[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4568":3,"related-4568":115},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":86,"slug":89,"view_count":90,"doi":91,"paper":92,"created_at":114},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。",null,"FUDMA Journal of Sciences","2026-10-08T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,21,17,12,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","物联网","智能灌溉","太阳能","西非农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,59,67,74,81],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},15,true,[34],[50,34],"灌溉",[29],{"code":53,"score":54,"supported":47,"title":55,"abstract":56,"tags":58},"I5",7,[],[28,57],"IoT",[28],{"code":60,"score":61,"supported":47,"title":62,"abstract":63,"tags":66},"G2",6,[],[64,65],"小农户","smallholder",[],{"code":68,"score":61,"supported":47,"title":69,"abstract":70,"tags":73},"H2",[],[71,72],"土壤","soil",[],{"code":75,"score":76,"supported":47,"title":77,"abstract":78,"tags":80},"B1",3,[],[79],"vegetable",[],{"code":82,"score":22,"supported":14,"title":83,"abstract":84,"tags":85},"I1",[],[],[27],[87,88],"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",0,"10.33003\u002Ffjs-2026-1019-6198",{"doi":91,"openalex_id":93,"authors":94,"venue":10,"cited_by_count":90,"oa_url":106,"card":107,"direction":111,"ingested_from":113},"W7221041404",[95,98,101,103],{"name":96,"orcid":97},"Izuchukwu Okafor","https:\u002F\u002Forcid.org\u002F0009-0003-8668-3155",{"name":99,"orcid":100},"Afeez Adewale","https:\u002F\u002Forcid.org\u002F0009-0009-7028-9237",{"name":102,"orcid":9},"Olawale Olugbenga Olarewaju",{"name":104,"orcid":105},"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":108,"method":109,"finding":110,"direction":111,"opportunity":112},"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","智慧农业 \u002F 农业物联网","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。","openalex","2026-10-10T23:30:11.359371Z",{"total":61,"page":22,"page_size":61,"items":116},[117,182,237,308,390,445],{"id":118,"title":119,"url":120,"summary":121,"summary_zh":122,"content":9,"source_name":123,"source_url":120,"published_at":124,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":125,"score_detail":126,"sources":130,"tags":132,"primary_topic":135,"secondary_topics":136,"classification":142,"search_phrases":164,"slug":167,"view_count":22,"doi":168,"paper":169,"created_at":181},4296,"Smart Irrigation System for Precision Farming","https:\u002F\u002Fdoi.org\u002F10.17148\u002Fijarcce.2026.151010","Smart Irrigation System for Precision Farming is anIoT-based and sensor-driven approach designed to improve water management, crop productivity, and agricultural sustainability.Conventional irrigation practices often depend on fixed schedules or manual decisions, which can result in over-irrigation, under-irrigation, water wastage, and increased labour requirements.The proposed system integrates soil-moisture, temperature, humidity, and rainfall sensors with a microcontroller such as Arduino or ESP32 to continuously monitor field conditions in real time.Based on the collected data, the system automatically determines the irrigation requirement and controls water pumps or solenoid valves accordingly.Weather information can also be incorporated to avoid unnecessary irrigation during expected rainfall and to adapt watering according to environmental conditions.IoT connectivity enables farmers to remotely monitor soil and weather parameters through a web or mobile dashboard.The system provides a low-cost, scalable, and farmer-friendly solution that supports precision agriculture by delivering the right amount of water at the right time.Such intelligent irrigation can conserve water and energy, reduce manual effort, improve crop health, and contribute to sustainable agricultural development. IJARCCE","基于物联网和传感器的精准农业智能灌溉系统，旨在改善水资源管理、作物生产力和农业可持续性。传统灌溉方式往往依赖固定时间表或人工决策，可能导致过度灌溉、灌溉不足、水资源浪费以及劳动力需求增加。所提出的系统将土壤湿度、温度、湿度和降雨传感器与Arduino或ESP32等微控制器集成，以实时持续监测田间状况。基于采集的数据，系统自动判断灌溉需求，并相应控制水泵或电磁阀。系统还可纳入天气信息，以避免在预计降雨期间进行不必要的灌溉，并根据环境条件调整浇水。物联网连接使农民能够通过网络或移动仪表板远程监测土壤和天气参数。该系统提供了一种低成本、可扩展且对农民友好的解决方案，通过在恰当的时间提供恰当的水量来支持精准农业。这种智能灌溉能够节约水和能源、减少人工劳动、改善作物健康，并促进农业可持续发展。IJARCCE","IJARCCE","2026-10-05T00:00:00Z",49,{"impact":17,"substance":127,"depth":128,"authority":61,"freshness":17,"relevant":22,"comment":129},14,13,"面向精准农业的物联网智能灌溉系统论文，方法常规、缺乏大规模实测数据，属细分领域技术进展，公共价值有限。",[131],{"name":123,"url":120},[27,28,29,133,134],"精准农业","传感器",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[137],{"code":53,"slug":138,"name":139,"path":140,"parent_code":141},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":143},{"candidates":144,"agricultural_evidence":47},[145,151,156,160],{"code":33,"score":146,"supported":47,"title":147,"abstract":148,"tags":150},18,[34],[50,149,34],"水资源",[29],{"code":82,"score":17,"supported":47,"title":152,"abstract":153,"tags":155},[],[133,154],"precision agriculture",[27,133],{"code":53,"score":17,"supported":47,"title":157,"abstract":158,"tags":159},[],[28,57],[134,28],{"code":68,"score":61,"supported":47,"title":161,"abstract":162,"tags":163},[],[71,72],[],[165,166],"智能灌溉系统 精准农业","物联网 土壤湿度传感器 灌溉","智能灌溉系统精准农业-4296","10.17148\u002Fijarcce.2026.151010",{"doi":168,"openalex_id":170,"authors":171,"venue":123,"cited_by_count":90,"oa_url":120,"card":176,"direction":111,"ingested_from":113},"W7220437851",[172,174],{"name":173,"orcid":9},"Mr. Bhagyesh N Chaudhari",{"name":175,"orcid":9},"Dr. Dinesh D Puri",{"tldr":177,"method":178,"finding":179,"direction":111,"opportunity":180},"基于物联网与传感器构建智能灌溉系统，实时监测土壤与气象并自动控制灌溉。","用Arduino\u002FESP32连接土壤湿度、温湿度、降雨传感器及物联网仪表盘。","系统可按需精准供水，节水节能、减少人工并提升作物健康与可持续性。","可延伸至多源数据融合的灌溉决策优化、边缘智能控制及大田规模化验证。","2026-10-06T23:30:07.841226Z",{"id":183,"title":184,"url":185,"summary":186,"summary_zh":187,"content":9,"source_name":188,"source_url":185,"published_at":189,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":190,"score_detail":191,"sources":193,"tags":195,"primary_topic":198,"secondary_topics":199,"classification":200,"search_phrases":217,"slug":220,"view_count":76,"doi":221,"paper":222,"created_at":236},3540,"Smart Drip Irrigation Maintains Phytosanitary Superiority under Dry Season Conditions in Shaded Cultivation of Celery (Apium graveolens L.) in Guyana","https:\u002F\u002Fdoi.org\u002F10.18805\u002Fijare.af-1081","Celery (Apium graveolens L.) is a commercially important crop in Guyana especially with the advancing of shaded cultivation as part of the national Climate-Smart Agriculture agenda. A companion study evaluating smart drip and sprinkler irrigation during the wet season (April-June 2025) demonstrated that smart drip irrigation significantly reduced foliar disease incidence while maintaining equivalent biomass yields. This study extends the investigation to the dry season (July-October 2025) to determine whether the phytosanitary and yield advantages of smart drip irrigation are sustained. A two-treatment, shade house trial (n = 6 beds per treatment, 504 plants per treatment) was conducted using an automated IoT irrigation system. No statistically significant difference in plant height, leaf length, leaf width or fresh biomass was detected between treatments (p greater than 0.05 for all parameters). Smart drip irrigation yielded a disease incidence of 2.8% compared to 9.3% under sprinkler irrigation (Fisher’s exact test: p = 1.30 x 10-9 and odds ratio = 0.282). These findings confirm that the phytosanitary superiority of smart drip irrigation is sustained across seasons, supporting its utilization for year-round shade house celery production in tropical contexts.","芹菜（Apium graveolens L.）是圭亚那具有重要商业价值的作物，尤其是在遮阴栽培作为国家气候智慧型农业议程的一部分不断推进的背景下。一项评估雨季（2025年4月至6月）智能滴灌与喷灌的配套研究表明，智能滴灌显著降低了叶部病害发生率，同时保持了同等的生物量产量。本研究将调查扩展至旱季（2025年7月至10月），以确定智能滴灌在植物检疫和产量方面的优势是否能够持续。采用自动化物联网灌溉系统开展了双处理遮阴棚试验（每处理6个苗床，n = 6；每处理504株植物）。两种处理间在株高、叶长、叶宽或鲜生物量方面均未检测到统计学显著差异（所有参数p大于0.05）。智能滴灌的病害发生率为2.8%，而喷灌为9.3%（Fisher精确检验：p = 1.30 x 10-9，优势比 = 0.282）。这些发现证实，智能滴灌在植物检疫方面的优越性在不同季节间均能持续，支持其用于热带地区遮阴棚芹菜的全年生产。","Indian Journal of Agricultural Research","2026-09-25T00:00:00Z",71,{"impact":20,"substance":18,"depth":19,"authority":128,"freshness":17,"relevant":22,"comment":192},"热带遮阴栽培下智能滴灌跨季节保持植保优势的试验论文，数据扎实但属区域性细分研究，公共影响有限。",[194],{"name":188,"url":185},[27,28,29,196,197],"设施栽培","芹菜",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":201},{"candidates":202,"agricultural_evidence":47},[203,207,211],{"code":33,"score":46,"supported":47,"title":204,"abstract":205,"tags":206},[34],[50,34],[29],{"code":53,"score":54,"supported":47,"title":208,"abstract":209,"tags":210},[],[28,57],[28],{"code":82,"score":212,"supported":47,"title":213,"abstract":214,"tags":216},4,[],[215],"smart agriculture",[27],[218,219],"圭亚那 芹菜 智能滴灌","IoT 灌溉 遮阴栽培","圭亚那芹菜智能滴灌-3540","10.18805\u002Fijare.af-1081",{"doi":221,"openalex_id":223,"authors":224,"venue":188,"cited_by_count":90,"oa_url":185,"card":231,"direction":111,"ingested_from":113},"W7214362492",[225,227,229],{"name":226,"orcid":9},"Joel Persaud",{"name":228,"orcid":9},"Pawan Kumar Persaud",{"name":230,"orcid":9},"Andrea Dookhu",{"tldr":232,"method":233,"finding":234,"direction":111,"opportunity":235},"研究验证旱季遮阴芹菜中智能滴灌能否保持病害防控与产量优势。","物联网自动灌溉系统，遮阴棚两处理对比试验，统计检验。","智能滴灌与喷灌产量无差异，但病害发生率显著更低（2.8% vs 9.3%）。","可探索智能滴灌在热带遮阴栽培中跨季节、跨作物的病害抑制机制与长期经济性。","2026-09-26T23:30:09.889320Z",{"id":238,"title":239,"url":240,"summary":241,"summary_zh":242,"content":9,"source_name":243,"source_url":240,"published_at":244,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":245,"score_detail":246,"sources":249,"tags":251,"primary_topic":254,"secondary_topics":255,"classification":260,"search_phrases":287,"slug":290,"view_count":291,"doi":292,"paper":293,"created_at":307},3158,"Internet of things-based smart irrigation system using soil moisture and weather data","https:\u002F\u002Fdoi.org\u002F10.12928\u002Ftelkomnika.v24i5.27889","Water wastage in agriculture remains a significant challenge due to irrigation practices that often rely on fixed schedules rather than actual field conditions. This study presents an internet of things (IoT)-based smart irrigation system designed to improve water-use efficiency through real-time monitoring and automated irrigation control. The system integrates a capacitive soil moisture sensor with weather information obtained from an online application programming interface (API), while all data processing is performed locally on a Raspberry Pi edge device. A rule-based decision mechanism is used to classify soil conditions into dry, optimal, and wet categories and to determine appropriate irrigation actions based on soil moisture levels and rainfall forecasts. The system was implemented using low-cost and readily available components and tested under controlled conditions with soil moisture levels ranging from approximately 0% to above 85%. Experimental results showed consistent classification of critical dry, optimal, and critical wet conditions, enabling appropriate irrigation responses under different scenarios. In addition, email notifications were generated only during critical conditions, while no alerts were triggered under optimal moisture levels, demonstrating stable and reliable operation. The proposed system provides a practical and cost-effective solution for supporting efficient irrigation management and sustainable agricultural practices.","农业中的水资源浪费仍然是一项重大挑战，因为灌溉实践往往依赖固定时间表，而非实际田间条件。本研究提出了一种基于物联网（IoT）的智能灌溉系统，旨在通过实时监测和自动灌溉控制来提高用水效率。该系统将电容式土壤湿度传感器与从在线应用程序编程接口（API）获取的天气信息相结合，同时所有数据处理均在Raspberry Pi边缘设备上本地完成。系统采用基于规则的决策机制，将土壤状况分为干燥、适宜和湿润三类，并根据土壤湿度水平和降雨预报确定适当的灌溉措施。该系统使用低成本和易于获取的组件实现，并在受控条件下进行了测试，土壤湿度水平范围约为0%至85%以上。实验结果表明，系统能够一致地分类临界干燥、适宜和临界湿润状况，从而在不同情景下实现适当的灌溉响应。此外，电子邮件通知仅在临界状况下生成，而在适宜湿度水平下未触发任何警报，表明系统运行稳定可靠。所提出的系统为支持高效灌溉管理和可持续农业实践提供了一种实用且具有成本效益的解决方案。","TELKOMNIKA (Telecommunication Computing Electronics and Control)","2026-09-21T00:00:00Z",58,{"impact":17,"substance":247,"depth":127,"authority":20,"freshness":17,"relevant":22,"comment":248},16,"低成本物联网智能灌溉系统，方法清晰、结论可靠，对节水农业有实用参考价值，但属常规技术验证类论文，影响范围有限。",[250],{"name":243,"url":240},[27,28,29,252,253],"土壤墒情","节水农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[256,257],{"code":53,"slug":138,"name":139,"path":140,"parent_code":141},{"code":68,"slug":72,"name":258,"path":259,"parent_code":37},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"status":40,"locked":14,"version":41,"method":42,"evidence":261},{"candidates":262,"agricultural_evidence":47},[263,268,273,277,283],{"code":33,"score":264,"supported":47,"title":265,"abstract":266,"tags":267},19,[34],[50,149,34],[29,253],{"code":53,"score":146,"supported":47,"title":269,"abstract":271,"tags":272},[270],"internet of things",[28,270,57],[28],{"code":68,"score":46,"supported":47,"title":274,"abstract":275,"tags":276},[72],[71,72],[252],{"code":278,"score":76,"supported":47,"title":279,"abstract":280,"tags":282},"H8",[],[281],"可持续农业",[],{"code":82,"score":22,"supported":14,"title":284,"abstract":285,"tags":286},[],[],[27],[288,289],"IoT 智能灌溉 土壤湿度","Raspberry Pi 边缘计算 灌溉","IoT智能灌溉土壤湿度-3158",5,"10.12928\u002Ftelkomnika.v24i5.27889",{"doi":292,"openalex_id":294,"authors":295,"venue":243,"cited_by_count":90,"oa_url":240,"card":302,"direction":111,"ingested_from":113},"W7213907872",[296,298,300],{"name":297,"orcid":9},"Zakarie Abdi Mohamud",{"name":299,"orcid":9},"Rozeha Binti A. Rashid",{"name":301,"orcid":9},"Yazid Abubakar Sufyan",{"tldr":303,"method":304,"finding":305,"direction":111,"opportunity":306},"基于物联网与土壤湿度及天气数据，实现低成本自动灌溉决策系统。","电容式土壤湿度传感器、天气API、Raspberry Pi边缘计算与规则决策。","系统能准确分类干、适宜、湿状态，仅在临界条件触发灌溉与邮件通知。","可引入机器学习预测土壤湿度动态，优化规则阈值并扩展至多作物多区域验证。","2026-09-22T23:30:10.956765Z",{"id":309,"title":310,"url":311,"summary":312,"summary_zh":313,"content":9,"source_name":314,"source_url":311,"published_at":315,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":316,"score_detail":317,"sources":319,"tags":321,"primary_topic":324,"secondary_topics":325,"classification":332,"search_phrases":366,"slug":369,"view_count":370,"doi":371,"paper":372,"created_at":389},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":20,"substance":18,"depth":19,"authority":128,"freshness":21,"relevant":22,"comment":318},"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。",[320],{"name":314,"url":311},[27,322,323,28,29],"农业人工智能","机器学习",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[326,331],{"code":327,"slug":328,"name":329,"path":330,"parent_code":141},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":53,"slug":138,"name":139,"path":140,"parent_code":141},{"status":40,"locked":14,"version":41,"method":42,"evidence":333},{"candidates":334,"agricultural_evidence":47},[335,339,344,348,352,358,362],{"code":33,"score":146,"supported":47,"title":336,"abstract":337,"tags":338},[34],[50,149,34],[29],{"code":327,"score":128,"supported":47,"title":340,"abstract":342,"tags":343},[341],"artificial intelligence",[323],[322,323],{"code":53,"score":21,"supported":47,"title":345,"abstract":346,"tags":347},[57],[],[28],{"code":68,"score":61,"supported":47,"title":349,"abstract":350,"tags":351},[],[71,72],[],{"code":353,"score":76,"supported":47,"title":354,"abstract":355,"tags":357},"H7",[],[356],"干旱",[],{"code":278,"score":76,"supported":47,"title":359,"abstract":360,"tags":361},[],[281],[],{"code":82,"score":22,"supported":14,"title":363,"abstract":364,"tags":365},[],[],[27],[367,368],"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":371,"openalex_id":373,"authors":374,"venue":314,"cited_by_count":90,"oa_url":311,"card":384,"direction":111,"ingested_from":113},"W7213546497",[375,378,381],{"name":376,"orcid":377},"Sarra Gourari","https:\u002F\u002Forcid.org\u002F0009-0005-1846-0886",{"name":379,"orcid":380},"Wafa Difallah","https:\u002F\u002Forcid.org\u002F0000-0002-6181-0395",{"name":382,"orcid":383},"Belkacem Draoui","https:\u002F\u002Forcid.org\u002F0000-0001-5490-3991",{"tldr":385,"method":386,"finding":387,"direction":111,"opportunity":388},"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":391,"title":392,"url":393,"summary":394,"summary_zh":395,"content":9,"source_name":396,"source_url":393,"published_at":397,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":398,"score_detail":399,"sources":401,"tags":403,"primary_topic":404,"secondary_topics":405,"classification":407,"search_phrases":427,"slug":430,"view_count":370,"doi":431,"paper":432,"created_at":444},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","2026-09-17T00:00:00Z",50,{"impact":17,"substance":127,"depth":128,"authority":291,"freshness":13,"relevant":22,"comment":400},"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。",[402],{"name":396,"url":393},[27,28,29,252,253],{"code":53,"slug":138,"name":139,"path":140,"parent_code":141},[406],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":408},{"candidates":409,"agricultural_evidence":47},[410,414,419,423],{"code":53,"score":146,"supported":47,"title":411,"abstract":412,"tags":413},[57],[28,270,57],[28],{"code":33,"score":415,"supported":47,"title":416,"abstract":417,"tags":418},11,[],[50,149,34],[29,253],{"code":68,"score":54,"supported":47,"title":420,"abstract":421,"tags":422},[],[71,72],[252],{"code":82,"score":22,"supported":14,"title":424,"abstract":425,"tags":426},[],[],[27],[428,429],"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":431,"openalex_id":433,"authors":434,"venue":396,"cited_by_count":90,"oa_url":393,"card":439,"direction":111,"ingested_from":113},"W7213462460",[435,437],{"name":436,"orcid":9},"Rifki Aldiansyah",{"name":438,"orcid":9},"Dani Sasmoko",{"tldr":440,"method":441,"finding":442,"direction":111,"opportunity":443},"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",{"id":446,"title":447,"url":448,"summary":449,"summary_zh":450,"content":9,"source_name":451,"source_url":448,"published_at":452,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":453,"score_detail":454,"sources":456,"tags":458,"primary_topic":459,"secondary_topics":460,"classification":462,"search_phrases":481,"slug":484,"view_count":76,"doi":485,"paper":486,"created_at":496},2645,"IOT AND WEATHER DATA-DRIVEN SMART IRRIGATION FOR WATER-EFFICIENT CROP PRODUCTION","https:\u002F\u002Fdoi.org\u002F10.65725\u002Fjcise\u002F2\u002F3\u002F012","Agriculture is one of the major consumers of freshwater, and inefficient irrigation practices can lead to significant water wastage, reduced crop productivity, and increased cultivation costs. Conventional irrigation methods often depend on fixed schedules or manual decisions and may not adequately respond to changing soil and weather conditions. This study proposes an IoT and Weather Data-Driven Smart Irrigation System for Water-Efficient Crop Production that enables intelligent and automated irrigation based on real-time field conditions. The proposed system integrates Internet of Things (IoT) sensors such as soil-moisture, temperature, humidity, and water-level sensors with external weather information including rainfall probability and temperature forecasts. The collected data are transmitted to a central monitoring platform using an IoT-enabled controller. Based on soil moisture levels and prevailing or predicted weather conditions, the system determines the appropriate timing and duration of irrigation. When adequate soil moisture or sufficient rainfall is expected, irrigation is reduced or postponed, thereby avoiding unnecessary water consumption. The proposed system was developed and evaluated using selected agricultural crops under controlled field conditions. Its performance was assessed in terms of water consumption, irrigation efficiency, soil moisture management, crop growth, and system reliability. Experimental results demonstrate a water savings of up to 35% compared to conventional schedule-based irrigation, alongside an 18.2% improvement in soil moisture stability. The study demonstrates how the integration of IoT and weather data can support precision agriculture, sustainable water management, and improved agricultural productivity.","农业是淡水的主要消耗者之一，低效的灌溉方式可能导致严重的水资源浪费、作物生产力下降以及种植成本增加。传统灌溉方法通常依赖固定时间表或人工决策，可能无法充分应对不断变化的土壤和天气条件。本研究提出了一种基于物联网与天气数据的智能灌溉系统，用于节水型作物生产，该系统能够根据实时田间条件实现智能化和自动化灌溉。所提出的系统将物联网（IoT）传感器（如土壤湿度、温度、湿度和水位传感器）与外部天气信息（包括降雨概率和温度预报）相结合。采集的数据通过支持物联网的控制器传输至中央监控平台。系统根据土壤湿度水平以及当前或预测的天气条件，确定适当的灌溉时间和时长。当土壤湿度充足或预计有足够降雨时，灌溉会减少或推迟，从而避免不必要的水资源消耗。所提出的系统在受控田间条件下选用特定农作物进行了开发和评估。其性能从耗水量、灌溉效率、土壤湿度管理、作物生长和系统可靠性等方面进行了评估。实验结果表明，与传统基于时间表的灌溉方式相比，该系统可节水高达35%，同时土壤湿度稳定性提高了18.2%。该研究展示了物联网与天气数据的融合如何支持精准农业、可持续水资源管理以及农业生产力的提升。","RCHUB JOURNAL OF COMPUTATIONAL INTELLIGENCE SCIENCE AND ENGINEERING (JCISE)","2026-09-15T00:00:00Z",74,{"impact":247,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":22,"comment":455},"该论文提出物联网与气象数据驱动的智能灌溉系统，实测节水35%、土壤湿度稳定性提升18.2%，方法新颖且数据可靠，对智慧农业节水管理有实质参考价值，值得进入每日精选。",[457],{"name":451,"url":448},[27,28,29,133,253],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[461],{"code":53,"slug":138,"name":139,"path":140,"parent_code":141},{"status":40,"locked":14,"version":41,"method":42,"evidence":463},{"candidates":464,"agricultural_evidence":47},[465,469,473,477],{"code":33,"score":264,"supported":47,"title":466,"abstract":467,"tags":468},[34],[50,149,34],[29,253],{"code":53,"score":146,"supported":47,"title":470,"abstract":471,"tags":472},[57],[28,270,57],[28],{"code":82,"score":17,"supported":47,"title":474,"abstract":475,"tags":476},[],[133,154],[27,133],{"code":68,"score":61,"supported":47,"title":478,"abstract":479,"tags":480},[],[71,72],[],[482,483],"智慧农业 智能灌溉 精准农业 节水农业","智慧农业 智能灌溉","智慧农业智能灌溉精准农业节水农业-2645","10.65725\u002Fjcise\u002F2\u002F3\u002F012",{"doi":485,"openalex_id":487,"authors":488,"venue":451,"cited_by_count":90,"oa_url":9,"card":491,"direction":111,"ingested_from":113},"W7213308969",[489],{"name":490,"orcid":9},"M. Rathamani",{"tldr":492,"method":493,"finding":494,"direction":111,"opportunity":495},"提出物联网与天气数据驱动的智能灌溉系统，按实时土壤与气象条件自动决策灌溉。","集成土壤湿度、温湿度、水位传感器与降雨概率、温度预报，经物联网控制器决策。","相比定时灌溉节水达35%，土壤湿度稳定性提升18.2%。","可探索多源气象预报误差下的灌溉决策鲁棒性，及跨作物、跨区域的节水模型泛化。","2026-09-16T23:30:12.064334Z"]