[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-71":3,"related-71":78},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":8,"secondary_topics":33,"classification":34,"search_phrases":72,"slug":75,"view_count":13,"doi":76,"paper":8,"created_at":77},71,"基于边缘计算、机器学习与物联网的智能灌溉系统提升农业水资源管理（土耳其Mustafa & Kutucu，Frontiers 2026-07-28接受）","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1827416\u002Ffull","Mustafa MS（卡拉比克大学计算机工程系）与Kutucu H（土耳其）在Frontiers in Sustainable Food Systems发表的原创研究\"Enhancing Water Management with Intelligent Irrigation Systems Using Edge Computing, Machine Learning and IoT\"于2026-07-28接受。针对农业占全球淡水取用量约70%、传统灌溉水耗大、效率低的现实，研究构建将物联网、机器学习与边缘计算融合的智能灌溉系统：物联网传感器和边缘设备实时采集环境数据并估算灌溉需求，机器学习模型与土壤监测单元（SMU）和水管理单元（WMU）协同，在边缘本地部署实现可扩展、实时、高效灌溉管理。研究比较决策树（DT）、随机森林（RF）、支持向量机（SVM）、K近邻（KNN）、随机梯度下降（SGD）和多层感知机（MLP）六种监督学习模型，其中决策树和随机森林分类准确率达93%、R²回归得分0.98，显著优于SVM（准确率58%、R²=0.18），为不同农业场景下水资源高效管理提供可复用方案。原文：Enhancing Water Management with Intelligent Irrigation Systems Using Edge Computing, Machine Learning and IoT",null,"[](https:\u002F\u002Fwww.frontiersin.org\u002F)\n\n[![Image 2](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1827416\u002Ffull) Frontiers in Sustainable Food Systems](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems)\n\n About us\n\nAbout us\n\n*   Who we are\n*   [Mission and values](https:\u002F\u002Fwww.frontiersin.org\u002Fabout\u002Fmission)\n*   [History](https:\u002F\u002Fwww.frontiersin.org\u002Fabout\u002Fhistory)\n*   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Water-Smart Food Production\n\nVolume 10 - 2026 |  doi: 10.3389\u002Ffsufs.2026.1827416\n\n[Published in ![Image 6: Frontiers in Sustainable Food Systems](https:\u002F\u002Fd2csxpduxe849s.cloudfront.net\u002Fmedia\u002FE32629C6-9347-4F84-81FEAEF7BFA342B3\u002FF3B1CE16-45F3-4D96-BE72BE0491F0DAF2\u002Fwebimage-08DFEB67-EB21-43D0-995D8A1DFF53DB18.png) Frontiers in Sustainable Food Systems * #### Water-Smart Food Production * **4.1** impact factor * **6** citescore](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems)[Part of a Research Topic Advanced technologies for water management: targeting sustainable agriculture Submission open * **19k** views * **11** articles](https:\u002F\u002Fwww.frontiersin.org\u002Fresearch-topics\u002F72689\u002Fadvanced-technologies-for-water-management-targeting-sustainable-agriculture)\n\n### Reviewers\n\nReviewed by\n\n![Image 7: 2 Anonymous reviewers](https:\u002F\u002Fstatic2.frontiersin.org\u002Fstatic-resources\u002Fimages\u002Fdefault_profileimage.webp)\n\n2 Anonymous reviewers\n\nOutline\n\n*    [Abstract](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1827416\u002Ffull#h1)\n\n## ORIGINAL RESEARCH article\n\nFront. Sustain. Food Syst.\n\nSec. Water-Smart Food Production\n\n# Enhancing Water Management with Intelligent Irrigation Systems Using Edge Computing, Machine Learning and IoT\n\n*   [![Image 8: Mohammed Sulaiman Mustafa](https:\u002F\u002Floop.frontiersin.org\u002Fimages\u002Fprofile\u002F3415048\u002F74) Mohammed Sulaiman Mustafa 1](https:\u002F\u002Floop.frontiersin.org\u002Fpeople\u002F3415048)\n*   [![Image 9: Hakan Kutucu](https:\u002F\u002Floop.frontiersin.org\u002Fimages\u002Fprofile\u002F3429124\u002F74) Hakan Kutucu 2](https:\u002F\u002Floop.frontiersin.org\u002Fpeople\u002F3429124)\n\n*   1. Karabuk Universitesi Bilgisayar Muhendislik Bolumu, Karabük, Türkiye\n\n*   2. Karabuk Universitesi Yazilim Muhendisligi Bolumu, Karabük, Türkiye\n\nArticle metrics\n\n[View details](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1827416\u002Ffull#metrics)\n\n15\n\nViews\n\nThe final, formatted version of the article will be published soon.\n\nNotify me\n\nSelect one of your emails\n\nYou have multiple emails registered with Frontiers:\n\nNotify me\n\nNotify me on publication\n\nPlease enter your email address:\n\nEmail \n\nIf you already have an account, please [login](https:\u002F\u002Fwww.frontiersin.org\u002Flogin?returnUrl=\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1827416\u002Fabstract)\n\nYou don't have a Frontiers account ? You can [register here](https:\u002F\u002Fwww.frontiersin.org\u002Fregister?returnUrl=\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1827416\u002Fabstract)\n\nNotify me\n\n## Abstract\n\nAgriculture is respons","Frontiers in Sustainable Food Systems · 2026-07-28","2026-07-28T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,16,13,9,1,"论文结合边缘计算、机器学习与物联网，提出智能灌溉系统，方法新颖，数据详实，对农业水资源管理有实际价值，发表于Frontiers期刊，权威性高，时效性好。",[25],{"name":10,"url":6},[12,27,28,29,30,31,32],"边缘计算","机器学习","物联网","智能灌溉","Frontiers","农业水资源",[],{"status":35,"locked":14,"version":36,"method":37,"evidence":38},"needs_review","2026-10-22.2","rules",{"candidates":39,"agricultural_evidence":43,"reason":71},[40,50,57,64],{"code":41,"score":42,"supported":43,"title":44,"abstract":47,"tags":49},"H3",27,true,[45,46],"灌溉","水资源",[45,46,48],"irrigation",[32,30],{"code":51,"score":42,"supported":43,"title":52,"abstract":53,"tags":56},"I5",[29,27],[29,27,54,55],"IoT","edge computing",[29,27],{"code":58,"score":59,"supported":43,"title":60,"abstract":61,"tags":63},"I2",15,[28],[28,62],"machine learning",[28],{"code":65,"score":66,"supported":43,"title":67,"abstract":68,"tags":70},"H2",3,[],[69],"土壤",[],"多个产业证据相当，需要确认主归属",[73,74],"农业水资源 智能灌溉 机器学习 边缘计算","农业水资源 智能灌溉","农业水资源智能灌溉机器学习边缘计算-71","10.3389\u002Ffsufs.2026.1827416\u002Ffull","2026-07-29T23:54:14.301855Z",{"total":79,"page":22,"page_size":79,"items":80},6,[81,180,259,314,389,465],{"id":82,"title":83,"url":84,"summary":85,"summary_zh":86,"content":8,"source_name":87,"source_url":84,"published_at":88,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":89,"score_detail":90,"sources":95,"tags":97,"primary_topic":100,"secondary_topics":104,"classification":114,"search_phrases":153,"slug":156,"view_count":157,"doi":158,"paper":159,"created_at":179},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":91,"substance":92,"depth":93,"authority":20,"freshness":21,"relevant":22,"comment":94},12,21,17,"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。",[96],{"name":87,"url":84},[98,99,28,29,30],"智慧农业","农业人工智能",{"code":41,"slug":48,"name":101,"path":102,"parent_code":103},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[105,110],{"code":58,"slug":106,"name":107,"path":108,"parent_code":109},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":51,"slug":111,"name":112,"path":113,"parent_code":109},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":115,"locked":14,"version":36,"method":37,"evidence":116},"classified",{"candidates":117,"agricultural_evidence":43},[118,122,127,131,136,142,148],{"code":41,"score":17,"supported":43,"title":119,"abstract":120,"tags":121},[48],[45,46,48],[30],{"code":58,"score":20,"supported":43,"title":123,"abstract":125,"tags":126},[124],"artificial intelligence",[28],[99,28],{"code":51,"score":21,"supported":43,"title":128,"abstract":129,"tags":130},[54],[],[29],{"code":65,"score":79,"supported":43,"title":132,"abstract":133,"tags":135},[],[69,134],"soil",[],{"code":137,"score":66,"supported":43,"title":138,"abstract":139,"tags":141},"H7",[],[140],"干旱",[],{"code":143,"score":66,"supported":43,"title":144,"abstract":145,"tags":147},"H8",[],[146],"可持续农业",[],{"code":149,"score":22,"supported":14,"title":150,"abstract":151,"tags":152},"I1",[],[],[98],[154,155],"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",2,"10.11591\u002Fijece.v16i5.pp2652-2663",{"doi":158,"openalex_id":160,"authors":161,"venue":87,"cited_by_count":171,"oa_url":84,"card":172,"direction":176,"ingested_from":178},"W7213546497",[162,165,168],{"name":163,"orcid":164},"Sarra Gourari","https:\u002F\u002Forcid.org\u002F0009-0005-1846-0886",{"name":166,"orcid":167},"Wafa Difallah","https:\u002F\u002Forcid.org\u002F0000-0002-6181-0395",{"name":169,"orcid":170},"Belkacem Draoui","https:\u002F\u002Forcid.org\u002F0000-0001-5490-3991",0,{"tldr":173,"method":174,"finding":175,"direction":176,"opportunity":177},"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",{"id":181,"title":182,"url":183,"summary":184,"summary_zh":8,"content":8,"source_name":185,"source_url":183,"published_at":186,"category":12,"cover_url":8,"hotness":187,"is_selected":14,"score":188,"score_detail":189,"sources":192,"tags":202,"primary_topic":203,"secondary_topics":204,"classification":207,"search_phrases":237,"slug":240,"view_count":241,"doi":242,"paper":243,"created_at":258},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",55,74,{"impact":17,"substance":190,"depth":17,"authority":20,"freshness":66,"relevant":22,"comment":191},22,"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。",[193,194,197,199],{"name":185,"url":183},{"name":195,"url":196},"Zenodo (CERN European Organization for Nuclear Research)","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790399",{"name":195,"url":198},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790398",{"name":200,"url":201},"Journal of Animal Science","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjas\u002Fskag311",[98,99,27,29,30],{"code":51,"slug":111,"name":112,"path":113,"parent_code":109},[205,206],{"code":41,"slug":48,"name":101,"path":102,"parent_code":103},{"code":58,"slug":106,"name":107,"path":108,"parent_code":109},{"status":115,"locked":14,"version":36,"method":37,"evidence":208},{"candidates":209,"agricultural_evidence":43},[210,215,219,223,229,233],{"code":51,"score":42,"supported":43,"title":211,"abstract":212,"tags":214},[54,55],[213,54,55],"internet of things",[29,27],{"code":41,"score":91,"supported":43,"title":216,"abstract":217,"tags":218},[48],[48],[30],{"code":58,"score":91,"supported":43,"title":220,"abstract":221,"tags":222},[62],[62],[99],{"code":224,"score":66,"supported":43,"title":225,"abstract":226,"tags":228},"A8",[],[227],"food security",[],{"code":65,"score":66,"supported":43,"title":230,"abstract":231,"tags":232},[],[134],[],{"code":149,"score":22,"supported":14,"title":234,"abstract":235,"tags":236},[],[],[98],[238,239],"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",4,"10.3389\u002Ffsufs.2026.1827416",{"doi":242,"openalex_id":244,"authors":245,"venue":185,"cited_by_count":171,"oa_url":252,"card":253,"direction":176,"ingested_from":178},"W7204189894",[246,249],{"name":247,"orcid":248},"Mohammed Sulaiman Mustafa","https:\u002F\u002Forcid.org\u002F0000-0002-2920-9428",{"name":250,"orcid":251},"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":254,"method":255,"finding":256,"direction":176,"opportunity":257},"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":260,"title":261,"url":262,"summary":263,"summary_zh":8,"content":8,"source_name":264,"source_url":262,"published_at":265,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":266,"score_detail":267,"sources":271,"tags":273,"primary_topic":8,"secondary_topics":276,"classification":277,"search_phrases":293,"slug":296,"view_count":171,"doi":297,"paper":298,"created_at":313},5239,"Development and Performance Evaluation of an IoT-Based Smart Irrigation System with Real-Time Data Monitoring for Beetroot (Beta vulgaris L.)","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11385500\u002Fv1","Development and Performance Evaluation of an IoT-Based Smart Irrigation System with Real-Time Data Monitoring for Beetroot (Beta vulgaris L.)。Research Square","Research Square","2026-10-16T00:00:00Z",53,{"impact":268,"substance":19,"depth":269,"authority":79,"freshness":21,"relevant":22,"comment":270},8,14,"预印本论文，聚焦物联网智能灌溉与实时监测，方法有参考价值但尚未经同行评审，影响范围限于细分领域。",[272],{"name":264,"url":262},[98,29,30,274,275],"实时监测","甜菜根",[],{"status":35,"locked":14,"version":36,"method":37,"evidence":278},{"candidates":279,"agricultural_evidence":14,"reason":292},[280,284,288],{"code":41,"score":91,"supported":43,"title":281,"abstract":282,"tags":283},[48],[48],[30],{"code":51,"score":91,"supported":43,"title":285,"abstract":286,"tags":287},[54],[54],[29],{"code":149,"score":22,"supported":14,"title":289,"abstract":290,"tags":291},[],[],[98],"农业相关性需要复核",[294,295],"IoT 智能灌溉 甜菜根","Research Square 智能灌溉","IoT智能灌溉甜菜根-5239","10.21203\u002Frs.3.rs-11385500\u002Fv1",{"doi":297,"openalex_id":299,"authors":300,"venue":264,"cited_by_count":171,"oa_url":312,"card":8,"direction":176,"ingested_from":178},"W7222511746",[301,303,305,308,310],{"name":302,"orcid":8},"Soulaiman Mwamini",{"name":304,"orcid":8},"Triphonie Uwamariya",{"name":306,"orcid":307},"Jean Nepo Nsengiyumva","https:\u002F\u002Forcid.org\u002F0000-0002-0607-1825",{"name":309,"orcid":8},"Venant Kubwimana",{"name":311,"orcid":8},"Marie Jeanne Umuhoza","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-11385500\u002Flatest.pdf","2026-10-18T23:30:09.469477Z",{"id":315,"title":316,"url":317,"summary":318,"summary_zh":319,"content":8,"source_name":320,"source_url":317,"published_at":321,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":322,"score_detail":323,"sources":325,"tags":327,"primary_topic":330,"secondary_topics":331,"classification":332,"search_phrases":367,"slug":370,"view_count":171,"doi":8,"paper":371,"created_at":388},4890,"ระบบพยากรณและเฝาระวงคณภาพนำอจฉรยะตามดชนคณภาพนำ ดวยเทคนคการเรยนรของเครองและอนเทอรเนตของสรรพสง","https:\u002F\u002Fso04.tci-thaijo.org\u002Findex.php\u002FJSSE\u002Farticle\u002Fview\u002F289948","This research utilizes Internet of Things (IoT) technology and Machine Learning (ML) to develop a prototype for a smart water quality forecasting and monitoring system. The study focuses on analyzing the Reduced Water Quality Index (rWQI) based on water temperature, pH, and Total Dissolved Solids (TDS). To forecast water quality 1 to 6 hours in advance, the performance of four predictive models was evaluated and compared: Extremely Randomized Trees (ET), Random Forest (RF), Simple Linear Regression (LR), and Multiple Linear Regression (MLR). The efficiency of these algorithms was validated using four key evaluation metrics: Coefficient of Determination (R2), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (sMAPE). The experimental results indicated that the predictive performance of the models varied significantly depending on the forecasting horizon. In the short-term horizon (1–3 hours ahead), the RF model achieved the highest performance, with an R² of 0.566 and an MAE of 5.215 in the first hour. Conversely, for long-term forecasting (4–6 hours ahead), all models exhibited a significant drop in performance (R² \u003C 0), yielding higher prediction errors than the Mean Baseline. This long-term limitation stems from high water quality volatility in real-world environments combined with error accumulation in multi-step ahead forecasting. In conclusion, these findings emphasize that aligning model selection with the specific forecasting horizon is critical for enhancing system efficiency and stability. The developed model demonstrates high accuracy and suitability for short-term early warnings within 3 hours. This knowledge enables operators to plan water quality management with precision and speed, thereby reducing operational costs and the risk of aquatic livestock loss. Furthermore, this system serves as a technological prototype in digital agriculture, elevating conventional water quality monitoring into proactive management through intelligent technology.","本研究利用物联网（IoT）技术与机器学习（ML），开发了一套智能水质预测与监测系统原型。研究聚焦于基于水温、pH值和总溶解固体（TDS）的简化水质指数（rWQI）分析。为提前1至6小时预测水质，本研究评估并比较了四种预测模型的性能：极端随机树（ET）、随机森林（RF）、简单线性回归（LR）和多元线性回归（MLR）。采用四项关键评价指标验证了这些算法的有效性：决定系数（R²）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）和对称平均绝对百分比误差（sMAPE）。实验结果表明，各模型的预测性能因预测时间跨度不同而存在显著差异。在短期预测（提前1至3小时）中，RF模型表现最佳，第一小时的R²为0.566，MAE为5.215。相反，在长期预测（提前4至6小时）中，所有模型性能均显著下降（R² \u003C 0），预测误差高于均值基线。这一长期局限性源于真实环境中水质的高波动性以及多步超前预测中的误差累积。综上所述，这些发现强调，将模型选择与特定预测时间跨度相匹配对于提升系统效率与稳定性至关重要。所开发的模型具有较高精度，适用于3小时内的短期预警。这一认识使操作人员能够精准、快速地规划水质管理，从而降低运营成本和水产养殖损失风险。此外，该系统可作为数字农业中的技术原型，通过智能技术将传统水质监测提升为主动管理。","Journal of Science and Science Education (JSSE)","2026-10-12T00:00:00Z",70,{"impact":91,"substance":18,"depth":93,"authority":20,"freshness":268,"relevant":22,"comment":324},"论文将物联网与机器学习结合用于水产养殖水质短期预警，方法对比扎实、结论明确，但属细分技术原型，影响范围有限。",[326],{"name":320,"url":317},[98,28,29,328,329],"水产养殖","水质监测",{"code":51,"slug":111,"name":112,"path":113,"parent_code":109},[],{"status":115,"locked":14,"version":36,"method":37,"evidence":333},{"candidates":334,"agricultural_evidence":43},[335,339,346,350,356,362],{"code":51,"score":13,"supported":43,"title":336,"abstract":337,"tags":338},[],[29,213,54],[29],{"code":149,"score":340,"supported":43,"title":341,"abstract":342,"tags":345},7,[],[343,344],"数字农业","digital agriculture",[98],{"code":58,"score":340,"supported":43,"title":347,"abstract":348,"tags":349},[],[28,62],[28],{"code":351,"score":241,"supported":43,"title":352,"abstract":353,"tags":355},"D",[],[354],"水产",[328],{"code":357,"score":66,"supported":43,"title":358,"abstract":359,"tags":361},"C",[],[360],"livestock",[],{"code":363,"score":22,"supported":14,"title":364,"abstract":365,"tags":366},"D4",[],[],[329],[368,369],"物联网 机器学习 水质预测","rWQI 水质监测 水产养殖","物联网机器学习水质预测-4890",{"doi":8,"openalex_id":372,"authors":373,"venue":320,"cited_by_count":171,"oa_url":317,"card":383,"direction":176,"ingested_from":178},"W7221732805",[374,376,378,380],{"name":375,"orcid":8},"Wanarase Sinhashthita",{"name":377,"orcid":8},"Tossaporn Alherbe",{"name":379,"orcid":8},"Kamol Chareonsri",{"name":381,"orcid":382},"Chayaporn Kaensar","https:\u002F\u002Forcid.org\u002F0009-0005-3330-8532",{"tldr":384,"method":385,"finding":386,"direction":176,"opportunity":387},"利用物联网与机器学习构建水质预测监测系统，提前1-6小时预测rWQI。","基于水温、pH、TDS数据，比较ET、RF、LR、MLR四种模型预测性能。","短期（1-3小时）RF最优，长期（4-6小时）所有模型性能显著下降。","可探索融合时序深度学习与误差校正，提升长期水质预测精度与鲁棒性。","2026-10-14T23:30:13.314647Z",{"id":390,"title":391,"url":392,"summary":393,"summary_zh":394,"content":8,"source_name":395,"source_url":392,"published_at":396,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":397,"score_detail":398,"sources":400,"tags":402,"primary_topic":405,"secondary_topics":406,"classification":407,"search_phrases":439,"slug":442,"view_count":171,"doi":443,"paper":444,"created_at":464},4568,"Design and Controlled-Plot Validation of a Smart Dual-Mode Irrigation System for West African Riverbank Farms","https:\u002F\u002Fdoi.org\u002F10.33003\u002Ffjs-2026-1019-6198","Ewedu (Corchorus olitorius) is a nutritionally and economically vital leafy vegetable in West Africa, yet dry-season cultivation on riverbank plots remains constrained by manual, intuition-driven irrigation that is labour-intensive and agronomically imprecise. Existing smart irrigation systems are poorly suited to this setting, as they typically assume mains electricity, reliable internet, and technically skilled users conditions largely absent on West African riverbank farms. To address this gap, we designed, built, and field-tested a low-cost, solar-powered IoT irrigation system that uses real-time capacitive soil moisture monitoring to automate ewedu irrigation for smallholder farmers. The system integrates an ESP32 microcontroller running crop-specific hysteresis logic, a 12V DC pump and micro-sprinkler network powered directly from a solar-charged battery bank (eliminating the inverter stage), and a farmer-friendly web dashboard usable both online and offline. Gravimetric calibration of the moisture sensor achieved a mean R² of 0.975. Field testing established an irrigation hysteresis band of 22–25% volumetric water content (VWC) for pump activation and 35–38% VWC for deactivation. Over a 42-day trial, the solar subsystem maintained a mean battery state of charge of 50%, with no low-battery events, against average daily consumption of 40 Wh. A recruited farmer completed all seven user-acceptance tasks unaided, rating ease of use at 4.6\u002F5.","长蒴黄麻（Ewedu，学名Corchorus olitorius）是西非一种在营养和经济上均至关重要的叶菜，然而其在河岸地块上的旱季种植仍受限于人工、凭直觉驱动的灌溉方式，这种方式劳动强度大且在农艺上不够精确。现有的智能灌溉系统难以适用于这一场景，因为它们通常假定具备市电供电、可靠的互联网以及技术熟练的用户——而这些条件在西非河岸农场中基本不存在。为弥补这一空白，我们设计、搭建并田间测试了一套低成本、太阳能供电的物联网灌溉系统，利用实时电容式土壤水分监测为小农户实现长蒴黄麻灌溉的自动化。该系统集成了一个运行作物专用滞回逻辑的ESP32微控制器、一套直接由太阳能充电电池组供电的12V直流泵和微喷灌网络（省去了逆变器环节），以及一个农民友好、可在线和离线使用的网页仪表盘。水分传感器的重量法校准取得了平均R²为0.975的结果。田间测试确定了灌溉滞回区间：体积含水量（VWC）22–25%时启动水泵，35–38%时关闭水泵。在为期42天的试验中，太阳能子系统在日均耗电40 Wh的情况下，将电池平均荷电状态维持在50%，且未出现低电量事件。一名招募的农民在无人协助下完成了全部七项用户验收任务，并将易用性评为4.6\u002F5。","FUDMA Journal of Sciences","2026-10-08T00:00:00Z",67,{"impact":268,"substance":92,"depth":93,"authority":91,"freshness":21,"relevant":22,"comment":399},"面向西非河岸小农户的低成本太阳能物联网灌溉系统，含实地验证数据，方法扎实但影响范围限于区域。",[401],{"name":395,"url":392},[98,29,30,403,404],"太阳能","西非农业",{"code":41,"slug":48,"name":101,"path":102,"parent_code":103},[],{"status":115,"locked":14,"version":36,"method":37,"evidence":408},{"candidates":409,"agricultural_evidence":43},[410,414,418,425,429,435],{"code":41,"score":59,"supported":43,"title":411,"abstract":412,"tags":413},[48],[45,48],[30],{"code":51,"score":340,"supported":43,"title":415,"abstract":416,"tags":417},[],[29,54],[29],{"code":419,"score":79,"supported":43,"title":420,"abstract":421,"tags":424},"G2",[],[422,423],"小农户","smallholder",[],{"code":65,"score":79,"supported":43,"title":426,"abstract":427,"tags":428},[],[69,134],[],{"code":430,"score":66,"supported":43,"title":431,"abstract":432,"tags":434},"B1",[],[433],"vegetable",[],{"code":149,"score":22,"supported":14,"title":436,"abstract":437,"tags":438},[],[],[98],[440,441],"ESP32 太阳能灌溉 西非","Ewedu 土壤湿度 灌溉系统","ESP32太阳能灌溉西非-4568","10.33003\u002Ffjs-2026-1019-6198",{"doi":443,"openalex_id":445,"authors":446,"venue":395,"cited_by_count":171,"oa_url":458,"card":459,"direction":176,"ingested_from":178},"W7221041404",[447,450,453,455],{"name":448,"orcid":449},"Izuchukwu Okafor","https:\u002F\u002Forcid.org\u002F0009-0003-8668-3155",{"name":451,"orcid":452},"Afeez Adewale","https:\u002F\u002Forcid.org\u002F0009-0009-7028-9237",{"name":454,"orcid":8},"Olawale Olugbenga Olarewaju",{"name":456,"orcid":457},"Mobolaji Oluyimika Omobowale","https:\u002F\u002Forcid.org\u002F0000-0002-7108-7188","https:\u002F\u002Ffjs.fudutsinma.edu.ng\u002Findex.php\u002Ffjs\u002Farticle\u002Fdownload\u002F6198\u002F4077",{"tldr":460,"method":461,"finding":462,"direction":176,"opportunity":463},"为西非河岸农场设计并验证低成本太阳能物联网灌溉系统，自动灌溉黄麻叶。","ESP32、电容式土壤湿度传感器、太阳能直驱水泵、离线网页仪表盘，42天田间试验","湿度传感器R²达0.975，灌溉阈值22-25%启动、35-38%停止，电池无低压事件，农户易用性4","可探索无电网、无网络环境下低成本IoT灌溉的作物通用化与长期可靠性验证。","2026-10-10T23:30:11.359371Z",{"id":466,"title":467,"url":468,"summary":469,"summary_zh":470,"content":8,"source_name":471,"source_url":468,"published_at":472,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":187,"score_detail":473,"sources":475,"tags":477,"primary_topic":479,"secondary_topics":480,"classification":482,"search_phrases":513,"slug":516,"view_count":22,"doi":517,"paper":518,"created_at":533},4420,"AI-Enabled Sustainable Precision Farming Using Raspberry Pi and Coral Edge TPU with Cloud-Based Model Training","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85019","Precision Agriculture requires intelligent, adaptable and efficient technology that is capable of making decisions in agriculture at the right time, based on the condition of the plants and the environment. This paper proposes the development of an AI-based sustainable precision farming system based on computer vision, cloud based model training, Raspberry Pi, Coral Edge TPU and the Internet of Things (IoT) sensing. The proposed architecture's sensing layer, edge processing layer, cloud intelligence layer, and application layer enables a complete data processing pipeline from the field to real time decision support. Crop photos are collected for AI condition classification and agricultural conditions such as soil moisture, temperature, humidity and light intensity are collected by built-in sensors. After the above preprocessing, normalisation, image resizing and augmentation, the dataset is divided into training, validation and test sets. The trained deep learning models in a cloud environment are then quantized and converted to be optimized for the edge, following this step. Following this step, the trained deep learning models are quantized and converted for optimization on edge. The optimized model achieves an exemplary accuracy of 94.82%, precision of 94.47%, recall of 94.05% and F1-score of 94.26%. Deploying with the Coral Edge TPU reduces inference latency to approximately 38ms compared to Raspberry Pi-only processing, increases processing throughput to 26.3 pictures per second and reduces CPU usage. The edge-assisted architecture reduces the need for constant connection to the cloud, giving local decision making a leg up. Illustrative sustainability study estimates that condition based monitoring can save 22% of the irrigation water used every day. The findings indicated that the introduction of the edge AI and cloud intelligence in precision agricultural systems that are responsive, scalable, and resource-efficient is technically possible.","精准农业需要智能、自适应且高效的技术，能够根据植物和环境状况在恰当时机做出农业决策。本文提出开发一种基于人工智能的可持续精准农业系统，该系统依托计算机视觉、基于云端的模型训练、树莓派（Raspberry Pi）、Coral边缘TPU（Edge TPU）以及物联网（IoT）传感技术。所提出的架构包含感知层、边缘处理层、云智能层和应用层，实现了从田间到实时决策支持的完整数据处理流水线。系统采集作物照片用于AI状态分类，并通过内置传感器采集土壤湿度、温度、空气湿度和光照强度等农业环境条件。经过上述预处理、归一化、图像缩放和数据增强后，数据集被划分为训练集、验证集和测试集。随后，在云环境中训练好的深度学习模型经过量化和转换，针对边缘设备进行优化。优化后的模型达到了94.82%的准确率、94.47%的精确率、94.05%的召回率和94.26%的F1分数，表现优异。与仅使用树莓派处理相比，部署Coral边缘TPU将推理延迟降低至约38毫秒，将处理吞吐量提升至每秒26.3张图片，并降低了CPU占用率。该边缘辅助架构减少了对云端持续连接的需求，提升了本地决策能力。一项示例性可持续性研究估计，基于状态的监测可节省22%的每日灌溉用水量。研究结果表明，在精准农业系统中引入边缘AI和云智能，构建响应迅速、可扩展且资源高效的农业系统，在技术上是可行的。","International Journal for Research in Applied Science and Engineering Technology","2026-10-07T00:00:00Z",{"impact":268,"substance":17,"depth":59,"authority":79,"freshness":268,"relevant":22,"comment":474},"技术路线完整、指标具体，但属单篇学术论文，产业影响有限，可作为智慧农业技术参考而非每日精选头条。",[476],{"name":471,"url":468},[98,99,27,29,478],"精准灌溉",{"code":51,"slug":111,"name":112,"path":113,"parent_code":109},[481],{"code":58,"slug":106,"name":107,"path":108,"parent_code":109},{"status":115,"locked":14,"version":36,"method":37,"evidence":483},{"candidates":484,"agricultural_evidence":43},[485,490,499,503,509],{"code":51,"score":486,"supported":43,"title":487,"abstract":488,"tags":489},11,[],[29,213,54],[29,27],{"code":58,"score":13,"supported":43,"title":491,"abstract":492,"tags":498},[],[493,494,495,496,497],"深度学习","计算机视觉","deep learning","computer vision","decision support",[99],{"code":41,"score":340,"supported":43,"title":500,"abstract":501,"tags":502},[],[45,48],[478],{"code":149,"score":340,"supported":43,"title":504,"abstract":505,"tags":508},[],[506,507],"精准农业","precision agriculture",[98],{"code":65,"score":79,"supported":43,"title":510,"abstract":511,"tags":512},[],[69,134],[],[514,515],"Raspberry Pi Coral Edge TPU 精准农业","边缘AI 作物分类 灌溉节水","RaspberryPiCoralEdgeTPU精准农业-4420","10.22214\u002Fijraset.2026.85019",{"doi":517,"openalex_id":519,"authors":520,"venue":471,"cited_by_count":171,"oa_url":468,"card":528,"direction":176,"ingested_from":178},"W7220855887",[521,524,526],{"name":522,"orcid":523},"Prakash Kanade","https:\u002F\u002Forcid.org\u002F0000-0001-6296-9368",{"name":525,"orcid":8},"Shetty Chirag",{"name":527,"orcid":8},"Sunay Kanade",{"tldr":529,"method":530,"finding":531,"direction":176,"opportunity":532},"提出基于树莓派与Coral Edge TPU的边缘AI精准农业系统，实现作物状态分类与实时决策。","计算机视觉、云端模型训练与量化、边缘TPU部署、IoT多传感器融合。","模型准确率94.82%，推理延迟约38ms，条件监测可节水22%。","可探索多作物泛化、边缘模型自适应更新及水肥药协同优化，并量化长期可持续效益。","2026-10-08T23:30:09.663393Z"]