture and, in a pattern only partly prescribed by the course, on keeping generative AI in an advisory or fail-safe-wrapped role. Deposited results were also compared, for illustrative purposes only, against the course’s internal competency rubric. An observed proposal from a Pontifical Catholic University of Chile’s collaboration is discussed as an informal reference point rather than as evidence for generalization. This paper discusses the implications and limits of this small, single-institution, single-cohort, simulation-only case set.","本文报告了一项基于文档的多案例研究，研究对象为六项物联网（Internet of Things, IoT）原型，这些原型是在2026年7月于蒙特雷理工学院（Tecnológico de Monterrey）应用人工智能专业硕士（Maestría en Inteligencia Artificial Aplicada, MNA）项目内开设的一周沉浸式课程“面向数据智能的物联网”（IoT for Data Intelligence）中设计与仿真的。六个团队遵循了相同的五日工具链——物联网理论；Oracle Application Express（APEX）、SQL与REST服务设计；MIT App Inventor；ESP32\u002FWokwi仿真；以及生成式AI集成——并产出了基于Wokwi仿真的原型，涵盖工业能源监测、农业灾害响应、住宅自动化、心血管远程监护、工业减废和精准农业。研究采用固定编码框架，从架构、AI集成模式、平台级失效模式、安全债务和可持续发展目标对齐五个维度进行分析，并区分了课程结构本身所规定的发现与各团队独立引入的发现。六个案例收敛于一个共享的五层架构，并在一种仅部分由课程规定的模式中，将生成式AI保持在顾问性或故障安全包裹的角色中。所提交的成果还仅出于示例目的与课程内部能力量规进行了比较。智利天主教大学一项合作中提出的方案作为非正式参照点加以讨论，而非作为可推广的证据。本文讨论了这一小型、单一机构、单一批次、仅仿真案例集的启示与局限。","Preprints.org","2026-09-23T00:00:00Z",50,{"impact":81,"substance":17,"depth":51,"authority":375,"freshness":376,"relevant":22,"comment":377},4,9,"单校单期小样本的预印本教学案例研究，含农业物联网原型与生成式AI集成经验，但样本与仿真局限明显，公共价值有限。",[379,380],{"name":371,"url":368},{"name":381,"url":382},"Computers","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fcomputers15100663",[27,134,28,31,384],"农业教育",{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},[387],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":389},{"candidates":390,"agricultural_evidence":52},[391,395,399],{"code":40,"score":18,"supported":52,"title":392,"abstract":393,"tags":394},[63],[61,62,63],[28],{"code":33,"score":290,"supported":52,"title":396,"abstract":397,"tags":398},[54],[54],[134],{"code":66,"score":21,"supported":52,"title":400,"abstract":401,"tags":402},[],[31,69],[27,31],[404,405],"Tecnológico de Monterrey 物联网 课程","ESP32 Wokwi 农业物联网 原型","TecnológicodeMonterrey物联网课程-3358","10.20944\u002Fpreprints202609.2011.v1",{"doi":407,"openalex_id":409,"authors":410,"venue":371,"cited_by_count":105,"oa_url":368,"card":429,"direction":110,"ingested_from":112},"W7214071608",[411,414,417,420,423,426],{"name":412,"orcid":413},"Antonio Carlos Bento","https:\u002F\u002Forcid.org\u002F0000-0001-8264-4771",{"name":415,"orcid":416},"Alexandro Ortiz","https:\u002F\u002Forcid.org\u002F0000-0002-3945-6908",{"name":418,"orcid":419},"Grettel Barceló-Alonso","https:\u002F\u002Forcid.org\u002F0009-0004-3373-6441",{"name":421,"orcid":422},"Jose Reinaldo Silva","https:\u002F\u002Forcid.org\u002F0000-0003-2796-1613",{"name":424,"orcid":425},"Luis E. Falcón-Morales","https:\u002F\u002Forcid.org\u002F0000-0001-8760-5640",{"name":427,"orcid":428},"Sérgio Camacho-León","https:\u002F\u002Forcid.org\u002F0000-0002-5996-9997",{"tldr":430,"method":431,"finding":432,"direction":433,"opportunity":434},"基于六组一周IoT课程原型文档，分析其架构、AI集成与安全模式。","文档多案例研究，固定编码框架，Wokwi仿真与生成式AI集成。","六案例收敛于五层架构，生成式AI多限于建议或故障保护角色。","其他","可探究仿真原型向真实农田部署时，安全债务与AI角色如何演变。","2026-09-24T23:30:13.353443Z",{"id":437,"title":438,"url":439,"summary":440,"summary_zh":441,"content":9,"source_name":442,"source_url":439,"published_at":443,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":444,"sources":447,"tags":449,"primary_topic":450,"secondary_topics":454,"classification":457,"search_phrases":490,"slug":493,"view_count":98,"doi":494,"paper":495,"created_at":512},2926,"An artificial intelligence-based stacking ensemble framework for smart irrigation pump control using IoT sensor data","https:\u002F\u002Fdoi.org\u002F10.11591\u002Fijece.v16i5.pp2652-2663","Efficient water management is essential for sustainable agricultural production, particularly in arid and semi-arid regions where water resources are limited. Machine-learning-based irrigation systems can support automated pump-operation decisions using environmental and soil-related sensor data. However, most previous studies have focused on individual machine-learning models, while the application of stacked ensembles to binary irrigation pump-status prediction remains relatively limited. This study proposes a stacking-based framework for predicting irrigation pump operation in an ON\u002FOFF classification setting. The framework uses environmental and soil-related variables, including soil moisture, temperature, humidity, and a numerical time-related feature. Random forest (RF), extreme gradient boosting (XGBoost), and multilayer perceptron models were trained as base learners, and their out-of-fold (OOF) predictions were combined using a logistic-regression meta-learner. The models were evaluated on a held-out test set using accuracy, precision, recall, specificity, and F1-score. The individual models achieved accuracies ranging from 97.34% to 99.95%, with XGBoost providing the best individual performance. The proposed stacking ensemble achieved 99.97% accuracy, 99.94% precision, 100.00% recall, 99.94% specificity, and a 99.97% F1-score. Compared with XGBoost, the ensemble further refined predictive performance, improving accuracy by 0.02 percentage points and F1-score by 0.01 percentage points while achieving complete elimination of false negatives (100.00% recall). These results demonstrate the potential of stacked ensemble learning to improve binary pump-operation prediction and support data-driven irrigation management in water-limited environments.","高效的水资源管理对可持续农业生产至关重要，尤其是在水资源有限的干旱和半干旱地区。基于机器学习的灌溉系统可以利用环境和土壤相关传感器数据支持自动化水泵运行决策。然而，以往大多数研究集中于单一机器学习模型，而堆叠集成（stacked ensemble）在二元灌溉水泵状态预测中的应用仍相对有限。本研究提出了一种基于堆叠（stacking）的框架，用于在开\u002F关（ON\u002FOFF）分类场景下预测灌溉水泵运行状态。该框架使用环境和土壤相关变量，包括土壤湿度、温度、湿度和一个数值型时间相关特征。随机森林（RF）、极端梯度提升（XGBoost）和多层感知机模型被训练为基学习器，其折外（OOF）预测结果通过逻辑回归元学习器进行组合。模型在留出测试集上使用准确率、精确率、召回率、特异度和F1分数进行评估。单一模型的准确率范围为97.34%至99.95%，其中XGBoost的单一模型性能最佳。所提出的堆叠集成达到了99.97%的准确率、99.94%的精确率、100.00%的召回率、99.94%的特异度和99.97%的F1分数。与XGBoost相比，该集成进一步优化了预测性能，准确率提高了0.02个百分点，F1分数提高了0.01个百分点，同时实现了假阴性的完全消除（100.00%召回率）。这些结果表明，堆叠集成学习在改进二元水泵运行预测和支持水资源受限环境下的数据驱动灌溉管理方面具有潜力。","International Journal of Power Electronics and Drive Systems\u002FInternational Journal of Electrical and Computer Engineering","2026-09-18T00:00:00Z",{"impact":290,"substance":445,"depth":19,"authority":20,"freshness":376,"relevant":22,"comment":446},21,"提出基于IoT传感器数据的堆叠集成模型实现灌溉水泵开关预测，准确率达99.97%，方法新颖、结论可靠，但属实验室验证阶段，产业影响有限。",[448],{"name":442,"url":439},[27,134,29,61,30],{"code":72,"slug":77,"name":451,"path":452,"parent_code":453},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[455,456],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"status":45,"locked":14,"version":46,"method":47,"evidence":458},{"candidates":459,"agricultural_evidence":52},[460,465,469,473,477,482,486],{"code":72,"score":18,"supported":52,"title":461,"abstract":462,"tags":464},[77],[76,463,77],"水资源",[30],{"code":33,"score":20,"supported":52,"title":466,"abstract":467,"tags":468},[54],[29],[134,29],{"code":40,"score":376,"supported":52,"title":470,"abstract":471,"tags":472},[63],[],[61],{"code":80,"score":81,"supported":52,"title":474,"abstract":475,"tags":476},[],[84,85],[],{"code":328,"score":241,"supported":52,"title":478,"abstract":479,"tags":481},[],[480],"干旱",[],{"code":88,"score":241,"supported":52,"title":483,"abstract":484,"tags":485},[],[91],[],{"code":66,"score":22,"supported":14,"title":487,"abstract":488,"tags":489},[],[],[27],[491,492],"智能灌溉 水泵控制 物联网","农业人工智能 智慧农业 智能灌溉 机器学习","智能灌溉水泵控制物联网-2926","10.11591\u002Fijece.v16i5.pp2652-2663",{"doi":494,"openalex_id":496,"authors":497,"venue":442,"cited_by_count":105,"oa_url":439,"card":507,"direction":110,"ingested_from":112},"W7213546497",[498,501,504],{"name":499,"orcid":500},"Sarra Gourari","https:\u002F\u002Forcid.org\u002F0009-0005-1846-0886",{"name":502,"orcid":503},"Wafa Difallah","https:\u002F\u002Forcid.org\u002F0000-0002-6181-0395",{"name":505,"orcid":506},"Belkacem Draoui","https:\u002F\u002Forcid.org\u002F0000-0001-5490-3991",{"tldr":508,"method":509,"finding":510,"direction":110,"opportunity":511},"提出基于堆叠集成学习的灌溉泵ON\u002FOFF预测框架，用物联网传感器数据实现智能灌溉控制。","用RF、XGBoost、MLP作基学习器，逻辑回归元学习器融合OOF预测，基于土","堆叠集成达99.97%准确率和100%召回率，完全消除假阴性，优于单一XGBoost模型。","可探索多模态传感器融合与在线增量学习，提升堆叠集成在不同作物和气候区的泛化与实时部署能力。","2026-09-19T23:30:11.009076Z",{"id":514,"title":515,"url":516,"summary":517,"summary_zh":518,"content":9,"source_name":519,"source_url":516,"published_at":520,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":521,"score_detail":522,"sources":524,"tags":526,"primary_topic":528,"secondary_topics":529,"classification":532,"search_phrases":572,"slug":575,"view_count":105,"doi":576,"paper":577,"created_at":596},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":18,"substance":187,"depth":18,"authority":20,"freshness":376,"relevant":22,"comment":523},"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。",[525],{"name":519,"url":516},[27,134,61,31,527],"气候韧性",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[530,531],{"code":40,"slug":41,"name":42,"path":43,"parent_code":37},{"code":72,"slug":77,"name":451,"path":452,"parent_code":453},{"status":45,"locked":14,"version":46,"method":47,"evidence":533},{"candidates":534,"agricultural_evidence":52},[535,541,545,549,553,557,561,567],{"code":33,"score":18,"supported":52,"title":536,"abstract":537,"tags":540},[54],[538,29,213,56,539,214],"深度学习","deep learning",[134],{"code":66,"score":17,"supported":52,"title":542,"abstract":543,"tags":544},[69],[31,69],[27,31],{"code":40,"score":13,"supported":52,"title":546,"abstract":547,"tags":548},[],[61,145,63,146],[61],{"code":72,"score":376,"supported":52,"title":550,"abstract":551,"tags":552},[],[76,463,77],[],{"code":328,"score":73,"supported":52,"title":554,"abstract":555,"tags":556},[],[331,332],[527],{"code":80,"score":81,"supported":52,"title":558,"abstract":559,"tags":560},[],[84,85],[],{"code":562,"score":241,"supported":52,"title":563,"abstract":564,"tags":566},"A7",[],[565],"间作",[],{"code":313,"score":241,"supported":52,"title":568,"abstract":569,"tags":571},[],[570],"养分管理",[],[573,574],"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":576,"openalex_id":578,"authors":579,"venue":519,"cited_by_count":105,"oa_url":516,"card":590,"direction":110,"ingested_from":112},"W7223121456",[580,583,585,587],{"name":581,"orcid":582},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":584,"orcid":9},"Oluwadare OYEBODE",{"name":586,"orcid":9},"Iretiayo Adelaiye",{"name":588,"orcid":589},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":591,"method":592,"finding":593,"direction":594,"opportunity":595},"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"]