[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3386":3,"related-3386":38},{"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,"search_phrases":32,"slug":35,"view_count":36,"doi":8,"paper":8,"created_at":37},3386,"龙江大地种田有多'聪明'？北大荒闫家岗农场72台套物联网设备构建'空天地人机'一体化数据网络","https:\u002F\u002Fwww.agri.cn\u002Fzx\u002Fxxlb\u002Fhlj\u002F202609\u002Ft20260924_8873814.htm","北大荒集团闫家岗农场拥有72台套物联网设备，24小时不间断采集农业生产数据上传平台，通过AI分析给出生产建议。农场用几个关键词概括：'数字大脑'——巡田无人机已全面替代传统人工作业；'全天候守护'——实现气象、墒情、光照全方位智能监测；'智能装备'——农田水利'双孪生'系统实现远程调水、智能控温、虫情监测等功能。北大荒集团已在20多个农场实施智慧农业示范项目，已在七星、友谊等重点农场落地见效。",null,"[![Image 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龙江大地种田有多“聪明”？在闫家岗农场解锁“丰收密码” \n\n时间：2026-09-24 来源：龙头新闻·黑龙江日报\n\n字号：大 中 小![Image 2](https:\u002F\u002Fwww.agri.cn\u002Fimages\u002Fnxw_printIcon.png) 打印 \n\n分享:[](https:\u002F\u002Fsns.qzone.qq.com\u002Fcgi-bin\u002Fqzshare\u002Fcgi_qzshare_onekey?url=https:\u002F\u002Fwww.agri.cn\u002Fzx\u002Fxxlb\u002Fhlj\u002F202609\u002Ft20260924_8873814.htm&title=%E9%BE%99%E6%B1%9F%E5%A4%A7%E5%9C%B0%E7%A7%8D%E7%94%B0%E6%9C%89%E5%A4%9A%E2%80%9C%E8%81%AA%E6%98%8E%E2%80%9D%EF%BC%9F%E5%9C%A8%E9%97%AB%E5%AE%B6%E5%B2%97%E5%86%9C%E5%9C%BA%E8%A7%A3%E9%94%81%E2%80%9C%E4%B8%B0%E6%94%B6%E5%AF%86%E7%A0%81%E2%80%9D \"qq空间\")[](javascript:void(0); \"微信\")[](http:\u002F\u002Fservice.weibo.com\u002Fshare\u002Fshare.php?url=https:\u002F\u002Fwww.agri.cn\u002Fzx\u002Fxxlb\u002Fhlj\u002F202609\u002Ft20260924_8873814.htm&title=%E9%BE%99%E6%B1%9F%E5%A4%A7%E5%9C%B0%E7%A7%8D%E7%94%B0%E6%9C%89%E5%A4%9A%E2%80%9C%E8%81%AA%E6%98%8E%E2%80%9D%EF%BC%9F%E5%9C%A8%E9%97%AB%E5%AE%B6%E5%B2%97%E5%86%9C%E5%9C%BA%E8%A7%A3%E9%94%81%E2%80%9C%E4%B8%B0%E6%94%B6%E5%AF%86%E7%A0%81%E2%80%9D \"新浪微博\")\n\n稻浪滚滚，锣鼓喧天，23日，在北大荒集团闫家岗农场2026年中国农民丰收节黑龙江省主场活动的现场，各界嘉宾、农民、市民游客与农场职工齐聚，处处洋溢着丰收的喜悦。在闫家岗七彩大道示范田边，几台物联网设备静静矗立，在热闹的节日中默默护航稻田，这是北大荒“聪明”种田的硬件设施，也是北大荒“空天地人机”一体化数据网络的神经末梢。\n\n站在金黄的水稻田边，北大荒集团黑龙江闫家岗农场智慧农业负责人郭腾接受了记者的采访。\n\n“你看到的我们身后的物联网设备，闫家岗农场拥有72台套，它可以实时24小时不间断采集田间的农业生产数据，上传到平台，通过AI分析给出农业生产建议，这些专业的生产建议可以通过手机传递到种植户手里。从凭经验种田到凭数据种粮，让更多种植户达到节本增收的效果。”\n\n闫家岗丰收节现场的无人收割机作业演示环节，不用人坐在驾驶室，机器自己就能在稻田里开展收割；AI病虫害识别技术也将亮相，不靠人工一遍遍下地巡查，借助智能手段就能快速捕捉田间病虫害线索，提前做好防控。\n\n当记者问到闫家岗农场在智慧农业建设方面取得了哪些进展时，郭腾笑着说：“智慧农业是我们闫家岗农场最亮的一张名片，也是我本人投入精力最多的领域。”\n\n他告诉记者，在闫家岗农场层面，用几个关键词来概括：第一个关键词是“数字大脑”。巡田无人机已经全面替代传统人工作业，实现了大面积、高效率的农田巡查。所有数据实时回传至田间监测系统后台，形成智慧农业的“数字大脑”，精准指导从种到收的每一个环节。第二个关键词是“全天候守护”。如今闫家岗的农田，实现了气象、墒情、光照的全方位智能监测，整套智能监测体系24小时不间断值守，气象风险、作物长势、土壤状态全部量化为精准数据，全程护航水稻稳产增收。第三个关键词是“智能装备”。农场建设了农田水利“双孪生”系统，实现远程调水、智能控温、虫情监测等功能，构建农情天空地感知网络。智能温室大棚、无人插秧机、智能灌溉等数字化设备已经成为标配。\n\n从垦区层面来看，北大荒集团的智慧农业建设已经形成了体系化的“北大荒方案”。\n\n记者了解到，北大荒集团已经在20多个农场实施了智慧农业示范项目，成功打造了可复制、可推广的“北大荒未来农场”技术模式，已在七星、友谊等重点农场落地见效，推动传统农耕从“会”种田向“慧”种田全面跨越。北大荒集团联合哈尔滨工业大学、东北农业大学共建了智慧农场技术与系统全国重点实验室，把科研攻关、系统集成、场景验证、规模推广串成一条闭环，让更多新技术从实验室走向田间。（黑龙江日报记者：刘畅）\n\n![Image 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\n即可将网页分享给您的微信好友或朋友圈。","中国农业农村信息网 2026-09-24","2026-09-24T00:00:00Z","报道",10,false,68,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,16,14,11,9,1,"以丰收节现场为切口，具体呈现北大荒闫家岗农场物联网、无人农机与数字大脑的落地成效，属省级媒体实地报道，有细节但偏宣传性，适合主题聚合而非头条精选。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","物联网","北大荒","无人农机",[33,34],"北大荒 闫家岗农场 智慧农业","闫家岗农场 物联网设备","北大荒闫家岗农场智慧农业-3386",0,"2026-09-25T00:09:27.560637Z",{"total":39,"page":22,"page_size":39,"items":40},6,[41,63,96,137,177,220],{"id":42,"title":43,"url":44,"summary":45,"summary_zh":8,"content":8,"source_name":46,"source_url":8,"published_at":47,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":48,"score_detail":49,"sources":53,"tags":55,"search_phrases":58,"slug":61,"view_count":36,"doi":8,"paper":8,"created_at":62},3311,"丰收节看龙江上演科技种田大戏——北大荒农场+科研院所+信息公司三方共建模式落地","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7687082026498818595\u002F","在2026年中国农民丰收节黑龙江主场活动现场，田间地头里矗立着一排排智能设备，这些田野间的数字哨兵24小时守着黑土地。北大荒探索形成农场提供应用场景、科研院所攻坚核心技术、信息公司负责成果转化的三方共建模式，将实验室里的科研成果转化为田间地头好用、实用的数字化生产工具。田间这些看似普通的监测终端，正是北大荒空天地人机一体化数据网络的神经末梢。无人收割机将带来作业演示不用人坐在驾驶室，机器自己就能在金黄稻田里开展收割；AI病虫害识别技术也将亮相不靠人工一遍遍下地巡查，借助智能手段就能快速捕捉田间病虫害线索，提前做好防控。","哈尔滨新闻网 2026年09月19日","2026-09-19T03:24:00Z",55,{"impact":17,"substance":19,"depth":50,"authority":39,"freshness":51,"relevant":22,"comment":52},13,4,"省级丰收节现场报道，三方共建模式与空天地人机数据网络有实质信息，但属地方媒体宣传性报道且已过时效，可作主题聚合素材而非每日精选。",[54],{"name":46,"url":44},[27,28,56,57,31],"成果转化","空天地一体化",[59,60],"北大荒 三方共建 智慧农业","无人收割机 空天地人机","北大荒三方共建智慧农业-3311","2026-09-24T00:04:00.159759Z",{"id":64,"title":65,"url":66,"summary":67,"summary_zh":8,"content":8,"source_name":68,"source_url":66,"published_at":69,"category":70,"cover_url":8,"hotness":13,"is_selected":14,"score":71,"score_detail":72,"sources":75,"tags":77,"search_phrases":80,"slug":83,"view_count":36,"doi":84,"paper":85,"created_at":95},3152,"An integrated IoT and machine learning framework for maize yield prediction and smart agriculture in Tanzania","https:\u002F\u002Fdoi.org\u002F10.1080\u002F23311932.2026.2725400","An integrated IoT and machine learning framework for maize yield prediction and smart agriculture in Tanzania。Cogent Food & Agriculture","Cogent Food & Agriculture","2026-09-22T00:00:00Z","论文",69,{"impact":73,"substance":17,"depth":18,"authority":50,"freshness":13,"relevant":22,"comment":74},12,"论文提出物联网与机器学习融合的玉米产量预测框架，方法有新意但属区域性应用研究，影响力有限，时效性强。",[76],{"name":68,"url":66},[27,28,78,29,79],"产量预测","玉米",[81,82],"坦桑尼亚 玉米 产量预测","物联网 机器学习 智慧农业","坦桑尼亚玉米产量预测-3152","10.1080\u002F23311932.2026.2725400",{"doi":84,"openalex_id":86,"authors":87,"venue":68,"cited_by_count":36,"oa_url":66,"card":8,"direction":93,"ingested_from":94},"W7213978365",[88,91],{"name":89,"orcid":90},"Alcardo Alex Barakabitze","https:\u002F\u002Forcid.org\u002F0000-0001-8960-8415",{"name":92,"orcid":8},"Yasinta Nzogera","智慧农业 \u002F 农业物联网","openalex","2026-09-22T23:30:10.178097Z",{"id":97,"title":98,"url":99,"summary":100,"summary_zh":101,"content":8,"source_name":102,"source_url":99,"published_at":103,"category":70,"cover_url":8,"hotness":13,"is_selected":14,"score":104,"score_detail":105,"sources":109,"tags":111,"search_phrases":114,"slug":117,"view_count":36,"doi":118,"paper":119,"created_at":136},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",72,{"impact":73,"substance":106,"depth":107,"authority":50,"freshness":21,"relevant":22,"comment":108},21,17,"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。",[110],{"name":102,"url":99},[27,28,112,29,113],"机器学习","智能灌溉",[115,116],"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":118,"openalex_id":120,"authors":121,"venue":102,"cited_by_count":36,"oa_url":99,"card":131,"direction":93,"ingested_from":94},"W7213546497",[122,125,128],{"name":123,"orcid":124},"Sarra Gourari","https:\u002F\u002Forcid.org\u002F0009-0005-1846-0886",{"name":126,"orcid":127},"Wafa Difallah","https:\u002F\u002Forcid.org\u002F0000-0002-6181-0395",{"name":129,"orcid":130},"Belkacem Draoui","https:\u002F\u002Forcid.org\u002F0000-0001-5490-3991",{"tldr":132,"method":133,"finding":134,"direction":93,"opportunity":135},"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":138,"title":139,"url":140,"summary":141,"summary_zh":142,"content":8,"source_name":143,"source_url":140,"published_at":144,"category":70,"cover_url":8,"hotness":145,"is_selected":14,"score":146,"score_detail":147,"sources":150,"tags":156,"search_phrases":159,"slug":162,"view_count":36,"doi":163,"paper":164,"created_at":176},2865,"AI-ENABLED WIRELESS POWER TRANSFER ARCHITECTURES FOR SUSTAINABLE IoT ECOSYSTEMS","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22809198","Abstract The proliferation of Internet of Things (IoT) devices in smart cities, industrial automation, and healthcare monitoring has created an unprecedented demand for sustainable energy solutions. Traditional battery-powered IoT deployments face significant challenges including limited operational lifespan, environmental hazards from battery disposal, and high maintenance costs associated with manual replacement. This study presents a novel AI-enabled wireless power transfer (WPT) architecture that leverages machine learning algorithms to optimize energy delivery, predict device energy demands, and autonomously manage power distribution across large-scale IoT ecosystems. We propose a three-tier architecture comprising (1) an intelligent RF energy harvesting layer with adaptive rectenna arrays, (2) a reinforcement learning-based power allocation engine, and (3) a cloud-native energy orchestration platform. Through extensive simulation and prototype validation across three distinct IoT scenarios smart agriculture, industrial asset monitoring, and wearable health networks we demonstrate that the proposed system achieves 78.4% energy transfer efficiency (a 34% improvement over conventional directional WPT), reduces network energy waste by 62%, and extends device operational lifespan by 4.7× compared to battery-dependent counterparts. Our findings establish that AI-driven dynamic optimization of WPT parameters including beam-forming angles, transmission power, and duty cycling enables scalable, sustainable IoT deployments that were previously infeasible. This research contributes to the emerging field of intelligent energy harvesting networks and provides a replicable framework for next-generation green IoT infrastructure. Keywords: wireless power transfer, Internet of Things, machine learning, energy harvesting, sustainable computing, reinforcement learning, smart cities, green communication. References Bi, S., Zeng, Y., & Zhang, R. (2019). Wireless powered communication networks: Techniques, applications, and future directions. IEEE Communications Surveys & Tutorials, 21(2), 1324–1353. https:\u002F\u002Fdoi.org\u002F10.1109\u002FCOMST.2018.2881084 Gartner. (2023). Forecast: Internet of Things—Endpoints and associated services, worldwide, 2023. Gartner Research. Kim, S., Park, J., & Lee, K. (2023). AI-driven optimization of RF energy harvesting for IoT devices using deep reinforcement learning. IEEE Internet of Things Journal, 10(4), 3124–3138. https:\u002F\u002Fdoi.org\u002F10.1109\u002FJIOT.2023.3245678 Ku, M.-L., Li, W., Chen, Y., & Liu, K. J. R. (2016). Advances in energy harvesting communications: Past, present, and future challenges. IEEE Communications Surveys & Tutorials, 18(2), 1384–1412. https:\u002F\u002Fdoi.org\u002F10.1109\u002FCOMST.2015.2497328 Lu, X., Wang, P., Niyato, D., Kim, D. I., & Han, Z. (2021). Wireless charger networking for mobile devices: Fundamentals, standards, and applications. IEEE Wireless Communications, 22(2), 32–41. https:\u002F\u002Fdoi.org\u002F10.1109\u002FMWC.2015.7091061 R Core Team. (2023). R: A language and environment for statistical computing (Version 4.3.1) [Computer software]. R Foundation for Statistical Computing. https:\u002F\u002Fwww.R-project.org\u002F Statista. (2023). Number of Internet of Things (IoT) connected devices worldwide from 2019 to 2030. https:\u002F\u002Fwww.statista.com\u002Fstatistics\u002F1183457\u002Fiot-connected-devices-worldwide\u002F Tran, N. H., Hoang, D. T., Niyato, D., Nguyen, C. M., & Han, Z. (2021). The roadmap to 6G: AI-empowered wireless networks. IEEE Communications Magazine, 59(1), 112–117. https:\u002F\u002Fdoi.org\u002F10.1109\u002FMCOM.001.2000406 United Nations Environment Programme. (2022). Global e-waste monitor 2022: Electronic waste management in the circular economy. United Nations Publications. Zhang, J., Guo, H., & Liu, H. (2022). Intelligent reflecting surface aided wireless power transfer for IoT devices. IEEE Transactions on Communications, 70(5), 3381–3396. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTCOMM.2022.3156789 How to Cite Lucky Joseph, O., & Osaremwinda, O. (2026). AI-ENABLED WIRELESS POWER TRANSFER ARCHITECTURES FOR SUSTAINABLE IoT ECOSYSTEMS. GPH-International Journal of Computer Science and Engineering, 9(1), 176-188. https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22809199","摘要 物联网（IoT）设备在智慧城市、工业自动化和医疗健康监测中的激增，催生了对可持续能源解决方案前所未有的需求。传统的电池供电物联网部署面临诸多重大挑战，包括运行寿命有限、电池处置带来的环境危害，以及人工更换电池所产生的高昂维护成本。本研究提出了一种新颖的人工智能赋能无线能量传输（WPT）架构，利用机器学习算法优化能量传输、预测设备能量需求，并在大规模物联网生态系统中自主管理功率分配。我们提出了一种三层架构，包括：（1）具有自适应整流天线阵列的智能射频能量收集层；（2）基于强化学习的功率分配引擎；（3）云原生能量编排平台。通过在三种不同物联网场景——智慧农业、工业资产监测和可穿戴健康网络——中进行大量仿真和原型验证，我们证明所提出的系统实现了78.4%的能量传输效率（较传统定向WPT提升34%），将网络能量浪费降低了62%，并将设备运行寿命较依赖电池的同类设备延长了4.7倍。我们的研究结果表明，对WPT参数（包括波束成形角度、发射功率和占空比）进行人工智能驱动的动态优化，能够实现此前不可行的大规模、可持续物联网部署。本研究为智能能量收集网络这一新兴领域作出了贡献，并为下一代绿色物联网基础设施提供了可复制的框架。关键词：无线能量传输，物联网，机器学习，能量收集，可持续计算，强化学习，智慧城市，绿色通信。参考文献 Bi, S., Zeng, Y., & Zhang, R. (2019). Wireless powered communication networks: Techniques, applications, and future directions. IEEE Communications Surveys & Tutorials, 21(2), 1324–1353. https:\u002F\u002Fdoi.org\u002F10.1109\u002FCOMST.2018.2881084 Gartner. (2023). Forecast: Internet of Things—Endpoints and associated services, worldwide, 2023. Gartner Research. Kim, S., Park, J., & Lee, K. (2023). AI-driven optimization of RF energy harvesting for IoT devices using deep reinforcement learning. IEEE Internet of Things Journal, 10(4), 3124–3138. https:\u002F\u002Fdoi.org\u002F10.1109\u002FJIOT.2023","Zenodo (CERN European Organization for Nuclear Research)","2026-09-17T00:00:00Z",40,77,{"impact":17,"substance":148,"depth":17,"authority":13,"freshness":21,"relevant":22,"comment":149},22,"论文提出AI驱动的无线能量传输三层架构，在智慧农业等场景验证能效提升34%、设备寿命延长4.7倍，对农业物联网可持续供电有参考价值，但属仿真与原型验证阶段，产业落地尚早。",[151,152,154],{"name":143,"url":140},{"name":143,"url":153},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22809199",{"name":143,"url":155},"https:\u002F\u002Fwww.gphjournal.org\u002Findex.php\u002Fcse\u002Farticle\u002Fview\u002F2581",[27,28,29,157,158],"无线能量传输","绿色通信",[160,161],"AI 无线能量传输 物联网","智能农业 能量采集 可持续","AI无线能量传输物联网-2865","10.5281\u002Fzenodo.22809198",{"doi":163,"openalex_id":165,"authors":166,"venue":143,"cited_by_count":36,"oa_url":140,"card":171,"direction":93,"ingested_from":94},"W7213495899",[167,169],{"name":168,"orcid":8},"Lucky Joseph Ogbogbo",{"name":170,"orcid":8},"Osaremwinda OMOROGIUWA",{"tldr":172,"method":173,"finding":174,"direction":93,"opportunity":175},"提出AI驱动的无线供电三层架构，优化大规模IoT设备能量传输与分配。","强化学习功率分配、自适应整流天线阵列与云原生编排平台仿真验证。","能量传输效率达78.4%，浪费减少62%，设备寿命延长4.7倍。","可将该WPT架构落地农田传感器网络，研究作物环境下的能量预测与波束自适应优化。","2026-09-18T23:30:08.902438Z",{"id":178,"title":179,"url":180,"summary":181,"summary_zh":182,"content":8,"source_name":183,"source_url":180,"published_at":184,"category":70,"cover_url":8,"hotness":13,"is_selected":14,"score":185,"score_detail":186,"sources":190,"tags":192,"search_phrases":195,"slug":198,"view_count":36,"doi":199,"paper":200,"created_at":219},2771,"Edge-AI Based Smart Pet Monitoring Framework: A Rabbit Case Study","https:\u002F\u002Fdoi.org\u002F10.64643\u002Fijirt.208534-459","Continuous Monitoring of animals is hard when the people who take care of them are not around.Changes in how they are eaten, how much they drink, the way they stand and what they do during the day can show if their health is changing or if their normal routine is different.This paper talks about an Edge AI-based Smart Pet Monitoring Framework.The goal is watching the pet's behaviour and the environment around them in time.The system keeps privacy in mind.It uses computer vision, IoT, sensors and computing that happens close to the data not in the cloud This reduces the need for internet access.A Raspberry Pi serves as the edge device running a Python and OpenCV image processing chain that uses a YOLOv9 detector to spot feeding, drinking, posture and activity states.Temperature, humidity, ultrasonic distance and water-level sensors give environment data while an Arduino control layer handles alerts using an LCD, buzzer, LEDs and a servo.Local inference gives real-time processing and improves privacy, data ownership and response time.We tested than 200 images in a controlled setting and found it works but the test is still early.This framework offers a flexible base for smart pet monitoring, which could help at home in shelters and, for vets once more tests are done.","当照顾动物的人不在身边时，对动物进行持续监测是很困难的。它们进食方式、饮水量、站立姿态以及白天活动的变化，可以反映其健康状况是否发生变化或日常规律是否出现异常。本文讨论了一种基于边缘人工智能的智能宠物监测框架。其目标是及时观察宠物的行为及其周围环境。该系统注重隐私保护。它使用计算机视觉、物联网、传感器以及靠近数据端而非云端进行的计算，这减少了对互联网接入的需求。树莓派作为边缘设备，运行Python和OpenCV图像处理流程，并使用YOLOv9检测器识别进食、饮水、姿态和活动状态。温度、湿度、超声波距离和水位传感器提供环境数据，而Arduino控制层通过LCD、蜂鸣器、LED和舵机处理警报。本地推理实现了实时处理，并改善了隐私、数据所有权和响应时间。我们在受控环境中测试了200多张图像，发现系统可以工作，但测试仍处于早期阶段。该框架为智能宠物监测提供了一个灵活的基础，在完成更多测试后，可能有助于家庭、收容所以及兽医使用。","International Journal of Innovative Research in Technology","2026-09-16T00:00:00Z",49,{"impact":187,"substance":19,"depth":50,"authority":188,"freshness":21,"relevant":22,"comment":189},8,5,"边缘AI宠物监测框架，与农业信息化关联偏弱且测试样本仅200张，属早期探索性论文，不宜进入每日精选。",[191],{"name":183,"url":180},[27,28,193,29,194],"边缘计算","动物监测",[196,197],"农业人工智能 动物监测 智慧农业 边缘计算","农业人工智能 动物监测","农业人工智能动物监测智慧农业边缘计算-2771","10.64643\u002Fijirt.208534-459",{"doi":199,"openalex_id":201,"authors":202,"venue":183,"cited_by_count":36,"oa_url":213,"card":214,"direction":93,"ingested_from":94},"W7213273522",[203,205,207,209,211],{"name":204,"orcid":8},"Vinit Masale",{"name":206,"orcid":8},"Suyog Mamankar",{"name":208,"orcid":8},"Prathamesh Sawant",{"name":210,"orcid":8},"Mhaboob Ali",{"name":212,"orcid":8},"Prof. Jyoti Shrote","https:\u002F\u002Fijirt.org\u002Fpublishedpaper\u002FIJIRT208534_PAPER.pdf",{"tldr":215,"method":216,"finding":217,"direction":93,"opportunity":218},"提出基于边缘AI的宠物监测框架，用树莓派和YOLOv9识别兔子行为与环境。","树莓派边缘计算、YOLOv9、OpenCV、Arduino及温湿度超声波传感器。","200张图像测试验证了框架可行性，但需更多测试才能实际应用。","可扩展至畜禽行为健康监测，解决边缘设备算力与多目标识别精度问题。","2026-09-17T23:30:10.728310Z",{"id":221,"title":222,"url":223,"summary":224,"summary_zh":225,"content":8,"source_name":226,"source_url":223,"published_at":227,"category":70,"cover_url":8,"hotness":13,"is_selected":14,"score":228,"score_detail":229,"sources":231,"tags":233,"search_phrases":236,"slug":239,"view_count":36,"doi":240,"paper":241,"created_at":262},2646,"IoT-RiceMobileNet: An improved lightweight MobileNetV2 Model for real-time multi-class rice disease detection using IoT","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0356383","Early and real-time detection of rice leaf diseases (RLD) poses a significant challenge for farmers, especially in rural regions with limited access to advanced technology. Conventional deep learning models often require substantial computational resources, rendering them impractical for deployment on mobile or edge devices commonly used in agricultural environments. Although models such as ResNet50, VGG16, and InceptionV3 can achieve high accuracy, they are computationally expensive and may be less suitable for real-time deployment in smart agricultural systems. Furthermore, many existing studies rely on limited datasets, which can restrict model generalizability under diverse real-world conditions. Although transfer learning can improve classification performance, developing lightweight models suitable for real-time IoT deployment remains challenging. To address these limitations, we curated a hybrid dataset of 7,092 rice leaf images by combining self-collected and Kaggle samples and proposed IoT-RiceMobileNet, a lightweight improved MobileNetV2-based model that can be effectively integrated into our developed IoT system. The proposed model outperformed transfer learning and deep learning baseline models, achieving 99.19% test accuracy and 99.20% precision while maintaining low computational complexity. We further confirmed model stability using stratified 5-fold and 10-fold cross-validation on both the constructed RLD and multi-source datasets, achieving mean accuracies of 98.04% and 98.13% on the constructed RLD dataset and 98.11% and 98.58% on the multi-source dataset, respectively. Moreover, our models compact size and 85.17 FPS inference speed support real-time deployment. Finally, the model was integrated into an IoT-enabled mobile, web, and cloud-based inference framework, demonstrating its practical potential for scalable rice disease detection in smart agriculture.","水稻叶片病害（RLD）的早期和实时检测对农民而言是一项重大挑战，尤其是在难以获取先进技术的农村地区。传统的深度学习模型通常需要大量计算资源，使其难以在实际农业环境中常用的移动或边缘设备上部署。尽管ResNet50、VGG16和InceptionV3等模型可以达到较高精度，但其计算成本高昂，可能不太适合在智慧农业系统中实时部署。此外，许多现有研究依赖有限的数据集，这可能限制模型在多样化真实条件下的泛化能力。尽管迁移学习可以提升分类性能，但开发适合实时物联网部署的轻量级模型仍具有挑战性。为解决这些局限，我们通过整合自采集样本和Kaggle样本，构建了一个包含7092张水稻叶片图像的混合数据集，并提出了IoT-RiceMobileNet，这是一种基于MobileNetV2改进的轻量级模型，可有效集成到我们开发的物联网系统中。所提出的模型优于迁移学习和深度学习基线模型，在保持低计算复杂度的同时，实现了99.19%的测试准确率和99.20%的精确率。我们进一步通过在构建的RLD数据集和多源数据集上采用分层5折和10折交叉验证确认了模型稳定性，在构建的RLD数据集上分别达到98.04%和98.13%的平均准确率，在多源数据集上分别达到98.11%和98.58%的平均准确率。此外，该模型紧凑的规模和85.17 FPS的推理速度支持实时部署。最后，该模型被集成到一个支持物联网的移动端、网页端和云端推理框架中，展示了其在智慧农业中可扩展水稻病害检测的实际潜力。","PLoS ONE","2026-09-15T00:00:00Z",80,{"impact":17,"substance":148,"depth":17,"authority":50,"freshness":21,"relevant":22,"comment":230},"提出轻量级MobileNetV2水稻病害检测模型并集成物联网推理框架，实测精度与推理速度俱佳，方法新颖、数据规模可观，对边缘端智慧农业落地有参考价值。",[232],{"name":226,"url":223},[27,28,29,234,235],"水稻病害","轻量化模型",[237,238],"农业人工智能 轻量化模型 智慧农业 水稻病害","农业人工智能 轻量化模型","农业人工智能轻量化模型智慧农业水稻病害-2646","10.1371\u002Fjournal.pone.0356383",{"doi":240,"openalex_id":242,"authors":243,"venue":226,"cited_by_count":36,"oa_url":223,"card":257,"direction":93,"ingested_from":94},"W7213328321",[244,246,248,250,252,254],{"name":245,"orcid":8},"Khawja Imran Masud",{"name":247,"orcid":8},"Md Yasin Zihad",{"name":249,"orcid":8},"Mehedi Hasan Shuvo",{"name":251,"orcid":8},"Mst Raonik Jannat",{"name":253,"orcid":8},"Jia Uddin",{"name":255,"orcid":256},"Sahara Ali","https:\u002F\u002Forcid.org\u002F0000-0002-8578-948X",{"tldr":258,"method":259,"finding":260,"direction":93,"opportunity":261},"提出轻量级IoT-RiceMobileNet模型，实现水稻病害实时多类检测。","改进MobileNetV2，融合自采与Kaggle共7092张图像，IoT端部署","测试准确率99.19%，推理速度85.17 FPS，适合边缘设备实时检测。","可探索多作物病害泛化、田间复杂光照下轻量模型鲁棒性及边缘联邦学习。","2026-09-16T23:30:12.197386Z"]