[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3158":3,"related-3158":54},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":53},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。",null,"TELKOMNIKA (Telecommunication Computing Electronics and Control)","2026-09-21T00:00:00Z","论文",10,false,58,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,16,14,12,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","物联网","智能灌溉","土壤墒情","节水农业",[32,33],"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",0,"10.12928\u002Ftelkomnika.v24i5.27889",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":46,"direction":50,"ingested_from":52},"W7213907872",[40,42,44],{"name":41,"orcid":9},"Zakarie Abdi Mohamud",{"name":43,"orcid":9},"Rozeha Binti A. Rashid",{"name":45,"orcid":9},"Yazid Abubakar Sufyan",{"tldr":47,"method":48,"finding":49,"direction":50,"opportunity":51},"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。","智慧农业 \u002F 农业物联网","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。","openalex","2026-09-22T23:30:10.956765Z",{"total":55,"page":21,"page_size":55,"items":56},6,[57,91,124,164,203,243],{"id":58,"title":59,"url":60,"summary":61,"summary_zh":62,"content":9,"source_name":63,"source_url":60,"published_at":64,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":65,"score_detail":66,"sources":70,"tags":72,"search_phrases":73,"slug":76,"view_count":35,"doi":77,"paper":78,"created_at":90},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":19,"depth":67,"authority":68,"freshness":13,"relevant":21,"comment":69},13,5,"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。",[71],{"name":63,"url":60},[26,27,28,29,30],[74,75],"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":77,"openalex_id":79,"authors":80,"venue":63,"cited_by_count":35,"oa_url":60,"card":85,"direction":50,"ingested_from":52},"W7213462460",[81,83],{"name":82,"orcid":9},"Rifki Aldiansyah",{"name":84,"orcid":9},"Dani Sasmoko",{"tldr":86,"method":87,"finding":88,"direction":50,"opportunity":89},"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":92,"title":93,"url":94,"summary":95,"summary_zh":96,"content":9,"source_name":97,"source_url":94,"published_at":98,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":99,"score_detail":100,"sources":104,"tags":106,"search_phrases":108,"slug":111,"view_count":35,"doi":112,"paper":113,"created_at":123},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":18,"substance":101,"depth":102,"authority":20,"freshness":17,"relevant":21,"comment":103},21,17,"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。",[105],{"name":97,"url":94},[26,27,28,107,30],"精准农业",[109,110],"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":112,"openalex_id":114,"authors":115,"venue":97,"cited_by_count":35,"oa_url":9,"card":118,"direction":50,"ingested_from":52},"W7213308969",[116],{"name":117,"orcid":9},"M. Rathamani",{"tldr":119,"method":120,"finding":121,"direction":50,"opportunity":122},"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",{"id":125,"title":126,"url":127,"summary":128,"summary_zh":129,"content":9,"source_name":130,"source_url":127,"published_at":131,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":132,"score_detail":133,"sources":136,"tags":138,"search_phrases":141,"slug":144,"view_count":35,"doi":145,"paper":146,"created_at":163},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":101,"depth":102,"authority":67,"freshness":134,"relevant":21,"comment":135},9,"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。",[137],{"name":130,"url":127},[26,139,140,27,28],"农业人工智能","机器学习",[142,143],"智能灌溉 水泵控制 物联网","农业人工智能 智慧农业 智能灌溉 机器学习","智能灌溉水泵控制物联网-2926","10.11591\u002Fijece.v16i5.pp2652-2663",{"doi":145,"openalex_id":147,"authors":148,"venue":130,"cited_by_count":35,"oa_url":127,"card":158,"direction":50,"ingested_from":52},"W7213546497",[149,152,155],{"name":150,"orcid":151},"Sarra Gourari","https:\u002F\u002Forcid.org\u002F0009-0005-1846-0886",{"name":153,"orcid":154},"Wafa Difallah","https:\u002F\u002Forcid.org\u002F0000-0002-6181-0395",{"name":156,"orcid":157},"Belkacem Draoui","https:\u002F\u002Forcid.org\u002F0000-0001-5490-3991",{"tldr":159,"method":160,"finding":161,"direction":50,"opportunity":162},"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":165,"title":166,"url":167,"summary":168,"summary_zh":169,"content":9,"source_name":170,"source_url":167,"published_at":171,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":172,"score_detail":173,"sources":175,"tags":177,"search_phrases":179,"slug":181,"view_count":35,"doi":182,"paper":183,"created_at":202},2654,"Design and Implementation of a Low-Cost GNSSBased Precision Agricultural Monitoring System with Real-Time IoT Visualization","https:\u002F\u002Fdoi.org\u002F10.38124\u002Fijisrt\u002F26aug1576","Precision agriculture provides an effective approach for improving agricultural productivity through spatially and temporally informed monitoring of soil, environmental, and crop conditions. However, many precision agriculture technologies remain relatively expensive and technically complex for small- and medium-scale farmers, particularly in developing countries. This paper presents a low-cost GNSS-based precision agricultural monitoring system integrating an ESP32, agricultural sensors, IoT communication, and real-time web visualization. The system acquires soil moisture, temperature, relative humidity, and GNSS data and transmits location-tagged measurements to a web server for remote monitoring.","精准农业通过基于空间和时间信息的土壤、环境及作物状况监测，为提高农业生产率提供了有效途径。然而，许多精准农业技术对中小规模农户而言仍然相对昂贵且技术复杂，尤其是在发展中国家。本文提出了一种基于GNSS的低成本精准农业监测系统，集成了ESP32、农业传感器、物联网通信和实时网页可视化。该系统采集土壤水分、温度、相对湿度和GNSS数据，并将带有位置标记的测量数据传输至网页服务器以实现远程监测。","International Journal of Innovative Science and Research Technology (IJISRT)","2026-09-14T00:00:00Z",52,{"impact":17,"substance":18,"depth":19,"authority":55,"freshness":17,"relevant":21,"comment":174},"面向中小农户的低成本GNSS+ESP32物联网监测方案，方法具体但属单篇应用型论文，影响范围有限，可作为智慧农业技术案例收录。",[176],{"name":170,"url":167},[26,27,107,29,178],"GNSS",[180,75],"土壤墒情 智慧农业 精准农业 物联网","土壤墒情智慧农业精准农业物联网-2654","10.38124\u002Fijisrt\u002F26aug1576",{"doi":182,"openalex_id":184,"authors":185,"venue":170,"cited_by_count":35,"oa_url":196,"card":197,"direction":50,"ingested_from":52},"W7213355767",[186,188,190,192,194],{"name":187,"orcid":9},"D. Z. Zakut",{"name":189,"orcid":9},"O. B. Goodtalk",{"name":191,"orcid":9},"A. N. Lawal",{"name":193,"orcid":9},"S. A. Yusuf",{"name":195,"orcid":9},"I. Isa","https:\u002F\u002Fwww.ijisrt.com\u002Fassets\u002Fupload\u002Ffiles\u002FIJISRT26AUG1576.pdf",{"tldr":198,"method":199,"finding":200,"direction":50,"opportunity":201},"设计并实现了一套低成本GNSS精准农业监测系统，集成ESP32与物联网实时可视化。","采用ESP32、农业传感器、GNSS模块与IoT通信，采集土壤水分、温湿度等数据","系统能以低成本实现位置标记的农田环境实时远程监测，适合中小农户。","可进一步研究低功耗长期部署、多源数据融合与面向小农户的智能预警决策模型。","2026-09-16T23:30:17.408536Z",{"id":204,"title":205,"url":206,"summary":207,"summary_zh":208,"content":9,"source_name":209,"source_url":206,"published_at":171,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":210,"score_detail":211,"sources":214,"tags":216,"search_phrases":219,"slug":222,"view_count":35,"doi":223,"paper":224,"created_at":242},2510,"A Low-Power, IP68-Compliant, BLE-Based Multi-Station Smart Irrigation Control Unit for Agricultural Applications","https:\u002F\u002Fdoi.org\u002F10.47115\u002Fbsagriculture.2004639","This study designs, develops, and evaluates the performance of a low-power, Bluetooth Low Energy (BLE) based, IP68 protection-rated, multi-station smart irrigation control unit for agricultural irrigation applications. The system is built on an STM32F103 microcontroller-based electronic control board and can manage 6 independent irrigation stations. Irrigation is automatically controlled by evaluating environmental data obtained from rain, frost, and wind sensors, and it is designed to be compatible with different types of rain sensors. Thanks to the user-definable cycle & soak algorithm, irrigation is performed in specific cycles, and a waiting period is applied after each cycle to allow the soil to absorb the water, aiming to increase water use efficiency. The control unit can be managed remotely from mobile devices compatible with common mobile operating systems via a mobile application using BLE technology, up to approximately 15 m; irrigation schedules can be created, past irrigation records can be viewed, and manual control operations can be performed remotely. The system is designed to operate with a 9V alkaline battery (550 mAh), offering approximately 6 months of continuous operation with a single battery and approximately 1 year with two batteries. The improved control unit, with its IP68 protection class housing, provides high durability against harsh environmental conditions. Its multi-station structure, low power consumption, and user-friendly features offer an integrated solution for smart irrigation applications.","本研究设计、开发并评估了一种用于农业灌溉应用的低功耗、基于低功耗蓝牙（BLE）、具有IP68防护等级的多站智能灌溉控制单元的性能。该系统构建于基于STM32F103微控制器的电子控制板上，可管理6个独立的灌溉站。通过评估来自雨量、霜冻和风速传感器的环境数据来自动控制灌溉，并设计为兼容不同类型的雨量传感器。借助用户可定义的循环与浸润算法，灌溉按特定周期进行，每个周期后设置等待期以使土壤吸收水分，旨在提高水资源利用效率。该控制单元可通过使用BLE技术的移动应用程序，从兼容常见移动操作系统的移动设备上进行远程管理，通信距离可达约15米；可创建灌溉计划、查看历史灌溉记录，并远程执行手动控制操作。该系统设计为使用9V碱性电池（550 mAh）运行，单节电池可连续运行约6个月，两节电池可连续运行约1年。改进后的控制单元采用IP68防护等级外壳，对恶劣环境条件具有高耐久性。其多站结构、低功耗和用户友好特性为智能灌溉应用提供了一体化解决方案。","Black Sea Journal of Agriculture",67,{"impact":20,"substance":212,"depth":18,"authority":20,"freshness":134,"relevant":21,"comment":213},18,"面向农业灌溉的低功耗BLE多站控制单元，方法完整、指标具体，但属细分技术方案，产业影响有限。",[215],{"name":209,"url":206},[26,28,217,30,218],"农业传感器","低功耗物联网",[220,221],"低功耗物联网 农业传感器 智慧农业 智能灌溉","低功耗物联网 农业传感器","低功耗物联网农业传感器智慧农业智能灌溉-2510","10.47115\u002Fbsagriculture.2004639",{"doi":223,"openalex_id":225,"authors":226,"venue":209,"cited_by_count":35,"oa_url":236,"card":237,"direction":50,"ingested_from":52},"W7212619308",[227,230,233],{"name":228,"orcid":229},"Samet Eyicil","https:\u002F\u002Forcid.org\u002F0009-0003-4651-6897",{"name":231,"orcid":232},"Yavuz Turan","https:\u002F\u002Forcid.org\u002F0000-0001-8537-3989",{"name":234,"orcid":235},"İbrahim Keleş","https:\u002F\u002Forcid.org\u002F0000-0001-8252-2635","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F6270760",{"tldr":238,"method":239,"finding":240,"direction":50,"opportunity":241},"设计并评估了一款低功耗、IP68防护、基于BLE的六站智能灌溉控制单元。","基于STM32F103与BLE，结合雨霜风传感器及循环浸泡算法，用9V电池供电。","单电池续航约6个月，双电池约1年，BLE控制距离约15米，IP68外壳耐恶劣环境。","可探索多传感器融合的灌溉决策与太阳能供电，延长野外部署寿命并提升节水效率。","2026-09-15T23:30:08.453960Z",{"id":244,"title":245,"url":246,"summary":247,"summary_zh":248,"content":9,"source_name":249,"source_url":246,"published_at":250,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":251,"score_detail":252,"sources":255,"tags":257,"search_phrases":259,"slug":262,"view_count":35,"doi":263,"paper":264,"created_at":280},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":212,"substance":253,"depth":102,"authority":20,"freshness":17,"relevant":21,"comment":254},20,"AIoT+LSTM 预测灌溉的完整实证研究，90 天试验与节水、能耗数据扎实，对精准农业落地有参考价值，但期刊层级与影响范围偏细分领域。",[256],{"name":249,"url":246},[26,139,27,28,107,258],"土壤监测",[260,261],"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":263,"openalex_id":265,"authors":266,"venue":249,"cited_by_count":35,"oa_url":246,"card":275,"direction":50,"ingested_from":52},"W7212115545",[267,269,271,273],{"name":268,"orcid":9},"Gowri M.",{"name":270,"orcid":9},"Boomika M.",{"name":272,"orcid":9},"S. Rakshana",{"name":274,"orcid":9},"Rubali R.",{"tldr":276,"method":277,"finding":278,"direction":50,"opportunity":279},"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"]