[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1258":3,"related-1258":112},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":44,"search_phrases":89,"slug":92,"view_count":73,"doi":93,"paper":94,"created_at":111},1258,"Integration of IoT and Sensor Technologies for Sustainable Smart Irrigation Systems: Trends, Challenges, and Future Directions","https:\u002F\u002Fdoi.org\u002F10.31272\u002Fajece.35","Efficient water management is a persistent challenge in modern agriculture, especially in arid and semi-arid regions. The development and adoption of advanced soil sensor technologies are essential for optimising water use and supporting sustainable, high-yield agricultural systems. This systematic review compares primary soil sensor methodologies and synthesises findings from 156 peer-reviewed studies published between 2015 and 2025. The review assesses dielectric techniques, including time-domain reflectometry (TDR) and frequency-domain reflectometry (FDR), as well as capacitive sensors, tensiometers, neutron probes, and instruments for measuring salinity and pH. Meta-analytical results show that TDR systems achieve high accuracy (pooled RMSE: 0.016 m³\u002Fm³) but are associated with significant costs ($500–$1200 per unit). In contrast, low-cost capacitive sensors can provide acceptable accuracy (RMSE: 0.045 m³\u002Fm³) when rigorously calibrated. LoRaWAN (Long Range Wide Area Network) is identified as the most effective communication protocol for agricultural Internet of Things (IoT) applications, with transmission ranges of 2–15 km and battery lifespans of 2–10 years. The use of machine learning methods improves irrigation scheduling accuracy by 15–30%, and artificial intelligence (AI) systems can achieve water savings of 35–50%. Key research gaps include the absence of standardised calibration procedures, as 67% of studies lack comprehensive validation, limited long-term stability assessments, with only 23% reporting data beyond six months, and insufficient investigation of multi-sensor data fusion. The review concludes by identifying future research priorities to support the development of robust and scalable innovative irrigation systems for sustainable agriculture.",null,"Academic Journal of Electrical and Computer Engineering","2026-08-30T00:00:00Z","论文",10,false,81,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,24,20,13,2,1,"系统综述整合156项研究，对比多种土壤传感器与通信协议，数据详实，对智慧灌溉技术发展有重要参考价值。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业物联网","机器学习","智能灌溉","传感器",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":45,"locked":13,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,58,63,71,78,84],{"code":32,"score":51,"supported":52,"title":53,"abstract":55,"tags":57},16,true,[54],"IoT",[56,54],"internet of things",[30,27],{"code":39,"score":59,"supported":52,"title":60,"abstract":61,"tags":62},12,[40],[40],[29],{"code":64,"score":65,"supported":52,"title":66,"abstract":67,"tags":70},"I2",7,[],[68,69],"machine learning","artificial intelligence",[28],{"code":72,"score":73,"supported":52,"title":74,"abstract":75,"tags":77},"H2",3,[],[76],"soil",[],{"code":79,"score":73,"supported":52,"title":80,"abstract":81,"tags":83},"H8",[],[82],"sustainable agriculture",[],{"code":85,"score":21,"supported":13,"title":86,"abstract":87,"tags":88},"I1",[],[],[26],[90,91],"农业物联网 智慧农业 智能灌溉 机器学习","农业物联网 智慧农业","农业物联网智慧农业智能灌溉机器学习-1258","10.31272\u002Fajece.35",{"doi":93,"openalex_id":95,"authors":96,"venue":9,"cited_by_count":103,"oa_url":6,"card":104,"direction":108,"ingested_from":110},"W7204712150",[97,99,101],{"name":98,"orcid":8},"Omar Talib Khazraji",{"name":100,"orcid":8},"Marwan J. Hussein",{"name":102,"orcid":8},"Ahmed M. Almawla",0,{"tldr":105,"method":106,"finding":107,"direction":108,"opportunity":109},"系统综述2015-2025年土壤传感器与IoT在智能灌溉中的应用，评估技术、成本与性能，指出研究空白","系统综述156篇研究，比较TDR、FDR、电容等传感器，结合LoRaWAN和机器","TDR精度高但贵，电容传感器性价比好；LoRaWAN最优；AI节水35-50%；缺乏标准化校准和长期","智慧农业 \u002F 农业物联网","研究空白：多传感器数据融合、标准化校准协议、长期稳定性评估，可开发低成本高精度融合系统。","openalex","2026-09-01T04:03:11.465487Z",{"total":113,"page":21,"page_size":113,"items":114},6,[115,207,266,347,423,523],{"id":116,"title":117,"url":118,"summary":119,"summary_zh":120,"content":8,"source_name":121,"source_url":118,"published_at":122,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":123,"score_detail":124,"sources":129,"tags":131,"primary_topic":134,"secondary_topics":135,"classification":137,"search_phrases":178,"slug":181,"view_count":103,"doi":182,"paper":183,"created_at":206},5077,"A Trust-Boundary Reference Architecture for Secure Cyber-Physical Agriculture: Design and Software-Based Proof-of-Concept for Smart Irrigation","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206545","Smart irrigation is an operational technology (OT) problem as much as an Internet of Things (IoT) one: software decisions act directly on valves, pumps, pressure and water delivery, so a spoofed reading or a replayed command has physical consequences. This paper presents a technology-neutral reference architecture that makes those consequences controllable. It is organised into seven functional layers and three cross-cutting planes, six trust-boundary classes that fix which properties must be re-established at each crossing, and an auditable crown-asset inventory with an explicit trust-level rubric. Two governing execution principles enforce this structure: local safety authority, which keeps final actuation permission at the field gateway, remote terminal unit (RTU) or programmable logic controller (PLC), and feedback-aware execution, which treats a command as complete only when independent physical feedback confirms it. Building on process-aware and safety-supervision ideas from industrial control, the architecture makes physical confirmation a mandatory completion condition at the local enforcement point, which catches a fully authorised command that is nonetheless physically unsafe, a failure mode that authorisation checks alone cannot detect. These properties are examined through a deterministic, openly available software proof-of-concept that verifies the design against its own acceptance criteria; it is not a field validation. The software executes six attack and failure scenarios plus one nominal-operation control against an intentionally unsecured negative control and a secure controller, under identical, controlled conditions: the negative control is compromised in all 6 attack scenarios and the secure controller in 0\u002F6, with 0\u002F1 false positives on legitimate operation. Analytically, the design costs 21 bytes and 50–60% additional Long Range Wide Area Network (LoRaWAN) time-on-air per record, and 78% of single-step perturbations of the trust-scoring inputs leave asset classification unchanged: the actuator class (A2) is completely stable, and the gateway\u002FRTU\u002FPLC class (A3) is stable under 6 of 7 admissible perturbations (86%), changing only when its credential\u002Fupdate authority reaches the trust-anchor threshold. A sensor-level mapping from sampling rate, measurement drift and physical fault modes to the edge-verification controls they dictate, provisions for secure time, counter recovery and the choice between fail-closed and fail-open safe states, an illustrative hypothetical worked deployment example, and a transfer of the model to greenhouse, fertigation, livestock-watering and cold-chain settings show how the architecture could be applied in practice.","智能灌溉既是一个物联网(IoT)问题，也是一个运营技术(OT)问题：软件决策直接作用于阀门、水泵、压力和水流输送，因此被欺骗的读数或被重放的命令会产生物理后果。本文提出了一种技术中立的参考架构，使这些后果可控。该架构由七个功能层和三个横切面、六类信任边界(规定每次跨越边界时必须重新建立哪些属性)，以及一份可审计的核心资产清单(配有明确的信任等级评定标准)组成。两条主导性执行原则保障了这一结构：本地安全授权，即将最终执行许可保留在田间网关、远程终端单元(RTU)或可编程逻辑控制器(PLC)处；以及反馈感知执行，即只有当独立的物理反馈予以确认时，才将命令视为完成。该架构以工业控制中的过程感知和安全监督思想为基础，将物理确认设为本地执行点的强制性完成条件，从而能够捕获完全获得授权但在物理上仍不安全的命令——这是仅靠授权检查无法检测到的失效模式。这些特性通过一个确定性、公开可用的软件概念验证进行了检验，该验证依据其自身的验收标准对设计进行评估；这不是现场验证。该软件在相同且受控的条件下，针对一个故意不设防护的阴性对照和一个安全控制器，执行了六种攻击与故障场景以及一项标称运行对照：阴性对照在所有6种攻击场景中均被攻破，而安全控制器为0\u002F6，对合法运行的误报为0\u002F1。分析表明，该设计每条记录额外增加21字节和50–60%的LoRaWAN(LoRaWAN)空中时间，且信任评分输入的78%单步扰动不会改变资产分类：执行器类(A2)完全稳定，网关\u002FRTU\u002FPLC类(A3)在7种可允许扰动中有6种保持稳定(86%)，仅当其凭证\u002F更新权限达到信任锚阈值时才会改变。还提出了从采样率、测量漂移和物理故障模式到其所决定的边缘验证控制的传感器级映射，以及安全时间、计数器恢复和故障关闭与故障打开安全状态之间选择的保障措施。","Sensors","2026-10-15T00:00:00Z",75,{"impact":59,"substance":16,"depth":125,"authority":126,"freshness":127,"relevant":21,"comment":128},18,14,9,"面向智慧灌溉的信任边界参考架构论文，方法新颖、含软件验证与量化开销数据，但属概念验证、非田间实证，产业影响有限，可作为技术前沿精选。",[130],{"name":121,"url":118},[132,26,27,29,133],"数字乡村","网络安全",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[136],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":13,"version":46,"method":47,"evidence":138},{"candidates":139,"agricultural_evidence":52},[140,146,151,157,163,169,174],{"code":39,"score":141,"supported":52,"title":142,"abstract":143,"tags":145},15,[40],[144,40],"灌溉",[29],{"code":32,"score":12,"supported":52,"title":147,"abstract":148,"tags":150},[],[149,56,54],"物联网",[27],{"code":152,"score":73,"supported":52,"title":153,"abstract":154,"tags":156},"B7",[],[155],"greenhouse",[],{"code":158,"score":73,"supported":52,"title":159,"abstract":160,"tags":162},"C",[],[161],"livestock",[],{"code":164,"score":73,"supported":52,"title":165,"abstract":166,"tags":168},"F3",[],[167],"fertigation",[],{"code":170,"score":20,"supported":13,"title":171,"abstract":172,"tags":173},"I6",[],[],[132,133],{"code":85,"score":21,"supported":13,"title":175,"abstract":176,"tags":177},[],[],[26],[179,180],"智能灌溉 信任边界 参考架构","LoRaWAN 灌溉 安全","智能灌溉信任边界参考架构-5077","10.3390\u002Fs26206545",{"doi":182,"openalex_id":184,"authors":185,"venue":121,"cited_by_count":103,"oa_url":118,"card":201,"direction":108,"ingested_from":110},"W7222165876",[186,189,192,195,198],{"name":187,"orcid":188},"Alvertos Ioannis Mourikis","https:\u002F\u002Forcid.org\u002F0009-0009-2386-2803",{"name":190,"orcid":191},"Iosif Polenakis","https:\u002F\u002Forcid.org\u002F0000-0002-6427-5519",{"name":193,"orcid":194},"Christos Sarantidis","https:\u002F\u002Forcid.org\u002F0009-0003-9486-8483",{"name":196,"orcid":197},"Romanos Kalamatianos","https:\u002F\u002Forcid.org\u002F0009-0000-6939-5424",{"name":199,"orcid":200},"Ioannis Karydis","https:\u002F\u002Forcid.org\u002F0000-0002-9470-2729",{"tldr":202,"method":203,"finding":204,"direction":108,"opportunity":205},"提出面向智慧灌溉的安全参考架构，用软件验证可抵御重放与欺骗攻击。","七层三面参考架构、信任边界分类、确定性软件概念验证与LoRaWAN开销分析。","安全控制器在6种攻击场景下0次被攻破，负控制全部被攻破，且误报率为0。","可研究边缘验证控制与采样率、漂移、故障模式的映射，以及安全时间与失效安全状态选择。","2026-10-16T23:30:08.098924Z",{"id":208,"title":209,"url":210,"summary":211,"summary_zh":212,"content":8,"source_name":213,"source_url":210,"published_at":214,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":215,"score_detail":216,"sources":219,"tags":221,"primary_topic":223,"secondary_topics":224,"classification":226,"search_phrases":248,"slug":251,"view_count":21,"doi":252,"paper":253,"created_at":265},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":217,"substance":126,"depth":19,"authority":113,"freshness":217,"relevant":21,"comment":218},8,"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。",[220],{"name":213,"url":210},[26,149,29,222,30],"精准农业",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[225],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":13,"version":46,"method":47,"evidence":227},{"candidates":228,"agricultural_evidence":52},[229,234,239,243],{"code":39,"score":125,"supported":52,"title":230,"abstract":231,"tags":233},[40],[144,232,40],"水资源",[29],{"code":85,"score":217,"supported":52,"title":235,"abstract":236,"tags":238},[],[222,237],"precision agriculture",[26,222],{"code":32,"score":217,"supported":52,"title":240,"abstract":241,"tags":242},[],[149,54],[30,149],{"code":72,"score":113,"supported":52,"title":244,"abstract":245,"tags":247},[],[246,76],"土壤",[],[249,250],"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":252,"openalex_id":254,"authors":255,"venue":213,"cited_by_count":103,"oa_url":210,"card":260,"direction":108,"ingested_from":110},"W7220437851",[256,258],{"name":257,"orcid":8},"Mr. Bhagyesh N Chaudhari",{"name":259,"orcid":8},"Dr. Dinesh D Puri",{"tldr":261,"method":262,"finding":263,"direction":108,"opportunity":264},"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":267,"title":268,"url":269,"summary":270,"summary_zh":271,"content":8,"source_name":272,"source_url":269,"published_at":273,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":274,"score_detail":275,"sources":279,"tags":281,"primary_topic":283,"secondary_topics":284,"classification":290,"search_phrases":324,"slug":327,"view_count":20,"doi":328,"paper":329,"created_at":346},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":59,"substance":276,"depth":277,"authority":19,"freshness":127,"relevant":21,"comment":278},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。",[280],{"name":272,"url":269},[26,282,28,149,29],"农业人工智能",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[285,289],{"code":64,"slug":286,"name":287,"path":288,"parent_code":36},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":13,"version":46,"method":47,"evidence":291},{"candidates":292,"agricultural_evidence":52},[293,297,301,305,309,315,320],{"code":39,"score":125,"supported":52,"title":294,"abstract":295,"tags":296},[40],[144,232,40],[29],{"code":64,"score":19,"supported":52,"title":298,"abstract":299,"tags":300},[69],[28],[282,28],{"code":32,"score":127,"supported":52,"title":302,"abstract":303,"tags":304},[54],[],[149],{"code":72,"score":113,"supported":52,"title":306,"abstract":307,"tags":308},[],[246,76],[],{"code":310,"score":73,"supported":52,"title":311,"abstract":312,"tags":314},"H7",[],[313],"干旱",[],{"code":79,"score":73,"supported":52,"title":316,"abstract":317,"tags":319},[],[318],"可持续农业",[],{"code":85,"score":21,"supported":13,"title":321,"abstract":322,"tags":323},[],[],[26],[325,326],"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":328,"openalex_id":330,"authors":331,"venue":272,"cited_by_count":103,"oa_url":269,"card":341,"direction":108,"ingested_from":110},"W7213546497",[332,335,338],{"name":333,"orcid":334},"Sarra Gourari","https:\u002F\u002Forcid.org\u002F0009-0005-1846-0886",{"name":336,"orcid":337},"Wafa Difallah","https:\u002F\u002Forcid.org\u002F0000-0002-6181-0395",{"name":339,"orcid":340},"Belkacem Draoui","https:\u002F\u002Forcid.org\u002F0000-0001-5490-3991",{"tldr":342,"method":343,"finding":344,"direction":108,"opportunity":345},"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":348,"title":349,"url":350,"summary":351,"summary_zh":352,"content":8,"source_name":353,"source_url":350,"published_at":354,"category":11,"cover_url":8,"hotness":355,"is_selected":13,"score":356,"score_detail":357,"sources":359,"tags":363,"primary_topic":366,"secondary_topics":367,"classification":369,"search_phrases":400,"slug":403,"view_count":103,"doi":404,"paper":405,"created_at":422},5474,"Real-World Impact: Transforming Agriculture with Technology A Multi-Stakeholder Review of Smart Monitoring, Precision Irrigation, and Digital Value-Chain Integration","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23510500","There's a deliberate and significant transition from input to information. This article combines peer-reviewed evidence and public-sector data on six interrelated technology clusters that are fundamentally changing on-farm and off-farm decision making artificial intelligence (AI) based Farm Management Platforms, Internet of Things (IoT) smart irrigation, aerial and satellite crop monitoring, blockchain-enabled supply-chain digitisation, sustainability and carbon-tracking instruments, and farm data analytics dashboards. In the literature examined, precision tools have been correlated with 15% to 30% gains in crop yields, 30% to 55% of water saved during irrigation, and 10% to 20% of operational costs reduced, all with a range of variability based on the crop, the geographical region, and the size of the farm. It examines the mechanisms of these effects on the one hand and the differential rates of benefit, on the other, between large commercial farms and smallholders, and the policy and infrastructural conditions that drive actual adoption. It also highlights areas of current evidence which are lacking, such as limited long horizon field trials, poor interoperability standards, and lack of social outcome data. The article ends with a discussion on how to turn pilots into lasting, system-level change in food production, both from the government and enterprise side, as well as from the perspective of small and medium producers.","从投入要素到信息的转变是有意为之且意义重大的。本文结合经同行评审的证据与公共部门数据，聚焦六个相互关联、正在从根本上改变农场内外决策方式的技术集群：基于人工智能（AI）的农场管理平台、物联网（IoT）智能灌溉、航空与卫星作物监测、区块链赋能的供应链数字化、可持续性与碳追踪工具，以及农场数据分析仪表盘。在所考察的文献中，精准工具与作物产量提升15%至30%、灌溉用水节约30%至55%、运营成本降低10%至20%相关联，且这些效果因作物、地理区域和农场规模而存在不同程度的差异。本文一方面考察这些效应的作用机制，另一方面分析大型商业农场与小农户之间受益程度的差异，以及驱动实际采用的政策与基础设施条件。文章还指出了当前证据的薄弱之处，如缺乏长期田间试验、互操作性标准不完善以及社会成效数据缺失。文章最后讨论了如何从政府与企业两侧，以及中小生产者视角出发，将试点项目转化为粮食生产中持久、系统层面的变革。","Zenodo (CERN European Organization for Nuclear Research)","2026-11-25T00:00:00Z",25,73,{"impact":16,"substance":276,"depth":277,"authority":19,"freshness":103,"relevant":21,"comment":358},"多利益相关方综述整合六类技术集群的量化效益与采纳差异，信息增量足、产业参考价值高，但发布于一个月后，时效性不足，暂不宜进入每日精选。",[360,361],{"name":353,"url":350},{"name":353,"url":362},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[26,282,27,364,365],"区块链溯源","精准灌溉",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[368],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":13,"version":46,"method":47,"evidence":370},{"candidates":371,"agricultural_evidence":52},[372,376,380,385,391,396],{"code":39,"score":141,"supported":52,"title":373,"abstract":374,"tags":375},[40],[144,40],[365],{"code":32,"score":12,"supported":52,"title":377,"abstract":378,"tags":379},[],[149,56,54],[27],{"code":64,"score":381,"supported":52,"title":382,"abstract":383,"tags":384},4,[],[69],[282],{"code":386,"score":73,"supported":52,"title":387,"abstract":388,"tags":390},"G2",[],[389],"小农户",[],{"code":392,"score":21,"supported":13,"title":393,"abstract":394,"tags":395},"E4",[],[],[364],{"code":85,"score":21,"supported":13,"title":397,"abstract":398,"tags":399},[],[],[26],[401,402],"Zenodo 智慧农业 多利益相关方综述","精准灌溉 物联网 区块链 供应链","Zenodo智慧农业多利益相关方综述-5474","10.5281\u002Fzenodo.23510500",{"doi":404,"openalex_id":406,"authors":407,"venue":353,"cited_by_count":103,"oa_url":350,"card":416,"direction":108,"ingested_from":110},"W7223034914",[408,411,413],{"name":409,"orcid":410},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":412,"orcid":8},"Dr.T.Baskar",{"name":414,"orcid":415},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":417,"method":418,"finding":419,"direction":420,"opportunity":421},"综述六大农业数字技术集群对农场决策的变革及实际效益与采纳差异。","综合同行评议证据与公共部门数据，多利益相关方综述六类技术。","精准技术可增产15-30%、节水30-55%、降本10-20%，但小农获益不均。","数字乡村与农业信息化","缺乏长期田间试验、互操作标准与社会效益数据，小农采纳机制是研究空白。","2026-10-21T23:30:08.684798Z",{"id":424,"title":425,"url":426,"summary":427,"summary_zh":428,"content":8,"source_name":429,"source_url":426,"published_at":430,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":431,"score_detail":432,"sources":434,"tags":436,"primary_topic":440,"secondary_topics":441,"classification":447,"search_phrases":493,"slug":496,"view_count":103,"doi":497,"paper":498,"created_at":522},4989,"Advances in micro-irrigation and supplementary irrigation in millets of dryland ecosystems","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.16312","Dryland ecosystems cover approximately 41 % of the Earth's terrestrial surface and support nearly two billion people. This review synthesises recent advances in micro-irrigation and supplementary irrigation strategies for enhancing millet productivity and resilience in dryland ecosystems. Drylands are characterised by low and erratic rainfall, high evapotranspiration, poor soil fertility and frequent drought stress, making agricultural production vulnerable to climate variability and water scarcity. Millets are drought-tolerant, climate-resilient cereals cultivated in dryland agroecosystems. Despite their resilience, millet productivity remains constrained by increasing rainfall variability, prolonged intra-seasonal dry spells and rising atmospheric aridity associated with climate change. Evidence from semi-arid and arid regions indicates that micro-irrigation systems, including drip, subsurface drip and micro-sprinkler irrigation, significantly improve yield and biomass accumulation compared to conventional surface irrigation methods. These irrigation systems also enhance water use efficiency (WUE) and nutrient use efficiency (NUE), making them highly suitable for millet production in dryland ecosystems. The integration of fertigation further improves nutrient synchronisation with crop demand, reduces leaching losses and supports soil fertility. The review also highlights optimised irrigation scheduling based on evapotranspiration, soil moisture thresholds, deficit irrigation principles and sensor-based automation. Smart irrigation technologies incorporating soil moisture sensors and Internet of Things (IoT)-based control systems show strong potential to enhance precision water management and reduce water consumption by up to 30–35 %. Their adoption, supported by technological innovation, policy frameworks and location-specific research, will promote sustainable water management and resource-use efficiency in millet-based dryland ecosystems. Future research should focus on optimising irrigation scheduling, smart technologies and cost-effective solutions for water-limited environments.","旱地生态系统约占地球陆地表面的41%，支撑着近20亿人口。本文综述了微灌与补充灌溉策略在提升旱地生态系统中小米生产力与恢复力方面的最新进展。旱地降水少且不稳定、蒸散量大、土壤肥力低、干旱胁迫频繁，使农业生产易受气候变率和缺水的影响。小米是种植于旱地农业生态系统中的耐旱、气候适应型谷物。尽管具有恢复力，小米生产力仍受降雨变率增大、季节性持续干旱期延长以及气候变化相关的大气干燥度上升的制约。来自半干旱和干旱地区的证据表明，与传统地面灌溉方式相比，微灌系统（包括滴灌、地下滴灌和微喷灌）显著提高了产量和生物量积累。这些灌溉系统还提高了水分利用效率（WUE）和养分利用效率（NUE），使其非常适合旱地生态系统中的小米生产。水肥一体化（fertigation）的整合进一步改善了养分与作物需求的同步性，减少了淋失损失，并有助于维持土壤肥力。本文还重点介绍了基于蒸散量、土壤水分阈值、亏缺灌溉原则和基于传感器的自动化的优化灌溉制度。结合土壤水分传感器和基于物联网（IoT）控制系统的智能灌溉技术，在提升精准水分管理和减少高达30%–35%的用水量方面展现出巨大潜力。在技术创新、政策框架和因地制宜研究的支持下，这些技术的采用将促进以小米为基础的旱地生态系统中的可持续水分管理和资源利用效率。未来研究应聚焦于优化灌溉制度、智能技术以及面向水资源受限环境的成本效益解决方案。","Plant Science Today","2026-10-14T00:00:00Z",76,{"impact":125,"substance":18,"depth":277,"authority":19,"freshness":217,"relevant":21,"comment":433},"系统综述微灌与补充灌溉提升旱地谷子产量与水肥效率，含物联网智能灌溉节水30-35%等实质结论，对旱作智慧农业有参考价值。",[435],{"name":429,"url":426},[26,27,437,438,439],"旱作农业","节水灌溉","谷子高粱",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[442,443],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":310,"slug":444,"name":445,"path":446,"parent_code":43},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":45,"locked":13,"version":46,"method":47,"evidence":448},{"candidates":449,"agricultural_evidence":52},[450,456,460,468,473,477,483,489],{"code":39,"score":451,"supported":52,"title":452,"abstract":453,"tags":455},19,[40],[144,232,40,454],"evapotranspiration",[437,438],{"code":32,"score":12,"supported":52,"title":457,"abstract":458,"tags":459},[],[149,56,54],[27],{"code":310,"score":127,"supported":52,"title":461,"abstract":462,"tags":467},[],[463,464,313,465,466],"气候变化","气候适应","climate change","drought",[],{"code":164,"score":113,"supported":52,"title":469,"abstract":470,"tags":472},[],[471,167],"水肥一体化",[],{"code":72,"score":113,"supported":52,"title":474,"abstract":475,"tags":476},[],[246,76],[],{"code":478,"score":73,"supported":52,"title":479,"abstract":480,"tags":482},"A5",[],[481],"millet",[],{"code":484,"score":73,"supported":52,"title":485,"abstract":486,"tags":488},"H6",[],[487],"农业生态",[],{"code":85,"score":21,"supported":13,"title":490,"abstract":491,"tags":492},[],[],[26],[494,495],"微灌 补充灌溉 旱地 谷子","智慧灌溉 水分利用效率 谷子","微灌补充灌溉旱地谷子-4989","10.14719\u002Fpst.16312",{"doi":497,"openalex_id":499,"authors":500,"venue":429,"cited_by_count":103,"oa_url":516,"card":517,"direction":108,"ingested_from":110},"W7221796668",[501,504,507,510,513],{"name":502,"orcid":503},"M Megha","https:\u002F\u002Forcid.org\u002F0009-0006-7303-6946",{"name":505,"orcid":506},"N Vadivel","https:\u002F\u002Forcid.org\u002F0000-0002-5301-5324",{"name":508,"orcid":509},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":511,"orcid":512},"P. Ayyadurai","https:\u002F\u002Forcid.org\u002F0000-0003-1889-6047",{"name":514,"orcid":515},"PD Rathinasamy","https:\u002F\u002Forcid.org\u002F0000-0001-8793-3513","https:\u002F\u002Fhorizonepublishing.com\u002Fjournals\u002Findex.php\u002FPST\u002Farticle\u002Fdownload\u002F16312\u002F16748",{"tldr":518,"method":519,"finding":520,"direction":108,"opportunity":521},"综述旱地微灌与补充灌溉提升小米产量与水分利用效率的研究进展。","综述滴灌、地下滴灌、微喷及水肥一体化、传感器与IoT智能灌溉技术。","微灌显著提高小米产量、WUE和NUE，智能灌溉可节水30–35%。","可研究低成本IoT灌溉决策模型在小米旱地的本地化适配与长期效应。","2026-10-15T23:30:08.971713Z",{"id":524,"title":525,"url":526,"summary":527,"summary_zh":528,"content":8,"source_name":529,"source_url":526,"published_at":530,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":531,"score_detail":532,"sources":534,"tags":536,"primary_topic":539,"secondary_topics":540,"classification":542,"search_phrases":561,"slug":564,"view_count":103,"doi":565,"paper":566,"created_at":584},4888,"A MATLAB-based AI-IoT simulation framework for LSTM-driven smart solar irrigation and renewable energy management","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-74935-0","Smart irrigation offers a great avenue for better water use efficiency, and farming sustainability. However, most of the studies conducted so far tend to concentrate on the monitoring systems and platforms of the Internet of Things (IoT) or on the prediction models based on Artificial Intelligence (AI) (but not on both). To address this gap, this research study proposes an Artificial Intelligence (AI)-Internet of Things (IoT) system to predict the water consumption requirements and control the energy produced by a PV system along with a battery State of Charge (SOC) management system, Maximum Power Point Tracking (MPPT) algorithm, adaptive pump-speed control, and conceptual IoT communication architecture. A dataset of real environmental data from the NASA POWER database was used to test the framework for Egypt’s New Delta region. The LSTM model was able to classify the irrigation pump operating states with an accuracy of 96.11%, a recall of 0.9820, a precision of 0.9100, and an F1-score of 0.9446. It had a better performance than the two models, Artificial Neural Network (ANN) and Support Vector Machine (SVM) when compared and robustness testing with various independent training runs showed a stable performance in predicting. An estimated annual water-saving rate of 28.57% was obtained relative to the assumed conventional fixed-schedule baseline, while motor energy use was reduced by 74.74% through adaptive pump-speed control. A sensitivity analysis was also performed, showing that the model was stable to moderate variations in environmental and operating conditions. Combined, these findings suggest that the use of AI-based modelling for renewable energy management in the context of an irrigation simulation model built in MATLAB could be a valuable asset in improving irrigation efficiency and lowering water and energy use, which is a promising start for future implementation of hardware versions of intelligent, solar-powered irrigation devices.","智能灌溉为提高用水效率和农业可持续性提供了重要途径。然而，迄今为止大多数研究倾向于集中在物联网（IoT）的监测系统和平台，或基于人工智能（AI）的预测模型（但很少两者兼顾）。为弥补这一空白，本研究提出了一种人工智能（AI）-物联网（IoT）系统，用于预测用水需求并控制光伏系统产生的能量，同时包含电池荷电状态（SOC）管理系统、最大功率点跟踪（MPPT）算法、自适应泵速控制以及概念性物联网通信架构。研究采用NASA POWER数据库中的真实环境数据集，在埃及新三角洲地区对该框架进行了测试。LSTM模型能够对灌溉泵运行状态进行分类，准确率达到96.11%，召回率为0.9820，精确率为0.9100，F1分数为0.9446。与人工神经网络（ANN）和支持向量机（SVM）两个模型相比，该模型表现更优，且通过多次独立训练运行进行的鲁棒性测试表明其在预测方面具有稳定性能。相对于假设的传统固定调度基线，估计年节水率为28.57%，而通过自适应泵速控制，电机能耗降低了74.74%。研究还进行了敏感性分析，表明该模型对环境和运行条件的中等程度变化具有稳定性。综合来看，这些发现表明，在MATLAB中构建的灌溉仿真模型背景下，将基于AI的建模用于可再生能源管理，可能是提高灌溉效率、降低水和能源消耗的有价值手段，这为未来实现智能太阳能灌溉设备的硬件版本提供了一个有前景的开端。","Scientific Reports","2026-10-12T00:00:00Z",78,{"impact":51,"substance":16,"depth":125,"authority":126,"freshness":217,"relevant":21,"comment":533},"该研究提出AI-IoT融合的太阳能灌溉仿真框架，LSTM预测精度达96.11%，节水28.57%、节能74.74%，方法新颖且数据可靠，对智慧农业与农业能源管理有参考价值。",[535],{"name":529,"url":526},[26,282,29,537,538],"节水农业","光伏农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[541],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":13,"version":46,"method":47,"evidence":543},{"candidates":544,"agricultural_evidence":52},[545,549,553,557],{"code":32,"score":277,"supported":52,"title":546,"abstract":547,"tags":548},[54],[149,56,54],[],{"code":39,"score":51,"supported":52,"title":550,"abstract":551,"tags":552},[40],[144,40],[29,537],{"code":64,"score":381,"supported":52,"title":554,"abstract":555,"tags":556},[],[69],[282],{"code":85,"score":21,"supported":13,"title":558,"abstract":559,"tags":560},[],[],[26],[562,563],"MATLAB AI-IoT 太阳能灌溉","LSTM 智能灌溉 埃及新三角洲","MATLABAI-IoT太阳能灌溉-4888","10.1038\u002Fs41598-026-74935-0",{"doi":565,"openalex_id":567,"authors":568,"venue":529,"cited_by_count":103,"oa_url":578,"card":579,"direction":108,"ingested_from":110},"W7221571268",[569,572,575],{"name":570,"orcid":571},"Ahmed Mashaly","https:\u002F\u002Forcid.org\u002F0009-0007-7009-4531",{"name":573,"orcid":574},"Ragab Abdelaziz El-Sehiemy","https:\u002F\u002Forcid.org\u002F0000-0002-3340-4031",{"name":576,"orcid":577},"Mohamed M. Elgohary","https:\u002F\u002Forcid.org\u002F0000-0003-4441-7812","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-74935-0.pdf",{"tldr":580,"method":581,"finding":582,"direction":108,"opportunity":583},"提出MATLAB中AI-IoT仿真框架，用LSTM预测灌溉需水并管理光伏与电池。","NASA POWER环境数据，LSTM对比ANN\u002FSVM，含MPPT与自适应泵速","LSTM分类准确率96.11%，节水28.57%，电机能耗降74.74%。","可延伸至真实硬件部署与多区域验证，并融合实时气象与土壤墒情数据。","2026-10-14T23:30:13.060384Z"]