[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5073":3,"related-5073":96},{"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":28,"primary_topic":34,"secondary_topics":40,"classification":41,"search_phrases":76,"slug":79,"view_count":72,"doi":80,"paper":81,"created_at":95},5073,"INCREASING REGIONAL ADDED VALUE THROUGH THE PROCESSING OF AGRICULTURAL PRODUCTS IN THE REPUBLIC OF KARAKALPAKSTAN","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23383466","This article examines the economic and environmental significance of using digital innovations to develop green agriculture in the Republic of Karakalpakstan. It analyzes challenges related to water scarcity, soil salinity, production costs, and the efficiency of natural resource utilization. The article highlights the potential of digital monitoring, smart irrigation, remote sensing, and data-driven management to improve agricultural production outcomes.","本文探讨了在卡拉卡尔帕克斯坦共和国利用数字创新发展绿色农业的经济与环境意义。文章分析了与水资源短缺、土壤盐渍化、生产成本以及自然资源利用效率相关的挑战。文章强调了数字监测、智能灌溉、遥感以及数据驱动管理在改善农业生产成效方面的潜力。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-10-15T00:00:00Z","论文",25,false,63,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,16,14,9,1,"聚焦乌兹别克斯坦卡拉卡尔帕克斯坦地区数字农业与盐碱地治理，属区域案例研究，对国内智慧农业与盐碱地改良有参考价值，但影响层级与信息增量有限。",[24,25],{"name":10,"url":6},{"name":26,"url":27},"SAMARALI TA’LIM VA BARQAROR INNOVATSIYALAR JURNALI","https:\u002F\u002Finnovativepublication.uz\u002Findex.php\u002Fjelsi\u002Farticle\u002Fview\u002F6404",[29,30,31,32,33],"数字农业","智慧农业","智能灌溉","盐碱地治理","遥感监测",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":42,"locked":14,"version":43,"method":44,"evidence":45},"classified","2026-10-22.2","rules",{"candidates":46,"agricultural_evidence":49},[47,55,63,70],{"code":35,"score":48,"supported":49,"title":50,"abstract":51,"tags":54},10,true,[],[52,53,36],"灌溉","水资源",[31],{"code":56,"score":57,"supported":49,"title":58,"abstract":59,"tags":62},"H2",7,[],[60,61],"土壤","soil",[32],{"code":64,"score":57,"supported":49,"title":65,"abstract":66,"tags":69},"I3",[],[67,68],"遥感","remote sensing",[33],{"code":71,"score":72,"supported":14,"title":73,"abstract":74,"tags":75},"I1",2,[],[],[29,30],[77,78],"卡拉卡尔帕克斯坦 数字农业","咸海地区 盐碱地 智能灌溉","卡拉卡尔帕克斯坦数字农业-5073","10.5281\u002Fzenodo.23383466",{"doi":80,"openalex_id":82,"authors":83,"venue":10,"cited_by_count":86,"oa_url":6,"card":87,"direction":93,"ingested_from":94},"W7221970206",[84],{"name":85,"orcid":9},"Sholpan Eshimova",0,{"tldr":88,"method":89,"finding":90,"direction":91,"opportunity":92},"研究卡拉卡尔帕克斯坦通过农产品加工与数字创新提升区域附加值。","分析水资源短缺、土壤盐渍化等挑战，评估数字监测、智能灌溉、遥感和数据管理潜力。","数字创新可提高农业生产效率并促进绿色农业发展。","农业绿色发展与碳","可量化数字技术对盐碱地农业附加值及资源效率的贡献，探索适配干旱区的智慧农业模式。","智慧农业 \u002F 农业物联网","openalex","2026-10-16T23:30:07.817464Z",{"total":97,"page":21,"page_size":97,"items":98},6,[99,216,310,377,454,521],{"id":100,"title":101,"url":102,"summary":103,"summary_zh":104,"content":9,"source_name":105,"source_url":102,"published_at":106,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":107,"score_detail":108,"sources":114,"tags":116,"primary_topic":120,"secondary_topics":125,"classification":130,"search_phrases":186,"slug":189,"view_count":86,"doi":190,"paper":191,"created_at":215},5483,"Soil Salinity under Climate Change in Pakistan: Emerging Challenges and Sustainable Management Approaches for Agriculture","https:\u002F\u002Fdoi.org\u002F10.71317\u002Fkjard.2.10.2026.883","Soil salinity is a major constraint to agricultural productivity, soil health, and food security in Pakistan, particularly under changing climatic conditions. Rising temperatures, increased evapotranspiration, irregular precipitation, droughts, flooding, and sea-level rise can intensify salt accumulation and alter soil–water interactions. This review examines the emerging challenges associated with climate change and soil salinity in Pakistan, focusing on their impacts on soil properties, crop physiology, agricultural production, and rural livelihoods. It discusses the mechanisms of soil salinity and sodicity, their spatial distribution, and the interactions among climate variability, irrigation practices, groundwater quality, and secondary salinization. Furthermore, sustainable management approaches are evaluated, including gypsum application, organic amendments, integrated nutrient management, efficient irrigation, drainage improvement, and appropriate leaching strategies. Climate-resilient approaches, such as conventional crop breeding, modern biotechnology, halophyte utilization, plant growth-promoting microorganisms, and mycorrhizal associations, are also explored. The review highlights the potential of remote sensing, geographic information systems, precision agriculture, and predictive modelling to support salinity monitoring and site-specific management. Despite these opportunities, important challenges remain, including insufficient long-term monitoring, limited region-specific data, economic constraints, and gaps in farmer awareness and institutional coordination. Addressing these challenges requires integrated research, effective agricultural policies, improved extension services, and collaboration among researchers, policymakers, and farming communities. Prioritizing locally adapted, economically feasible, and environmentally sustainable interventions can strengthen agricultural resilience, restore salt-affected soils, and protect freshwater resources. Overall, integrating climate adaptation with sustainable soil and water management is essential for maintaining agricultural productivity and improving long-term food security in Pakistan.","土壤盐渍化是制约巴基斯坦农业生产力、土壤健康和粮食安全的主要因素，在气候变化条件下尤为突出。气温升高、蒸散加剧、降水不规律、干旱、洪水以及海平面上升可能加剧盐分积累并改变土壤—水相互作用。本文综述了巴基斯坦气候变化与土壤盐渍化相关的新兴挑战，重点关注其对土壤性质、作物生理、农业生产和农村生计的影响。文章讨论了土壤盐渍化和钠质化的机制、其空间分布，以及气候变率、灌溉实践、地下水质量和次生盐渍化之间的相互作用。此外，评估了可持续管理途径，包括施用石膏、有机改良、综合养分管理、高效灌溉、排水改良和适宜的淋洗策略。还探讨了气候韧性方法，如常规作物育种、现代生物技术、盐生植物利用、植物促生微生物和菌根共生。本文强调了遥感、地理信息系统、精准农业和预测建模在支持盐渍化监测和因地制宜管理方面的潜力。尽管存在这些机遇，重要挑战依然存在，包括长期监测不足、区域特定数据有限、经济制约以及农民意识和制度协调方面的差距。应对这些挑战需要综合研究、有效的农业政策、改进的推广服务以及研究人员、政策制定者和农业社区之间的协作。优先采取适应当地、经济可行且环境可持续的干预措施，可增强农业韧性、修复盐渍化土壤并保护淡水资源。总体而言，将气候适应与可持续水土管理相结合，对于维持巴基斯坦农业生产力和改善长期粮食安全至关重要。","Kashmir Journal of Academic Research and Development","2026-10-20T00:00:00Z",66,{"impact":109,"substance":110,"depth":111,"authority":17,"freshness":112,"relevant":21,"comment":113},13,18,15,8,"综述性论文，系统梳理气候变化下巴基斯坦土壤盐渍化成因与可持续治理路径，兼具遥感与精准农业视角，对盐碱地治理议题有参考价值，但属区域性综述、非突破性成果。",[115],{"name":105,"url":102},[117,32,33,118,119],"气候变化","智慧灌溉","土壤盐渍化",{"code":121,"slug":122,"name":123,"path":124,"parent_code":39},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",[126,129],{"code":56,"slug":61,"name":127,"path":128,"parent_code":39},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":42,"locked":14,"version":43,"method":44,"evidence":131},{"candidates":132,"agricultural_evidence":49},[133,140,144,149,153,160,166,173,180],{"code":121,"score":110,"supported":49,"title":134,"abstract":136,"tags":139},[135],"climate change",[117,137,138,135],"气候适应","干旱",[117],{"code":56,"score":111,"supported":49,"title":141,"abstract":142,"tags":143},[61],[60,61],[32],{"code":35,"score":48,"supported":49,"title":145,"abstract":146,"tags":148},[],[52,53,36,147],"evapotranspiration",[118],{"code":64,"score":57,"supported":49,"title":150,"abstract":151,"tags":152},[],[67,68],[33],{"code":154,"score":97,"supported":49,"title":155,"abstract":156,"tags":159},"A8",[],[157,158],"粮食安全","food security",[],{"code":71,"score":97,"supported":49,"title":161,"abstract":162,"tags":165},[],[163,164],"精准农业","precision agriculture",[],{"code":167,"score":97,"supported":49,"title":168,"abstract":169,"tags":172},"I4",[],[170,171],"地理信息","geographic information",[],{"code":174,"score":175,"supported":49,"title":176,"abstract":177,"tags":179},"F3",3,[],[178],"养分管理",[],{"code":181,"score":175,"supported":49,"title":182,"abstract":183,"tags":185},"J1",[],[184],"农业政策",[],[187,188],"巴基斯坦 土壤盐渍化","盐碱地 遥感监测","巴基斯坦土壤盐渍化-5483","10.71317\u002Fkjard.2.10.2026.883",{"doi":190,"openalex_id":192,"authors":193,"venue":105,"cited_by_count":86,"oa_url":102,"card":209,"direction":213,"ingested_from":94},"W7222999158",[194,196,198,200,202,204,207],{"name":195,"orcid":9},"Asifa Naz",{"name":197,"orcid":9},"Muhammad Abu Baker Siddiq",{"name":199,"orcid":9},"Syed Asghar Hussain Shah",{"name":201,"orcid":9},"Shahrish Nawaz",{"name":203,"orcid":9},"Muhmmad Irfan",{"name":205,"orcid":206},"Rizwana Kausar","https:\u002F\u002Forcid.org\u002F0000-0002-1339-3312",{"name":208,"orcid":9},"Muhammad Nouman Khalid",{"tldr":210,"method":211,"finding":212,"direction":213,"opportunity":214},"综述气候变化下巴基斯坦土壤盐渍化的挑战与可持续管理途径。","文献综述，整合盐渍化机制、空间分布及遥感GIS与预测模型应用。","需将气候适应与水土管理结合，优先本地化、经济可行的干预措施。","农业遥感与作物表型","可填补长期盐渍化监测与区域数据空白，发展遥感+预测模型的精准分区治理。","2026-10-21T23:30:23.059448Z",{"id":217,"title":218,"url":219,"summary":220,"summary_zh":221,"content":9,"source_name":222,"source_url":219,"published_at":11,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":223,"score_detail":224,"sources":227,"tags":229,"primary_topic":233,"secondary_topics":234,"classification":241,"search_phrases":281,"slug":284,"view_count":86,"doi":285,"paper":286,"created_at":309},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",75,{"impact":17,"substance":225,"depth":110,"authority":19,"freshness":20,"relevant":21,"comment":226},22,"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。",[228],{"name":222,"url":219},[230,30,231,31,232],"数字乡村","农业物联网","网络安全",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[235],{"code":236,"slug":237,"name":238,"path":239,"parent_code":240},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"status":42,"locked":14,"version":43,"method":44,"evidence":242},{"candidates":243,"agricultural_evidence":49},[244,248,255,261,267,272,277],{"code":35,"score":111,"supported":49,"title":245,"abstract":246,"tags":247},[36],[52,36],[31],{"code":236,"score":48,"supported":49,"title":249,"abstract":250,"tags":254},[],[251,252,253],"物联网","internet of things","IoT",[231],{"code":256,"score":175,"supported":49,"title":257,"abstract":258,"tags":260},"B7",[],[259],"greenhouse",[],{"code":262,"score":175,"supported":49,"title":263,"abstract":264,"tags":266},"C",[],[265],"livestock",[],{"code":174,"score":175,"supported":49,"title":268,"abstract":269,"tags":271},[],[270],"fertigation",[],{"code":273,"score":72,"supported":14,"title":274,"abstract":275,"tags":276},"I6",[],[],[230,232],{"code":71,"score":21,"supported":14,"title":278,"abstract":279,"tags":280},[],[],[30],[282,283],"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":285,"openalex_id":287,"authors":288,"venue":222,"cited_by_count":86,"oa_url":219,"card":304,"direction":93,"ingested_from":94},"W7222165876",[289,292,295,298,301],{"name":290,"orcid":291},"Alvertos Ioannis Mourikis","https:\u002F\u002Forcid.org\u002F0009-0009-2386-2803",{"name":293,"orcid":294},"Iosif Polenakis","https:\u002F\u002Forcid.org\u002F0000-0002-6427-5519",{"name":296,"orcid":297},"Christos Sarantidis","https:\u002F\u002Forcid.org\u002F0009-0003-9486-8483",{"name":299,"orcid":300},"Romanos Kalamatianos","https:\u002F\u002Forcid.org\u002F0009-0000-6939-5424",{"name":302,"orcid":303},"Ioannis Karydis","https:\u002F\u002Forcid.org\u002F0000-0002-9470-2729",{"tldr":305,"method":306,"finding":307,"direction":93,"opportunity":308},"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":311,"title":312,"url":313,"summary":314,"summary_zh":315,"content":9,"source_name":316,"source_url":313,"published_at":317,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":318,"score_detail":319,"sources":321,"tags":323,"primary_topic":327,"secondary_topics":328,"classification":330,"search_phrases":353,"slug":356,"view_count":86,"doi":357,"paper":358,"created_at":376},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":18,"substance":225,"depth":110,"authority":19,"freshness":112,"relevant":21,"comment":320},"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。",[322],{"name":316,"url":313},[30,324,31,325,326],"农业人工智能","节水农业","光伏农业",{"code":236,"slug":237,"name":238,"path":239,"parent_code":240},[329],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":42,"locked":14,"version":43,"method":44,"evidence":331},{"candidates":332,"agricultural_evidence":49},[333,338,342,349],{"code":236,"score":334,"supported":49,"title":335,"abstract":336,"tags":337},17,[253],[251,252,253],[],{"code":35,"score":18,"supported":49,"title":339,"abstract":340,"tags":341},[36],[52,36],[31,325],{"code":343,"score":344,"supported":49,"title":345,"abstract":346,"tags":348},"I2",4,[],[347],"artificial intelligence",[324],{"code":71,"score":21,"supported":14,"title":350,"abstract":351,"tags":352},[],[],[30],[354,355],"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":357,"openalex_id":359,"authors":360,"venue":316,"cited_by_count":86,"oa_url":370,"card":371,"direction":93,"ingested_from":94},"W7221571268",[361,364,367],{"name":362,"orcid":363},"Ahmed Mashaly","https:\u002F\u002Forcid.org\u002F0009-0007-7009-4531",{"name":365,"orcid":366},"Ragab Abdelaziz El-Sehiemy","https:\u002F\u002Forcid.org\u002F0000-0002-3340-4031",{"name":368,"orcid":369},"Mohamed M. Elgohary","https:\u002F\u002Forcid.org\u002F0000-0003-4441-7812","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-74935-0.pdf",{"tldr":372,"method":373,"finding":374,"direction":93,"opportunity":375},"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",{"id":378,"title":379,"url":380,"summary":381,"summary_zh":382,"content":9,"source_name":383,"source_url":380,"published_at":384,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":385,"score_detail":386,"sources":389,"tags":391,"primary_topic":394,"secondary_topics":395,"classification":396,"search_phrases":428,"slug":431,"view_count":86,"doi":432,"paper":433,"created_at":453},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":112,"substance":387,"depth":334,"authority":17,"freshness":20,"relevant":21,"comment":388},21,"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。",[390],{"name":383,"url":380},[30,251,31,392,393],"太阳能","西非农业",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":397},{"candidates":398,"agricultural_evidence":49},[399,403,407,414,418,424],{"code":35,"score":111,"supported":49,"title":400,"abstract":401,"tags":402},[36],[52,36],[31],{"code":236,"score":57,"supported":49,"title":404,"abstract":405,"tags":406},[],[251,253],[251],{"code":408,"score":97,"supported":49,"title":409,"abstract":410,"tags":413},"G2",[],[411,412],"小农户","smallholder",[],{"code":56,"score":97,"supported":49,"title":415,"abstract":416,"tags":417},[],[60,61],[],{"code":419,"score":175,"supported":49,"title":420,"abstract":421,"tags":423},"B1",[],[422],"vegetable",[],{"code":71,"score":21,"supported":14,"title":425,"abstract":426,"tags":427},[],[],[30],[429,430],"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":432,"openalex_id":434,"authors":435,"venue":383,"cited_by_count":86,"oa_url":447,"card":448,"direction":93,"ingested_from":94},"W7221041404",[436,439,442,444],{"name":437,"orcid":438},"Izuchukwu Okafor","https:\u002F\u002Forcid.org\u002F0009-0003-8668-3155",{"name":440,"orcid":441},"Afeez Adewale","https:\u002F\u002Forcid.org\u002F0009-0009-7028-9237",{"name":443,"orcid":9},"Olawale Olugbenga Olarewaju",{"name":445,"orcid":446},"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":449,"method":450,"finding":451,"direction":93,"opportunity":452},"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":455,"title":456,"url":457,"summary":458,"summary_zh":459,"content":9,"source_name":316,"source_url":457,"published_at":384,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":460,"score_detail":461,"sources":463,"tags":465,"primary_topic":466,"secondary_topics":467,"classification":472,"search_phrases":498,"slug":501,"view_count":97,"doi":502,"paper":503,"created_at":520},4567,"An embedded deep learning–fuzzy logic framework for intelligent and automated irrigation in smart farming","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-72706-5","Water scarcity and the need to improve resource efficiency have made intelligent and automated irrigation management an important research challenge in smart farming. This study proposes IrrigaDL-FL, a low-cost automated smart irrigation framework based on the internet of things, integrating intelligent data routing with adaptive irrigation decision-making. The proposed framework consists of two complementary components. First, a Deep Learning–Fuzzy Logic (DL-FL) framework is integrated with the Open Shortest Path First (OSPF) protocol to enhance data routing efficiency in the network. The DL-FL framework combines these heterogeneous characteristics to identify suitable next-hop nodes and establish energy-efficient routing paths. Second, irrigation decision-making is performed using a lightweight Convolutional Neural Network–Fuzzy Logic Controller (CNN-FLC). The CNN-FLC processes sensor-derived and crop-related information, including air temperature, relative humidity, soil moisture, evapotranspiration rate, crop growth coefficient, crop water requirement, planting date, cumulative rainfall, recent rainfall, irrigation history, crop growth stage, and the difference between actual and target soil moisture. These inputs are used to characterize crop hydration stress and dynamically determine the appropriate irrigation program and sprinkler activation duration. The proposed IrrigaDL-FL framework is evaluated through simulations with different numbers of sensor nodes over predefined routing rounds and is compared with several existing approaches. The results demonstrate that IrrigaDL-FL achieves superior performance in terms of network lifetime, residual energy, energy variance, number of transmitted packets, and average path length.","水资源短缺与提高资源效率的需求，使智能化和自动化灌溉管理成为智慧农业中的重要研究挑战。本研究提出IrrigaDL-FL，一种基于物联网的低成本自动化智能灌溉框架，将智能数据路由与自适应灌溉决策相结合。该框架由两个互补部分组成。首先，将深度学习-模糊逻辑（DL-FL）框架与开放最短路径优先（OSPF）协议集成，以提高网络中的数据路由效率。DL-FL框架结合这些异构特征来识别合适的下一跳节点并建立节能路由路径。其次，灌溉决策通过轻量级卷积神经网络-模糊逻辑控制器（CNN-FLC）执行。CNN-FLC处理来自传感器的信息和作物相关信息，包括空气温度、相对湿度、土壤湿度、蒸散速率、作物生长系数、作物需水量、种植日期、累计降雨量、近期降雨量、灌溉历史、作物生长阶段以及实际土壤湿度与目标土壤湿度之间的差值。这些输入用于表征作物水分胁迫，并动态确定合适的灌溉方案和喷灌器启动时长。所提出的IrrigaDL-FL框架通过在预定义路由轮次下对不同数量传感器节点进行仿真评估，并与若干现有方法进行比较。结果表明，IrrigaDL-FL在网络寿命、剩余能量、能量方差、传输数据包数量和平均路径长度方面均取得了更优性能。",77,{"impact":18,"substance":387,"depth":110,"authority":19,"freshness":112,"relevant":21,"comment":462},"提出低成本物联网智能灌溉框架，融合深度学习与模糊逻辑优化路由与灌溉决策，方法新颖、仿真验证充分，对智慧农业节水研究有参考价值。",[464],{"name":316,"url":457},[30,324,231,31,325],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[468],{"code":343,"slug":469,"name":470,"path":471,"parent_code":240},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":42,"locked":14,"version":43,"method":44,"evidence":473},{"candidates":474,"agricultural_evidence":49},[475,480,486,490,494],{"code":35,"score":476,"supported":49,"title":477,"abstract":478,"tags":479},19,[36],[52,53,36,147],[31,325],{"code":343,"score":111,"supported":49,"title":481,"abstract":483,"tags":485},[482],"deep learning",[484,482],"深度学习",[324],{"code":236,"score":57,"supported":49,"title":487,"abstract":488,"tags":489},[],[251,252],[231],{"code":56,"score":97,"supported":49,"title":491,"abstract":492,"tags":493},[],[60,61],[],{"code":71,"score":344,"supported":49,"title":495,"abstract":496,"tags":497},[],[30],[30],[499,500],"IrrigaDL-FL 智能灌溉","CNN-FLC 灌溉决策","IrrigaDL-FL智能灌溉-4567","10.1038\u002Fs41598-026-72706-5",{"doi":502,"openalex_id":504,"authors":505,"venue":316,"cited_by_count":86,"oa_url":514,"card":515,"direction":93,"ingested_from":94},"W7221040139",[506,508,510,512],{"name":507,"orcid":9},"Ze Luo",{"name":509,"orcid":9},"Zhuo Yang",{"name":511,"orcid":9},"Yi Hu",{"name":513,"orcid":9},"Jun Wang","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-72706-5_reference.pdf",{"tldr":516,"method":517,"finding":518,"direction":93,"opportunity":519},"提出IrrigaDL-FL低成本物联网智能灌溉框架，融合深度学习与模糊逻辑优化路由和灌溉决策。","用CNN-模糊逻辑控制器处理多源传感器与作物数据，结合OSPF协议做节能路由。","在仿真中网络寿命、剩余能量、能耗方差、传输包数和平均路径长度均优于现有方法。","可探索真实农田部署下多作物、多气候场景的泛化能力与边缘计算资源约束优化。","2026-10-10T23:30:11.306685Z",{"id":522,"title":523,"url":524,"summary":525,"summary_zh":526,"content":9,"source_name":527,"source_url":524,"published_at":528,"category":12,"cover_url":9,"hotness":48,"is_selected":49,"score":529,"score_detail":530,"sources":532,"tags":534,"primary_topic":536,"secondary_topics":541,"classification":543,"search_phrases":576,"slug":579,"view_count":21,"doi":580,"paper":581,"created_at":604},4371,"Intelligent greenhouse irrigation control via multilayer perceptron neural network climate prediction integrated with fuzzy logic control","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compeleceng.2026.111587","Conventional irrigation methods rely on indoor sensor feedback, which fails to anticipate rapid microclimate fluctuations and often leads to over-irrigation or water stress. Additionally, traditional controllers used in the watering process lack the ability to adjust water flow rate dynamically, resulting in inefficient water usage and energy waste. Intelligent Greenhouse Irrigation Management (IGIM) remains a critical challenge for achieving sustainable, high-yield greenhouse agriculture. Precise irrigation management is especially important for crops like tomatoes, where both timing and quantity directly influence productivity and energy consumption. To address these challenges, this paper proposes a hybrid intelligent system that integrates a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) with an Adaptive Fuzzy Logic Controller (AFLC). The MLP-ANN forecasts key indoor variables such as temperature, humidity, and soil moisture, achieving R² values of 0.999. The Root Mean Square Errors of 0.266 °C, 0.254%, and 0.808%, respectively. These predictions directly feed into the AFLC, which employs an adaptive rule base to dynamically adjust irrigation flow rates in real time (0–18 LPH), significantly enhancing water-use efficiency and reducing waste to save energy consumption in the watering process. The key novelty of this work is the direct integration of MLP-predicted microclimate variables into the antecedent of adaptive fuzzy rules, enabling the controller to anticipate and respond to climate variations before they affect crop conditions. Unlike conventional reactive or static rule-based systems, the proposed MLP-AFLC framework creates a closed-loop coupling between forecasting and actuation, allowing predictive, rather than threshold-based, irrigation decisions. By integrating predictive intelligence with adaptive control, the proposed MLP-AFLC framework provides a robust, autonomous, and sustainable solution for greenhouse management, advancing precision agriculture in highly dynamic environments.","传统灌溉方法依赖室内传感器反馈，无法预判微气候的快速波动，常导致过度灌溉或水分胁迫。此外，灌溉过程中使用的传统控制器缺乏动态调节流速的能力，造成水资源利用效率低下和能源浪费。智能温室灌溉管理（IGIM）仍是实现可持续、高产温室农业的关键挑战。对于番茄等作物而言，精准灌溉管理尤为重要，因为灌溉时机和水量直接影响产量和能耗。为解决这些挑战，本文提出了一种将多层感知器人工神经网络（MLP-ANN）与自适应模糊逻辑控制器（AFLC）相集成的混合智能系统。MLP-ANN预测温度、湿度和土壤湿度等关键室内变量，R²值达到0.999，均方根误差分别为0.266 °C、0.254%和0.808%。这些预测结果直接输入AFLC，后者采用自适应规则库实时动态调节灌溉流速（0–18 LPH），显著提高水资源利用效率并减少浪费，从而在灌溉过程中节约能耗。本工作的关键创新在于将MLP预测的微气候变量直接集成到自适应模糊规则的前件中，使控制器能够在气候变化影响作物状况之前进行预判和响应。与传统反应式或静态规则系统不同，所提出的MLP-AFLC框架在预测与执行之间建立了闭环耦合，实现了基于预测而非基于阈值的灌溉决策。通过将预测智能与自适应控制相结合，所提出的MLP-AFLC框架为温室管理提供了一种稳健、自主且可持续的解决方案，推动了高度动态环境下的精准农业发展。","Computers & Electrical Engineering","2026-10-06T00:00:00Z",81,{"impact":110,"substance":225,"depth":110,"authority":19,"freshness":20,"relevant":21,"comment":531},"提出MLP-ANN与自适应模糊控制融合的温室预测性灌溉框架，R²达0.999，方法新颖且数据可靠，对精准农业节水节能有实质参考价值。",[533],{"name":527,"url":524},[30,324,31,163,535],"温室种植",{"code":256,"slug":537,"name":538,"path":539,"parent_code":540},"protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[542],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":42,"locked":14,"version":43,"method":44,"evidence":544},{"candidates":545,"agricultural_evidence":49},[546,550,555,559,563,568,572],{"code":35,"score":110,"supported":49,"title":547,"abstract":548,"tags":549},[36],[52,53,36],[31],{"code":256,"score":111,"supported":49,"title":551,"abstract":552,"tags":554},[259],[553,259],"温室",[535],{"code":71,"score":112,"supported":49,"title":556,"abstract":557,"tags":558},[],[163,164],[30,163],{"code":56,"score":97,"supported":49,"title":560,"abstract":561,"tags":562},[],[60,61],[],{"code":419,"score":175,"supported":49,"title":564,"abstract":565,"tags":567},[],[566],"番茄",[],{"code":121,"score":175,"supported":49,"title":569,"abstract":570,"tags":571},[],[117],[],{"code":343,"score":21,"supported":14,"title":573,"abstract":574,"tags":575},[],[],[324],[577,578],"MLP-AFLC 温室灌溉","神经网络 模糊控制 灌溉","MLP-AFLC温室灌溉-4371","10.1016\u002Fj.compeleceng.2026.111587",{"doi":580,"openalex_id":582,"authors":583,"venue":527,"cited_by_count":86,"oa_url":524,"card":598,"direction":603,"ingested_from":94},"W7220746994",[584,587,590,592,595],{"name":585,"orcid":586},"Jamel Riahi","https:\u002F\u002Forcid.org\u002F0009-0006-1434-4367",{"name":588,"orcid":589},"Hamza Nasri","https:\u002F\u002Forcid.org\u002F0009-0008-7343-4199",{"name":591,"orcid":9},"Abdelkader Mami",{"name":593,"orcid":594},"Mario Carpentieri","https:\u002F\u002Forcid.org\u002F0000-0001-5165-5873",{"name":596,"orcid":597},"Silvano Vergura","https:\u002F\u002Forcid.org\u002F0000-0002-0826-9237",{"tldr":599,"method":600,"finding":601,"direction":93,"opportunity":602},"提出MLP-ANN预测温室微气候并驱动自适应模糊控制器，实现预测性智能灌溉。","MLP神经网络预测温湿度与土壤水分，结合自适应模糊逻辑控制流量。","预测R²达0.999，模糊控制器动态调节0-18 LPH流量，显著节水节能。","可探索多作物多环境迁移学习与边缘部署，降低预测模型算力需求。","农业人工智能与决策模型","2026-10-07T23:30:43.848308Z"]