[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2865":3,"related-2865":54},{"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":23,"tags":27,"search_phrases":33,"slug":36,"view_count":37,"doi":38,"paper":39,"created_at":53},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",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-17T00:00:00Z","论文",25,false,77,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,10,9,1,"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。",[24,25],{"name":10,"url":6},{"name":10,"url":26},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22809199",[28,29,30,31,32],"智慧农业","农业人工智能","物联网","无线能量传输","绿色通信",[34,35],"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",0,"10.5281\u002Fzenodo.22809198",{"doi":38,"openalex_id":40,"authors":41,"venue":10,"cited_by_count":37,"oa_url":6,"card":46,"direction":50,"ingested_from":52},"W7213495899",[42,44],{"name":43,"orcid":9},"Lucky Joseph Ogbogbo",{"name":45,"orcid":9},"Osaremwinda OMOROGIUWA",{"tldr":47,"method":48,"finding":49,"direction":50,"opportunity":51},"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。","智慧农业 \u002F 农业物联网","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。","openalex","2026-09-18T23:30:08.902438Z",{"total":55,"page":21,"page_size":55,"items":56},6,[57,102,145,195,234,273],{"id":58,"title":59,"url":60,"summary":61,"summary_zh":62,"content":9,"source_name":63,"source_url":60,"published_at":64,"category":12,"cover_url":9,"hotness":19,"is_selected":14,"score":65,"score_detail":66,"sources":72,"tags":74,"search_phrases":77,"slug":80,"view_count":37,"doi":81,"paper":82,"created_at":101},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":67,"substance":68,"depth":69,"authority":70,"freshness":20,"relevant":21,"comment":71},8,14,13,5,"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。",[73],{"name":63,"url":60},[28,29,75,30,76],"边缘计算","动物监测",[78,79],"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":81,"openalex_id":83,"authors":84,"venue":63,"cited_by_count":37,"oa_url":95,"card":96,"direction":50,"ingested_from":52},"W7213273522",[85,87,89,91,93],{"name":86,"orcid":9},"Vinit Masale",{"name":88,"orcid":9},"Suyog Mamankar",{"name":90,"orcid":9},"Prathamesh Sawant",{"name":92,"orcid":9},"Mhaboob Ali",{"name":94,"orcid":9},"Prof. Jyoti Shrote","https:\u002F\u002Fijirt.org\u002Fpublishedpaper\u002FIJIRT208534_PAPER.pdf",{"tldr":97,"method":98,"finding":99,"direction":50,"opportunity":100},"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":103,"title":104,"url":105,"summary":106,"summary_zh":107,"content":9,"source_name":108,"source_url":105,"published_at":109,"category":12,"cover_url":9,"hotness":19,"is_selected":14,"score":110,"score_detail":111,"sources":113,"tags":115,"search_phrases":118,"slug":121,"view_count":37,"doi":122,"paper":123,"created_at":144},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":18,"depth":17,"authority":69,"freshness":20,"relevant":21,"comment":112},"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。",[114],{"name":108,"url":105},[28,29,30,116,117],"水稻病害","轻量化模型",[119,120],"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":122,"openalex_id":124,"authors":125,"venue":108,"cited_by_count":37,"oa_url":105,"card":139,"direction":50,"ingested_from":52},"W7213328321",[126,128,130,132,134,136],{"name":127,"orcid":9},"Khawja Imran Masud",{"name":129,"orcid":9},"Md Yasin Zihad",{"name":131,"orcid":9},"Mehedi Hasan Shuvo",{"name":133,"orcid":9},"Mst Raonik Jannat",{"name":135,"orcid":9},"Jia Uddin",{"name":137,"orcid":138},"Sahara Ali","https:\u002F\u002Forcid.org\u002F0000-0002-8578-948X",{"tldr":140,"method":141,"finding":142,"direction":50,"opportunity":143},"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",{"id":146,"title":147,"url":148,"summary":149,"summary_zh":150,"content":9,"source_name":151,"source_url":148,"published_at":152,"category":12,"cover_url":9,"hotness":19,"is_selected":14,"score":153,"score_detail":154,"sources":159,"tags":161,"search_phrases":164,"slug":167,"view_count":37,"doi":168,"paper":169,"created_at":194},2302,"AgriSphere: A Smart Agriculture Framework Integrating IoT and Artificial Intelligence for Adaptive Crop Selection","https:\u002F\u002Fdoi.org\u002F10.2174\u002F0118743315503187260909160504","Introduction Rapid climate change, soil degradation, and changing environmental conditions make crop selection difficult for farmers. This study proposes an IoT- and AI-based framework to recommend suitable crops using current soil conditions and future weather forecasts. It also identifies the key environmental factors influencing crop selection. Methods A three-layer architecture was designed with data collection, communication, and data processing modules. Real-time data on nitrogen, phosphorus, potassium, pH, temperature, and humidity were collected through IoT sensors and combined with rainfall and historical agricultural data. A dataset containing multiple environmental features and 22 crop classes was used for model development. Machine learning and deep learning methods, including Random Forest, XGBoost, K-Nearest Neighbours (KNN), Support Vector Machines (SVM), Convolutional Neural Networks (CNN), Decision Trees (DT), Deep Neural Networks (DNN), and Long Short-Term Memory (LSTM), were applied for classification and forecasting. Performance was evaluated using accuracy, F1-Score, MAE, RMSE, and R 2 . Pareto analysis was also performed to identify the most influential parameters. Results Random Forest and CNN achieved the highest classification accuracy of 99.54% with an F1-Score of 0.995, while XGBoost also performed strongly with 99.32% accuracy. Regression analysis showed that ensemble models outperformed linear models. Pareto analysis revealed that rainfall, humidity, and potassium were the most influential factors in crop recommendation. In a real-time case study, the framework recommended rice as the most suitable crop for the given input conditions. Discussion The results show that integrating IoT sensing with AI-based forecasting supports proactive crop planning before sowing and improves sustainable farming decisions under changing climate conditions. Conclusion The proposed framework effectively combines real-time monitoring, predictive analytics, and intelligent crop recommendation, offering a practical foundation for scalable precision agriculture systems.","引言 快速的气候变化、土壤退化以及不断变化的环境条件使农民难以进行作物选择。本研究提出了一种基于物联网（IoT）和人工智能（AI）的框架，利用当前土壤条件和未来天气预报来推荐适宜的作物。研究还识别了影响作物选择的关键环境因素。方法 设计了一个三层架构，包括数据采集、通信和数据处理模块。通过物联网传感器采集氮、磷、钾、pH值、温度和湿度的实时数据，并结合降雨量和历史农业数据。使用包含多个环境特征和22种作物类别的数据集进行模型开发。应用机器学习和深度学习方法进行分类和预测，包括随机森林（Random Forest）、XGBoost、K近邻（KNN）、支持向量机（SVM）、卷积神经网络（CNN）、决策树（DT）、深度神经网络（DNN）和长短期记忆网络（LSTM）。采用准确率、F1分数、MAE、RMSE和R²评估性能。同时进行帕累托分析以识别最具影响力的参数。结果 随机森林和CNN取得了最高的分类准确率99.54%，F1分数为0.995，XGBoost也表现强劲，准确率为99.32%。回归分析表明，集成模型优于线性模型。帕累托分析显示，降雨量、湿度和钾是作物推荐中影响最大的因素。在实时案例研究中，该框架推荐水稻为给定输入条件下最适宜的作物。讨论 结果表明，将物联网感知与基于人工智能的预测相结合，有助于在播种前进行主动的作物规划，并在气候变化条件下改善可持续农业决策。结论 所提出的框架有效结合了实时监测、预测分析和智能作物推荐，为可扩展的精准农业系统提供了实用基础。","The Open Agriculture Journal","2026-09-11T00:00:00Z",76,{"impact":17,"substance":155,"depth":156,"authority":157,"freshness":67,"relevant":21,"comment":158},21,17,12,"该论文提出IoT与AI融合的智能选种框架，多模型对比与实时案例验证充分，方法新颖且结论可靠，对智慧农业精准种植有较高参考价值。",[160],{"name":151,"url":148},[28,29,30,162,163],"精准农业","作物推荐",[165,166],"农业人工智能 作物推荐 智慧农业 精准农业","农业人工智能 作物推荐","农业人工智能作物推荐智慧农业精准农业-2302","10.2174\u002F0118743315503187260909160504",{"doi":168,"openalex_id":170,"authors":171,"venue":151,"cited_by_count":37,"oa_url":148,"card":189,"direction":50,"ingested_from":52},"W7212307638",[172,174,177,180,183,186],{"name":173,"orcid":9},"Shreya Sriram",{"name":175,"orcid":176},"Prajeesh C B","https:\u002F\u002Forcid.org\u002F0000-0002-8404-8583",{"name":178,"orcid":179},"Delphin Raj Kesari Mary","https:\u002F\u002Forcid.org\u002F0000-0002-8989-7090",{"name":181,"orcid":182},"Anju S. Pillai","https:\u002F\u002Forcid.org\u002F0000-0001-5298-6789",{"name":184,"orcid":185},"R. V.","https:\u002F\u002Forcid.org\u002F0000-0002-0699-2990",{"name":187,"orcid":188},"V. M. Manikandan","https:\u002F\u002Forcid.org\u002F0000-0001-6903-7563",{"tldr":190,"method":191,"finding":192,"direction":50,"opportunity":193},"提出IoT与AI融合框架，结合实时土壤数据与天气预报推荐适宜作物。","三层IoT架构采集NPK、pH等数据，用RF、CNN等8种模型分类22种作物。","RF与CNN分类准确率达99.54%，降雨、湿度和钾是作物选择最关键因素。","可探索多源遥感与边缘计算融合，实现小农户低成本、可解释的实时作物推荐系统。","2026-09-13T23:30:09.745236Z",{"id":196,"title":197,"url":198,"summary":199,"summary_zh":200,"content":9,"source_name":201,"source_url":198,"published_at":202,"category":12,"cover_url":9,"hotness":19,"is_selected":14,"score":203,"score_detail":204,"sources":207,"tags":209,"search_phrases":212,"slug":215,"view_count":37,"doi":216,"paper":217,"created_at":233},2152,"STEM Education and AI Applications in Chemistry Teaching: From Pedagogical Ecosystems to the Self-Efficacy of Secondary School Teachers","https:\u002F\u002Fdoi.org\u002F10.12691\u002Fwjce-14-3-3","This study explores the integration of STEM education and artificial intelligence (AI) in chemistry teaching to foster a sustainable pedagogical ecosystem. Current secondary school practices reveal three critical operational gaps: a deficiency in interdisciplinary integration methodologies at the lower secondary level, intense high-stakes national examination pressures driving technological risk aversion at the upper secondary level, and pervasive administrative formalism in teacher professional development. To address these challenges, the research combines conceptual framework construction with an exemplary case study of a chemistry STEM project entitled \"Chemical Fertilizers and Smart Agriculture\". This project is structured across three progressive technical layers: direct soil pH and nutrient analysis using IoT sensors in field environments (Layer 1); the integration of large language models (such as ChatGPT) as personalized learning co-pilots alongside virtual laboratories to optimize learning trajectories (Layer 2); and the process-oriented evaluation of 21st-century competencies via a Digital Portfolio platform (Layer 3). Based on these empirical findings, the study proposes a bipolar set of breakthrough solutions: activating teachers' internal \"self-efficacy\" through digital self-directed learning, and reforming the \"transformative mission\" of teacher education institutions through the development of smart campus laboratories and professional learning networks. Finally, the study recommends institutionalizing Digital Portfolios parallel to traditional academic transcripts to alleviate high-stakes examination pressures and establish a genuine symbiotic connection between teacher education institutions and secondary schools in the AI era.","本研究探讨了STEM教育与人工智能（AI）在化学教学中的融合，以构建可持续的教学生态系统。当前中学实践暴露出三个关键的操作性缺口：初中阶段跨学科整合方法的缺失，高中阶段高利害全国性考试压力导致的技术风险规避，以及教师专业发展中普遍存在的行政形式主义。为应对这些挑战，本研究将概念框架构建与题为“化肥与智慧农业”的化学STEM项目案例研究相结合。该项目按三个递进的技术层次进行架构：在田间环境中利用物联网传感器进行直接的土壤pH和养分分析（第一层）；将大语言模型（如ChatGPT）作为个性化学习协 pilot，与虚拟实验室相结合以优化学习路径（第二层）；以及通过数字档案平台对21世纪能力进行过程性评价（第三层）。基于这些实证发现，本研究提出了一组两极突破性解决方案：通过数字化自主学习激活教师内在的“自我效能感”，以及通过建设智慧校园实验室和专业学习网络来变革教师教育机构的“转型使命”。最后，本研究建议将数字档案与传统学业成绩单并行制度化，以缓解高利害考试压力，并在AI时代建立教师教育机构与中学之间真正的共生联系。","World journal of chemical education","2026-09-09T00:00:00Z",62,{"impact":67,"substance":17,"depth":205,"authority":157,"freshness":67,"relevant":21,"comment":206},16,"以“化肥与智慧农业”项目为案例，探讨AI与物联网在化学教学中的融合路径，对农业信息化人才培养与教师数字素养建设有参考价值，但属教育研究范畴，产业影响有限。",[208],{"name":201,"url":198},[28,29,210,30,211],"数字素养","STEM教育",[213,214],"农业人工智能 数字素养 智慧农业 物联网","农业人工智能 数字素养","农业人工智能数字素养智慧农业物联网-2152","10.12691\u002Fwjce-14-3-3",{"doi":216,"openalex_id":218,"authors":219,"venue":201,"cited_by_count":37,"oa_url":226,"card":227,"direction":50,"ingested_from":52},"W7212041762",[220,223],{"name":221,"orcid":222},"Cao Thi Van Giang","https:\u002F\u002Forcid.org\u002F0009-0003-3483-9496",{"name":224,"orcid":225},"Cao Cự Giác","https:\u002F\u002Forcid.org\u002F0000-0003-4804-9009","https:\u002F\u002Fpubs.sciepub.com\u002Fwjce\u002F14\u002F3\u002F3\u002Fwjce-14-3-3.pdf",{"tldr":228,"method":229,"finding":230,"direction":231,"opportunity":232},"构建化学STEM教学框架，以“化肥与智慧农业”案例融合AI与物联网，提升教师自我效能。","概念框架+案例研究，用IoT传感器、ChatGPT、虚拟实验室和数字档案袋。","提出激活教师自我效能与改革师范教育的双极方案，建议数字档案袋制度化。","农业人工智能与决策模型","可实证检验AI+IoT农业项目对教师自我效能与学生21世纪能力的长效影响。","2026-09-11T23:30:13.553449Z",{"id":235,"title":236,"url":237,"summary":238,"summary_zh":239,"content":9,"source_name":240,"source_url":237,"published_at":241,"category":12,"cover_url":9,"hotness":19,"is_selected":14,"score":242,"score_detail":243,"sources":246,"tags":248,"search_phrases":251,"slug":254,"view_count":37,"doi":255,"paper":256,"created_at":272},2140,"AIOT in Predictive Agriculture - IoT and AI Integration for Real-Time Soil Monitoring and Smart Irrigation in Predictive Agriculture","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.84727","Modern agriculture faces severe challenges due to climate volatility, accelerating groundwater depletion, and the global imperative to maximize crop production on diminishing arable land. Traditional irrigation frameworks rely predominantly on static schedules or reactive threshold switching, leading to substantial water waste, energy inefficiencies, and suboptimal crop yields. To overcome these limitations, this paper proposes an end-to-end Artificial Intelligence of Things (AIoT) framework designed for real-time multi-parameter soil tracking and predictive smart irrigation. The system architecture deploys low-power IoT field nodes driven by ESP32 microcontrollers, integrated with capacitive soil moisture sensors, environmental sensors, and soil pH probes that stream telemetry data over lightweight MQTT protocols. To transition from reactive monitoring to proactive resource allocation, a cloud-based predictive engine utilizes Long Short-Term Memory (LSTM) neural networks to forecast 24- to-48-hour soil moisture depletion dynamics based on historical moisture profiles and localized meteorological factors. Experimental validation across a 90-day testbed demonstrates that the proposed predictive framework achieves a to reduction in total water consumption while maintaining optimal volumetric soil water content. Furthermore, deep-sleep dynamic power profiling confirms node energy autonomy of up to 219 days on a single battery charge, presenting a scalable, sustainable, and economically viable solution for precision agriculture.","现代农业正面临气候波动、地下水加速枯竭以及全球在日益减少的耕地上最大化作物产量的迫切需求等严峻挑战。传统灌溉框架主要依赖静态调度或反应式阈值切换，导致大量水资源浪费、能源效率低下以及作物产量欠优。为克服这些局限，本文提出了一种端到端的人工智能物联网（AIoT）框架，专为实时多参数土壤监测与预测性智能灌溉而设计。该系统架构部署了由ESP32微控制器驱动的低功耗物联网田间节点，集成了电容式土壤水分传感器、环境传感器和土壤pH探头，通过轻量级MQTT协议传输遥测数据。为实现从反应式监测向主动式资源分配的转变，基于云的预测引擎利用长短期记忆（LSTM）神经网络，根据历史水分剖面和局部气象因素，预测24至48小时的土壤水分消耗动态。在为期90天的测试平台上进行的实验验证表明，所提出的预测框架在保持最优土壤体积含水量的同时，实现了总用水量的降低。此外，深度睡眠动态功耗分析证实，节点在单次电池充电下可实现长达219天的能量自主运行，为精准农业提供了一种可扩展、可持续且经济可行的解决方案。","International Journal for Research in Applied Science and Engineering Technology","2026-09-10T00:00:00Z",75,{"impact":17,"substance":244,"depth":156,"authority":157,"freshness":67,"relevant":21,"comment":245},20,"AIoT+LSTM 预测灌溉的完整实证研究，90 天试验与节水、能耗数据扎实，对精准农业落地有参考价值，但期刊层级与影响范围偏细分领域。",[247],{"name":240,"url":237},[28,29,30,249,162,250],"智能灌溉","土壤监测",[252,253],"农业人工智能 土壤监测 智慧农业 智能灌溉","农业人工智能 土壤监测","农业人工智能土壤监测智慧农业智能灌溉-2140","10.22214\u002Fijraset.2026.84727",{"doi":255,"openalex_id":257,"authors":258,"venue":240,"cited_by_count":37,"oa_url":237,"card":267,"direction":50,"ingested_from":52},"W7212115545",[259,261,263,265],{"name":260,"orcid":9},"Gowri M.",{"name":262,"orcid":9},"Boomika M.",{"name":264,"orcid":9},"S. Rakshana",{"name":266,"orcid":9},"Rubali R.",{"tldr":268,"method":269,"finding":270,"direction":50,"opportunity":271},"提出AIoT框架，用LSTM预测土壤湿度实现智能灌溉，节水并延长节点续航。","ESP32节点+电容湿度\u002FpH传感器，MQTT上云，LSTM预测24-48小时湿","90天试验节水显著，土壤含水量保持最优，单次电池续航达219天。","可探索多作物多气候下LSTM泛化能力，及边缘端轻量预测模型降低云依赖。","2026-09-11T23:30:10.173013Z",{"id":274,"title":275,"url":276,"summary":277,"summary_zh":278,"content":9,"source_name":279,"source_url":276,"published_at":241,"category":12,"cover_url":9,"hotness":19,"is_selected":14,"score":280,"score_detail":281,"sources":283,"tags":285,"search_phrases":288,"slug":291,"view_count":37,"doi":292,"paper":293,"created_at":314},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",73,{"impact":205,"substance":17,"depth":156,"authority":68,"freshness":67,"relevant":21,"comment":282},"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。",[284],{"name":279,"url":276},[28,29,286,30,287],"设施农业","病虫害预警",[289,290],"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":292,"openalex_id":294,"authors":295,"venue":279,"cited_by_count":37,"oa_url":276,"card":309,"direction":50,"ingested_from":52},"W7212169080",[296,299,302,304,306],{"name":297,"orcid":298},"Lin‐Ya Chiu","https:\u002F\u002Forcid.org\u002F0000-0003-1691-5582",{"name":300,"orcid":301},"Dan Jeric Arcega Rustia","https:\u002F\u002Forcid.org\u002F0000-0002-5855-8109",{"name":303,"orcid":9},"Ya‐Fang Wu",{"name":305,"orcid":9},"Jui‐Yung Chung",{"name":307,"orcid":308},"Ta‐Te Lin","https:\u002F\u002Forcid.org\u002F0000-0003-0852-1372",{"tldr":310,"method":311,"finding":312,"direction":50,"opportunity":313},"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。","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。","2026-09-11T23:30:01.850931Z"]