[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2926":3,"related-2926":58},{"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,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":57},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。",null,"International Journal of Power Electronics and Drive Systems\u002FInternational Journal of Electrical and Computer Engineering","2026-09-18T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,13,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","机器学习","物联网","智能灌溉",[33,34],"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",0,"10.11591\u002Fijece.v16i5.pp2652-2663",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":50,"direction":54,"ingested_from":56},"W7213546497",[41,44,47],{"name":42,"orcid":43},"Sarra Gourari","https:\u002F\u002Forcid.org\u002F0009-0005-1846-0886",{"name":45,"orcid":46},"Wafa Difallah","https:\u002F\u002Forcid.org\u002F0000-0002-6181-0395",{"name":48,"orcid":49},"Belkacem Draoui","https:\u002F\u002Forcid.org\u002F0000-0001-5490-3991",{"tldr":51,"method":52,"finding":53,"direction":54,"opportunity":55},"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。","智慧农业 \u002F 农业物联网","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。","openalex","2026-09-19T23:30:11.009076Z",{"total":59,"page":22,"page_size":59,"items":60},6,[61,102,145,181,221,262],{"id":62,"title":63,"url":64,"summary":65,"summary_zh":66,"content":9,"source_name":67,"source_url":64,"published_at":68,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":69,"score_detail":70,"sources":75,"tags":77,"search_phrases":80,"slug":83,"view_count":36,"doi":84,"paper":85,"created_at":101},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":71,"substance":72,"depth":19,"authority":17,"freshness":73,"relevant":22,"comment":74},18,20,8,"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。",[76],{"name":67,"url":64},[27,28,30,31,78,79],"精准农业","土壤监测",[81,82],"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":84,"openalex_id":86,"authors":87,"venue":67,"cited_by_count":36,"oa_url":64,"card":96,"direction":54,"ingested_from":56},"W7212115545",[88,90,92,94],{"name":89,"orcid":9},"Gowri M.",{"name":91,"orcid":9},"Boomika M.",{"name":93,"orcid":9},"S. Rakshana",{"name":95,"orcid":9},"Rubali R.",{"tldr":97,"method":98,"finding":99,"direction":54,"opportunity":100},"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":103,"title":104,"url":105,"summary":106,"summary_zh":9,"content":9,"source_name":107,"source_url":105,"published_at":108,"category":12,"cover_url":9,"hotness":109,"is_selected":14,"score":110,"score_detail":111,"sources":115,"tags":122,"search_phrases":124,"slug":127,"view_count":36,"doi":128,"paper":129,"created_at":144},1249,"Enhancing water management with intelligent irrigation systems using edge computing, machine learning and IoT","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1827416","Agriculture is responsible for about 70% of freshwater withdrawals, and water scarcity and inefficient methods of water use remain threats to food security. The traditional way of irrigation causes more water loss and requires innovative solutions to optimize water distribution. This research provides an overview of an intelligent irrigation system, which combines the Internet of Things (IoT), machine learning (ML), and edge computing to optimize water usage and improve crop production. The system uses IoT sensors and edge devices to collect data on the environment and estimate the need for irrigation in real-time. It combines machine learning with system components like the Soil Monitoring Unit (SMU) and Water Management Unit (WMU) to provide scalable, real-time and efficient irrigation management. It is deployed locally at the edge, without the need for cloud computing, has the potential to integrate multi-sensor fusion for storm detection, and offers multiple ML models in a modular, low cost system that can be adapted to various agricultural settings. To optimize irrigation schedules, six supervised machine-learning models were used, such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD) and Multi-Layer Perceptron (MLP). DT and RF had the highest classification accuracy of 93% and R 2 regression scores of 0.98 respectively, indicating high prediction reliability. However, Support Vector Machine (SVM) did not do that well, with an accuracy percentage of 58% and an R 2 score of 0.18. Although more complex, RF’s accuracy is a great option for larger applications and DT balances efficiency and scalability. The findings corroborate the utility of water efficient irrigation system and its prospect in augmenting productivity of agriculture. Future work will include expanding the field trials and developing hybrid models to further improve adaptability of the system in different agricultural environments. The findings of this research can offer understanding for sustainable farming practices, that are essential when it comes to water scarcity and food security in the global context.","Frontiers in Sustainable Food Systems","2026-08-25T00:00:00Z",40,74,{"impact":71,"substance":112,"depth":71,"authority":20,"freshness":113,"relevant":22,"comment":114},22,3,"研究提出结合IoT、ML与边缘计算的智能灌溉系统，DT和RF模型精度高，对节水农业有实质参考价值。",[116,117,120],{"name":107,"url":105},{"name":118,"url":119},"Zenodo (CERN European Organization for Nuclear Research)","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790399",{"name":118,"url":121},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790398",[27,28,123,30,31],"边缘计算",[125,126],"农业人工智能 智慧农业 智能灌溉 边缘计算","农业人工智能 智慧农业","农业人工智能智慧农业智能灌溉边缘计算-1249","10.3389\u002Ffsufs.2026.1827416",{"doi":128,"openalex_id":130,"authors":131,"venue":107,"cited_by_count":36,"oa_url":138,"card":139,"direction":54,"ingested_from":56},"W7204189894",[132,135],{"name":133,"orcid":134},"Mohammed Sulaiman Mustafa","https:\u002F\u002Forcid.org\u002F0000-0002-2920-9428",{"name":136,"orcid":137},"Hakan Kutucu","https:\u002F\u002Forcid.org\u002F0000-0001-7144-7246","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1827416\u002Fpdf",{"tldr":140,"method":141,"finding":142,"direction":54,"opportunity":143},"提出结合IoT、机器学习和边缘计算的智能灌溉系统，优化用水并提高作物产量。","使用IoT传感器和边缘设备，结合六种监督学习模型（如DT、RF）进行实时灌溉管理","决策树和随机森林表现最佳，准确率93%，R²为0.98，优于其他模型。","可探索混合模型和扩展田间试验，提高系统在不同农业环境中的适应性。","2026-09-01T04:03:09.639788Z",{"id":146,"title":147,"url":148,"summary":149,"summary_zh":150,"content":9,"source_name":118,"source_url":148,"published_at":151,"category":12,"cover_url":9,"hotness":152,"is_selected":14,"score":153,"score_detail":154,"sources":156,"tags":160,"search_phrases":163,"slug":166,"view_count":36,"doi":167,"paper":168,"created_at":180},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","2026-09-17T00:00:00Z",25,77,{"impact":71,"substance":112,"depth":71,"authority":13,"freshness":21,"relevant":22,"comment":155},"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。",[157,158],{"name":118,"url":148},{"name":118,"url":159},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22809199",[27,28,30,161,162],"无线能量传输","绿色通信",[164,165],"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":167,"openalex_id":169,"authors":170,"venue":118,"cited_by_count":36,"oa_url":148,"card":175,"direction":54,"ingested_from":56},"W7213495899",[171,173],{"name":172,"orcid":9},"Lucky Joseph Ogbogbo",{"name":174,"orcid":9},"Osaremwinda OMOROGIUWA",{"tldr":176,"method":177,"finding":178,"direction":54,"opportunity":179},"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":182,"title":183,"url":184,"summary":185,"summary_zh":186,"content":9,"source_name":187,"source_url":184,"published_at":151,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":188,"score_detail":189,"sources":192,"tags":194,"search_phrases":196,"slug":198,"view_count":36,"doi":199,"paper":200,"created_at":220},2802,"Advanced olive leaf area prediction using machine learning methods","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71390-9","Abstract Accurate leaf area estimation is essential for understanding olive tree physiology, productivity, and stress adaptationThis study developed and evaluated machine learning models for non-destructive olive leaf area prediction using linear measurements (length and width) from 30 diverse cultivars at the Tarom Olive Research Station, Iran. Six machine learning algorithms, Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest, Decision Tree, AdaBoost, and XGBoost were optimized using Bayesian optimization, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Results demonstrated that PSO consistently outperformed other optimization methods across most models. The ANN model optimized with PSO achieved the highest prediction accuracy (R 2 = 0.9828, RMSE = 0.3009 cm 2 ). External validation using eight additional cultivars confirmed model generalizability, with the universal ANN model maintaining R 2 > 0.98. This study provides a robust, non-destructive methodology for olive leaf area estimation applicable across diverse cultivars, offering practical implications for precision agriculture, phenotyping, and orchard management under changing climatic conditions.","摘要 准确的叶面积估算对于理解油橄榄树的生理特性、生产力及逆境适应性至关重要。本研究在伊朗塔罗姆油橄榄研究站，利用来自30个不同品种的线性测量数据（长度和宽度），开发并评估了用于无损油橄榄叶面积预测的机器学习模型。采用贝叶斯优化、遗传算法（GA）和粒子群优化（PSO）对六种机器学习算法——人工神经网络（ANN）、支持向量回归（SVR）、随机森林、决策树、AdaBoost和XGBoost——进行了优化。结果表明，在大多数模型中，PSO始终优于其他优化方法。经PSO优化后的ANN模型取得了最高的预测精度（R² = 0.9828，RMSE = 0.3009 cm²）。利用另外八个品种进行的外部验证证实了模型的泛化能力，通用ANN模型保持R² > 0.98。本研究为适用于不同品种的油橄榄叶面积估算提供了一种稳健的无损方法，为气候变化条件下的精准农业、表型分析和果园管理提供了实际应用价值。","Scientific Reports",73,{"impact":17,"substance":72,"depth":19,"authority":190,"freshness":13,"relevant":22,"comment":191},14,"基于30个品种的机器学习叶片面积无损预测研究，方法新颖、验证充分，对精准农业与表型分析有实用价值，但属细分领域技术进展，影响范围有限。",[193],{"name":187,"url":184},[27,28,29,78,195],"表型分析",[197,126],"农业人工智能 智慧农业 机器学习 精准农业","农业人工智能智慧农业机器学习精准农业-2802","10.1038\u002Fs41598-026-71390-9",{"doi":199,"openalex_id":201,"authors":202,"venue":187,"cited_by_count":36,"oa_url":184,"card":213,"direction":219,"ingested_from":56},"W7213449017",[203,206,208,210],{"name":204,"orcid":205},"Ahmad Reza Dadras","https:\u002F\u002Forcid.org\u002F0000-0001-8591-5813",{"name":207,"orcid":9},"Hossein Sabouri",{"name":209,"orcid":9},"Ali Tanhaei",{"name":211,"orcid":212},"Sayed Javad Sajadi","https:\u002F\u002Forcid.org\u002F0000-0002-6555-080X",{"tldr":214,"method":215,"finding":216,"direction":217,"opportunity":218},"用机器学习基于叶长宽非破坏性预测30个橄榄品种叶面积，PSO优化ANN精度最高。","30个品种叶长宽数据，六种ML算法结合贝叶斯、GA、PSO优化。","PSO优化ANN最优（R²=0.9828），外部8品种验证R²>0.98，通用性好。","农业遥感与作物表型","可拓展至多物种、多环境及无人机\u002F手机图像自动测量，构建通用叶面积表型平台。","农业人工智能与决策模型","2026-09-17T23:30:59.169744Z",{"id":222,"title":223,"url":224,"summary":225,"summary_zh":226,"content":9,"source_name":227,"source_url":224,"published_at":228,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":229,"score_detail":230,"sources":233,"tags":235,"search_phrases":237,"slug":240,"view_count":36,"doi":241,"paper":242,"created_at":261},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":73,"substance":190,"depth":20,"authority":231,"freshness":21,"relevant":22,"comment":232},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。",[234],{"name":227,"url":224},[27,28,123,30,236],"动物监测",[238,239],"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":241,"openalex_id":243,"authors":244,"venue":227,"cited_by_count":36,"oa_url":255,"card":256,"direction":54,"ingested_from":56},"W7213273522",[245,247,249,251,253],{"name":246,"orcid":9},"Vinit Masale",{"name":248,"orcid":9},"Suyog Mamankar",{"name":250,"orcid":9},"Prathamesh Sawant",{"name":252,"orcid":9},"Mhaboob Ali",{"name":254,"orcid":9},"Prof. Jyoti Shrote","https:\u002F\u002Fijirt.org\u002Fpublishedpaper\u002FIJIRT208534_PAPER.pdf",{"tldr":257,"method":258,"finding":259,"direction":54,"opportunity":260},"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":263,"title":264,"url":265,"summary":266,"summary_zh":267,"content":9,"source_name":268,"source_url":265,"published_at":151,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":269,"score_detail":270,"sources":272,"tags":274,"search_phrases":277,"slug":280,"view_count":36,"doi":281,"paper":282,"created_at":294},2767,"Prototype Sistem Kendali Penyiraman Tanaman Otomatis Berbasis IoT dengan Mode Manual dan Otomatis Menggunakan ESP32 dan Platform Blynk","https:\u002F\u002Fdoi.org\u002F10.61132\u002Fjupiter.v4i5.1609","The increasing scarcity of water resources and the need for optimal soil moisture levels for plant growth have driven the development of smart irrigation systems based on the Internet of Things (IoT). This study presents a prototype of an automatic plant watering control system using an ESP32 microcontroller equipped with a capacitive soil moisture sensor, relay module, and I2C LCD interface. The system is designed with two operating modes: an automatic mode that independently activates the water pump based on soil moisture thresholds, and a manual mode that allows users to control the pump directly through the Blynk application on a smartphone. Sensor data is transmitted to the Blynk platform in real-time via a WiFi connection using virtual pin communication protocols. Test results show that the system is capable of reading soil moisture values with good accuracy, responding to changes in soil conditions in less than 2 seconds, and simultaneously displaying moisture status and pump conditions on both the LCD and the Blynk application. This system is expected to serve as a solution for efficient water use in urban and household agriculture, supporting the concept of sustainable and energy-efficient farming.","水资源日益稀缺以及植物生长对最佳土壤湿度的需求，推动了基于物联网（IoT）的智能灌溉系统的发展。本研究提出了一种自动植物浇水控制系统的原型，该系统采用ESP32微控制器，并配备电容式土壤湿度传感器、继电器模块和I2C液晶显示接口。系统设计了两种运行模式：自动模式根据土壤湿度阈值独立启动水泵，手动模式允许用户通过智能手机上的Blynk应用程序直接控制水泵。传感器数据通过WiFi连接，利用虚拟引脚通信协议实时传输至Blynk平台。测试结果表明，该系统能够以良好的精度读取土壤湿度值，在2秒内响应土壤条件变化，并同时在液晶显示屏和Blynk应用程序上显示湿度状态和水泵运行情况。该系统有望为城市和家庭农业中的高效用水提供解决方案，支持可持续和节能农业的理念。","Jupiter Publikasi Ilmu Keteknikan Industri Teknik Elektro dan Informatika",50,{"impact":73,"substance":190,"depth":20,"authority":231,"freshness":13,"relevant":22,"comment":271},"基于ESP32与Blynk的自动灌溉原型系统，方法常规、规模有限，属细分技术验证，公共价值与权威性一般，不宜进入每日精选。",[273],{"name":268,"url":265},[27,30,31,275,276],"土壤墒情","节水农业",[278,279],"土壤墒情 智慧农业 智能灌溉 节水农业","土壤墒情 智慧农业","土壤墒情智慧农业智能灌溉节水农业-2767","10.61132\u002Fjupiter.v4i5.1609",{"doi":281,"openalex_id":283,"authors":284,"venue":268,"cited_by_count":36,"oa_url":265,"card":289,"direction":54,"ingested_from":56},"W7213462460",[285,287],{"name":286,"orcid":9},"Rifki Aldiansyah",{"name":288,"orcid":9},"Dani Sasmoko",{"tldr":290,"method":291,"finding":292,"direction":54,"opportunity":293},"基于ESP32和Blynk开发了带手动\u002F自动模式的自动浇水原型系统。","ESP32、电容式土壤湿度传感器、继电器、I2C LCD与Blynk物联网平台。","系统能准确读取土壤湿度，响应时间小于2秒，并实时同步显示状态。","可扩展多传感器融合与自适应阈值算法，提升灌溉决策的精准性和节能性。","2026-09-17T23:30:10.351496Z"]