[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3975":3,"related-3975":86},{"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,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":55,"slug":58,"view_count":59,"doi":60,"paper":61,"created_at":85},3975,"3D-architectured self-powered thermoelectric plant e-skins for high-precision early-stage water stress detection","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112484","3D-architectured self-powered thermoelectric plant e-skins for high-precision early-stage water stress detection。Computers and Electronics in Agriculture","用于高精度早期水分胁迫检测的3D结构自供电热电植物电子皮肤。《农业计算机与电子学》",null,"Computers and Electronics in Agriculture","2026-10-01T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,20,14,8,1,"核心期刊论文，提出3D结构自供电植物电子皮肤用于早期水分胁迫高精度检测，方法新颖且对精准灌溉有应用价值，值得进入每日精选。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","精准灌溉","水分胁迫","农业传感器","植物可穿戴",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"unclassified","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":53,"reason":54},[38,43,48],{"code":39,"score":21,"supported":14,"title":40,"abstract":41,"tags":42},"H3",[],[],[27],{"code":44,"score":21,"supported":14,"title":45,"abstract":46,"tags":47},"I1",[],[],[26],{"code":49,"score":21,"supported":14,"title":50,"abstract":51,"tags":52},"I5",[],[],[29],true,"缺少足够的主题证据",[56,57],"植物电子皮肤 水分胁迫 检测","自供电 热电 电子皮肤 作物","植物电子皮肤水分胁迫检测-3975",5,"10.1016\u002Fj.compag.2026.112484",{"doi":60,"openalex_id":62,"authors":63,"venue":10,"cited_by_count":77,"oa_url":9,"card":78,"direction":82,"ingested_from":84},"W7215104396",[64,66,68,70,72,74],{"name":65,"orcid":9},"Shuyang Chen",{"name":67,"orcid":9},"Duo Xu",{"name":69,"orcid":9},"Jing Wang",{"name":71,"orcid":9},"Chenyu Wang",{"name":73,"orcid":9},"Xiaobiao Liu",{"name":75,"orcid":76},"Yaling Wang","https:\u002F\u002Forcid.org\u002F0009-0002-1164-3215",0,{"tldr":79,"method":80,"finding":81,"direction":82,"opportunity":83},"提出3D结构自供电热电植物电子皮肤，用于高精度早期水分胁迫检测。","3D热电架构与自供电电子皮肤，用于植物水分胁迫监测。","该电子皮肤可高精度检测植物早期水分胁迫，无需外部供电。","其他","可探索自供电柔性传感器在作物水分胁迫早期预警中的田间部署与多指标融合。","openalex","2026-10-02T23:30:02.528077Z",{"total":87,"page":21,"page_size":87,"items":88},6,[89,182,295,373,484,548],{"id":90,"title":91,"url":92,"summary":93,"summary_zh":94,"content":9,"source_name":10,"source_url":92,"published_at":95,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":96,"score_detail":97,"sources":101,"tags":103,"primary_topic":106,"secondary_topics":112,"classification":118,"search_phrases":157,"slug":160,"view_count":161,"doi":162,"paper":163,"created_at":181},3131,"Water stress detection from plant electrophysiology: A machine learning framework for irrigation management","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112434","Fast detection of plant stress is key to plant phenotyping, precision agriculture, and automated crop management. In particular, efficient irrigation management requires early identification of water stress to optimize resource use while maintaining crop performance. Direct physiological sensing offers the potential to detect stress responses before visible symptoms appear. In this study, we recorded electrophysiological signals from greenhouse-grown tomato plants subjected to water stress and developed a framework based on machine learning for online stress detection. The resulting time-series data were segmented into tumbling windows of 1 min, 5 min, 30 min, 1 h and 6 h, then passed through a processing pipeline that includes statistical feature extraction and selection, automated machine learning or alternatively deep learning, and probability calibration. Across multiple input time horizons, we found that a 30-minute look-back horizon strikes the best balance between rapid decision-making and classification performance. Using automated machine learning, the framework achieved classification accuracies of up to 92%, outperforming deep learning approaches. Sequential backward selection reduced the feature set while maintaining performance. Out-of-sample testing on withheld plant individuals provides methodological validation, confirming that the framework detects healthy-to-stress transitions despite inter-individual electrophysiological variability.Overall, we develop and provide a decision-support tool for agricultural practitioners and researchers and establish a foundation for biofeedback-driven irrigation control to improve resource efficiency in (semi-)autonomous crop production systems.","植物胁迫的快速检测是植物表型分析、精准农业和自动化作物管理的关键。特别是，高效的灌溉管理需要早期识别水分胁迫，以优化资源利用并维持作物表现。直接生理传感有望在可见症状出现之前检测到胁迫响应。在本研究中，我们记录了温室种植的番茄植株在水分胁迫下的电生理信号，并开发了一个基于机器学习的在线胁迫检测框架。所得时间序列数据被分割为1分钟、5分钟、30分钟、1小时和6小时的滑动窗口，然后通过一个处理流程，包括统计特征提取与选择、自动化机器学习或深度学习，以及概率校准。在多个输入时间跨度中，我们发现30分钟的回溯窗口在快速决策与分类性能之间取得了最佳平衡。使用自动化机器学习，该框架达到了高达92%的分类准确率，优于深度学习方法。序列后向选择在保持性能的同时减少了特征集。对保留的植株个体进行样本外测试提供了方法学验证，确认该框架能够在个体间电生理变异性存在的情况下检测从健康到胁迫的转变。总体而言，我们为农业从业者和研究人员开发并提供了一种决策支持工具，并为生物反馈驱动的灌溉控制奠定了基础，以提高（半）自主作物生产系统中的资源效率。","2026-09-21T00:00:00Z",81,{"impact":17,"substance":98,"depth":17,"authority":19,"freshness":99,"relevant":21,"comment":100},22,9,"该研究提出基于植物电生理信号与机器学习的在线水分胁迫检测框架，30分钟窗口下准确率达92%，为精准灌溉与生物反馈控制提供决策支持，方法新颖、结论可靠，值得进入每日精选。",[102],{"name":10,"url":92},[26,104,27,28,105],"农业人工智能","植物电生理",{"code":107,"slug":108,"name":109,"path":110,"parent_code":111},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[113],{"code":39,"slug":114,"name":115,"path":116,"parent_code":117},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":119,"locked":14,"version":34,"method":35,"evidence":120},"classified",{"candidates":121,"agricultural_evidence":53},[122,130,136,143,150],{"code":107,"score":17,"supported":53,"title":123,"abstract":125,"tags":129},[124],"machine learning",[126,127,124,128],"深度学习","机器学习","deep learning",[104],{"code":39,"score":131,"supported":53,"title":132,"abstract":133,"tags":135},15,[114],[134,114],"灌溉",[27],{"code":44,"score":137,"supported":53,"title":138,"abstract":139,"tags":142},7,[],[140,141],"精准农业","precision agriculture",[26],{"code":144,"score":87,"supported":53,"title":145,"abstract":146,"tags":149},"B1",[],[147,148],"番茄","tomato",[],{"code":151,"score":87,"supported":53,"title":152,"abstract":153,"tags":156},"B7",[],[154,155],"温室","greenhouse",[],[158,159],"植物电生理 水分胁迫 机器学习","番茄 水分胁迫 灌溉管理","植物电生理水分胁迫机器学习-3131",4,"10.1016\u002Fj.compag.2026.112434",{"doi":162,"openalex_id":164,"authors":165,"venue":10,"cited_by_count":77,"oa_url":92,"card":175,"direction":180,"ingested_from":84},"W7204541435",[166,169,172],{"name":167,"orcid":168},"Eduard Buss","https:\u002F\u002Forcid.org\u002F0000-0001-6993-5873",{"name":170,"orcid":171},"Till Aust","https:\u002F\u002Forcid.org\u002F0000-0003-2863-1341",{"name":173,"orcid":174},"Heiko Hamann","https:\u002F\u002Forcid.org\u002F0000-0002-2458-8289",{"tldr":176,"method":177,"finding":178,"direction":82,"opportunity":179},"利用番茄电生理信号与机器学习实现水分胁迫早期在线检测。","温室番茄电生理时序，滑窗分段、特征选择、自动机器学习与概率校准。","30分钟回溯窗口最优，自动机器学习分类准确率达92%，优于深度学习。","可探索多作物电生理泛化、田间部署及闭环灌溉控制，提升资源效率。","农业遥感与作物表型","2026-09-22T23:30:02.024960Z",{"id":183,"title":184,"url":185,"summary":186,"summary_zh":187,"content":9,"source_name":188,"source_url":185,"published_at":189,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":190,"score_detail":191,"sources":194,"tags":196,"primary_topic":199,"secondary_topics":200,"classification":206,"search_phrases":265,"slug":268,"view_count":77,"doi":269,"paper":270,"created_at":294},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today","2026-10-21T00:00:00Z",79,{"impact":17,"substance":98,"depth":17,"authority":192,"freshness":99,"relevant":21,"comment":193},12,"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[195],{"name":188,"url":185},[26,104,197,198,27],"遥感","作物水分胁迫",{"code":39,"slug":114,"name":115,"path":116,"parent_code":117},[201,202],{"code":107,"slug":108,"name":109,"path":110,"parent_code":111},{"code":49,"slug":203,"name":204,"path":205,"parent_code":111},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":119,"locked":14,"version":34,"method":35,"evidence":207},{"candidates":208,"agricultural_evidence":53},[209,213,218,225,231,237,244,251,258],{"code":39,"score":131,"supported":53,"title":210,"abstract":211,"tags":212},[114],[134,114],[27],{"code":107,"score":13,"supported":53,"title":214,"abstract":215,"tags":217},[],[127,124,216],"artificial intelligence",[104],{"code":49,"score":99,"supported":53,"title":219,"abstract":220,"tags":224},[],[221,222,223],"物联网","internet of things","IoT",[],{"code":44,"score":137,"supported":53,"title":226,"abstract":227,"tags":230},[],[228,229],"数字孪生","digital twin",[26],{"code":232,"score":137,"supported":53,"title":233,"abstract":234,"tags":236},"I3",[],[197,235],"remote sensing",[197],{"code":238,"score":87,"supported":53,"title":239,"abstract":240,"tags":243},"A2",[],[241,242],"小麦","wheat",[],{"code":245,"score":87,"supported":53,"title":246,"abstract":247,"tags":250},"A3",[],[248,249],"玉米","maize",[],{"code":252,"score":87,"supported":53,"title":253,"abstract":254,"tags":257},"A5",[],[255,256],"马铃薯","potato",[],{"code":259,"score":260,"supported":53,"title":261,"abstract":262,"tags":264},"B2",3,[],[263],"葡萄",[],[266,267],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":269,"openalex_id":271,"authors":272,"venue":188,"cited_by_count":77,"oa_url":185,"card":288,"direction":293,"ingested_from":84},"W7223088921",[273,276,279,282,285],{"name":274,"orcid":275},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":277,"orcid":278},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":280,"orcid":281},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":283,"orcid":284},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":286,"orcid":287},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":289,"method":290,"finding":291,"direction":180,"opportunity":292},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.526683Z",{"id":296,"title":297,"url":298,"summary":299,"summary_zh":300,"content":9,"source_name":301,"source_url":298,"published_at":302,"category":12,"cover_url":9,"hotness":303,"is_selected":14,"score":304,"score_detail":305,"sources":310,"tags":314,"primary_topic":317,"secondary_topics":318,"classification":320,"search_phrases":350,"slug":353,"view_count":77,"doi":354,"paper":355,"created_at":372},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":98,"substance":306,"depth":307,"authority":308,"freshness":77,"relevant":21,"comment":309},21,17,13,"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。",[311,312],{"name":301,"url":298},{"name":301,"url":313},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[26,104,315,316,27],"农业物联网","区块链溯源",{"code":39,"slug":114,"name":115,"path":116,"parent_code":117},[319],{"code":49,"slug":203,"name":204,"path":205,"parent_code":111},{"status":119,"locked":14,"version":34,"method":35,"evidence":321},{"candidates":322,"agricultural_evidence":53},[323,327,331,335,341,346],{"code":39,"score":131,"supported":53,"title":324,"abstract":325,"tags":326},[114],[134,114],[27],{"code":49,"score":13,"supported":53,"title":328,"abstract":329,"tags":330},[],[221,222,223],[315],{"code":107,"score":161,"supported":53,"title":332,"abstract":333,"tags":334},[],[216],[104],{"code":336,"score":260,"supported":53,"title":337,"abstract":338,"tags":340},"G2",[],[339],"小农户",[],{"code":342,"score":21,"supported":14,"title":343,"abstract":344,"tags":345},"E4",[],[],[316],{"code":44,"score":21,"supported":14,"title":347,"abstract":348,"tags":349},[],[],[26],[351,352],"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":354,"openalex_id":356,"authors":357,"venue":301,"cited_by_count":77,"oa_url":298,"card":366,"direction":293,"ingested_from":84},"W7223034914",[358,361,363],{"name":359,"orcid":360},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":362,"orcid":9},"Dr.T.Baskar",{"name":364,"orcid":365},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":367,"method":368,"finding":369,"direction":370,"opportunity":371},"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":374,"title":375,"url":376,"summary":377,"summary_zh":378,"content":9,"source_name":379,"source_url":376,"published_at":380,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":381,"score_detail":382,"sources":384,"tags":386,"primary_topic":390,"secondary_topics":395,"classification":396,"search_phrases":455,"slug":458,"view_count":260,"doi":459,"paper":460,"created_at":483},5081,"Nanobubble-Driven Precision Aeration: Deciphering Abiotic Hypoxia Responses via Multi-Omics for Next-Generation Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fantiox15101332","Soil salinity and drought can restrict oxygen availability in the root zone, adding hypoxic stress to the ionic, osmotic, and hydraulic constraints experienced by crops. Nanobubble (NB) water offers a chemical-free approach for delivering highly dispersed oxygen and sustaining dissolved oxygen during irrigation. This review connects the physicochemical behavior of NBs with plant responses to salinity- and drought-associated hypoxia. It examines restoration of aerobic respiration and ATP homeostasis, modulation of ethylene response factor group VII and hypoxia-responsive gene signaling, recovery of ion transport and aquaporin function, redox regulation, hormonal cross-talk, and rhizosphere microbiome responses. Evidence from rice, maize, wheat, cotton, and controlled-environment systems is evaluated alongside opportunities for nanoparticle co-delivery, nanofertilizers, seed priming, and sensor-guided irrigation. The review also outlines how transcriptomics, proteomics, metabolomics, epigenomics, ionomics, and phenomics can resolve NB-mediated mechanisms across the plant–soil continuum. Current evidence supports NB irrigation as a promising precision-aeration platform, but field-scale validation, standardized NB characterization, dose–response studies, energy and cost assessments, and long-term environmental safety analyses remain necessary before broad agricultural deployment.","土壤盐渍化和干旱会限制根区氧气供应，在作物已承受的离子、渗透和水力胁迫之外，进一步叠加低氧胁迫。纳米气泡（NB）水提供了一种无需化学添加的方式，可输送高度分散的氧气并在灌溉过程中维持溶解氧水平。本文综述将纳米气泡的物理化学行为与植物对盐渍化和干旱相关低氧的响应联系起来，考察有氧呼吸和ATP稳态的恢复、乙烯响应因子第VII组及低氧响应基因信号的调控、离子转运和水通道蛋白功能的恢复、氧化还原调节、激素互作以及根际微生物组响应。文章评估了来自水稻、玉米、小麦、棉花及受控环境系统的证据，并探讨了纳米颗粒共递送、纳米肥料、种子引发和传感器引导灌溉的应用机会。综述还概述了转录组学、蛋白质组学、代谢组学、表观基因组学、离子组学和表型组学如何解析纳米气泡在植物–土壤连续体中的介导机制。当前证据支持纳米气泡灌溉作为一种有前景的精准增氧平台，但在广泛农业应用之前，仍需进行田间规模验证、标准化纳米气泡表征、剂量–响应研究、能耗与成本评估以及长期环境安全性分析。","Antioxidants","2026-10-15T00:00:00Z",80,{"impact":17,"substance":306,"depth":17,"authority":19,"freshness":99,"relevant":21,"comment":383},"综述系统梳理纳米气泡增氧缓解盐旱缺氧胁迫的机制与多组学解析路径，学术价值高但尚处田间验证前阶段，属细分领域前沿进展。",[385],{"name":379,"url":376},[26,27,387,388,389],"多组学","土壤盐渍化","纳米气泡",{"code":391,"slug":392,"name":393,"path":394,"parent_code":117},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[],{"status":119,"locked":14,"version":34,"method":35,"evidence":397},{"candidates":398,"agricultural_evidence":53},[399,406,411,415,422,426,430,437,444,450],{"code":391,"score":99,"supported":53,"title":400,"abstract":401,"tags":405},[],[402,403,392,404],"土壤","根际","rhizosphere",[],{"code":44,"score":99,"supported":53,"title":407,"abstract":409,"tags":410},[408],"smart agriculture",[],[26],{"code":39,"score":137,"supported":53,"title":412,"abstract":413,"tags":414},[],[134,114],[27],{"code":416,"score":87,"supported":53,"title":417,"abstract":418,"tags":421},"A1",[],[419,420],"水稻","rice",[],{"code":238,"score":87,"supported":53,"title":423,"abstract":424,"tags":425},[],[241,242],[],{"code":245,"score":87,"supported":53,"title":427,"abstract":428,"tags":429},[],[248,249],[],{"code":431,"score":87,"supported":53,"title":432,"abstract":433,"tags":436},"A6",[],[434,435],"棉花","cotton",[],{"code":438,"score":87,"supported":53,"title":439,"abstract":440,"tags":443},"H7",[],[441,442],"干旱","drought",[],{"code":445,"score":260,"supported":53,"title":446,"abstract":447,"tags":449},"F3",[],[448],"肥料",[],{"code":451,"score":21,"supported":14,"title":452,"abstract":453,"tags":454},"F2",[],[],[387],[456,457],"纳米气泡 灌溉 增氧","盐碱地 缺氧 多组学","纳米气泡灌溉增氧-5081","10.3390\u002Fantiox15101332",{"doi":459,"openalex_id":461,"authors":462,"venue":379,"cited_by_count":77,"oa_url":376,"card":478,"direction":293,"ingested_from":84},"W7222342675",[463,466,469,472,475],{"name":464,"orcid":465},"Hafiz Abdul Kareem","https:\u002F\u002Forcid.org\u002F0000-0002-8209-0446",{"name":467,"orcid":468},"Sana Saleem","https:\u002F\u002Forcid.org\u002F0000-0001-9836-2744",{"name":470,"orcid":471},"Muhammad Bilal Hafeez","https:\u002F\u002Forcid.org\u002F0000-0001-6719-6667",{"name":473,"orcid":474},"Naeem Ahmad","https:\u002F\u002Forcid.org\u002F0000-0002-5023-8362",{"name":476,"orcid":477},"Da‐Chuan Yin","https:\u002F\u002Forcid.org\u002F0000-0003-2161-5867",{"tldr":479,"method":480,"finding":481,"direction":293,"opportunity":482},"综述纳米气泡灌溉缓解盐旱根区缺氧的机制，并展望多组学与智能农业应用。","综述多组学（转录\u002F蛋白\u002F代谢\u002F表型等）与纳米气泡水灌溉证据。","纳米气泡灌溉可恢复呼吸与离子稳态，但田间验证与标准仍缺乏。","可研究纳米气泡剂量-响应与传感器引导灌溉闭环，结合多组学解析根际机制。","2026-10-16T23:30:09.487745Z",{"id":485,"title":486,"url":487,"summary":488,"summary_zh":489,"content":9,"source_name":490,"source_url":487,"published_at":491,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":492,"score_detail":493,"sources":496,"tags":498,"primary_topic":502,"secondary_topics":508,"classification":509,"search_phrases":533,"slug":536,"view_count":21,"doi":537,"paper":538,"created_at":547},4995,"Визначення положення та геометричних параметрів сільськогосподарської техніки для автоматизованого керування процесом миття","https:\u002F\u002Fdoi.org\u002F10.15276\u002Fict.03.2026.76","The article develops and experimentally verifies a method for determining the position and geometric parameters of agricultural machinery for automated washing process control.The method combines photoelectric sensors for verifying the admissible position of the machinery and ultrasonic sensors for determining distances to its upper and side surfaces.Median filtering is used to reduce the influence of anomalous ultrasonic measurements, while the measured distances are converted into geometric parameters using the known dimensions of the experimental installation.The obtained parameters are used by the controller to define the permissible operating area and generate control actions for the washing carriage, pump, and air-blowing system.If the machinery is positioned incorrectly or exceeds the permissible dimensions of the working zone, the technological cycle is blocked.Experimental verification using the developed automated washing installation and a tractor model confirmed correct operation of the positioning subsystem and sufficient accuracy of ultrasonic measurements.The maximum absolute error did not exceed 2 mm.The proposed approach provides direct integration of sensor measurements into the control logic and enables adaptation of the washing cycle to the actual position and dimensions of the machinery.Further research should address the integration of computer vision and adaptive generation of washing-carriage trajectories.","本文提出并实验验证了一种用于自动化清洗过程控制的农业机械位置与几何参数确定方法。该方法结合光电传感器验证机械的允许位置，并利用超声波传感器确定其顶面和侧面距离。采用中值滤波降低异常超声测量的影响，同时利用实验装置的已知尺寸将测量距离转换为几何参数。控制器利用所得参数确定允许作业区域，并生成对清洗小车、水泵和吹风系统的控制动作。若机械位置不正确或超出工作区允许尺寸，则封锁工艺循环。利用所开发的自动化清洗装置和拖拉机模型进行的实验验证，证实了定位子系统的正确运行以及超声测量的足够精度。最大绝对误差不超过2 mm。所提方法实现了传感器测量与控制逻辑的直接集成，并使清洗循环能够适应机械的实际位置和尺寸。后续研究应关注计算机视觉的集成以及清洗小车轨迹的自适应生成。","Ìnformatika, kulʹtura, tehnìka.","2026-10-13T00:00:00Z",66,{"impact":192,"substance":18,"depth":494,"authority":13,"freshness":20,"relevant":21,"comment":495},16,"该论文提出光电与超声传感器融合的农机自动清洗定位方法，实验误差不超过2毫米，方法新颖且数据可靠，但属细分技术进展，公共影响有限。",[497],{"name":490,"url":487},[26,499,29,500,501],"农机装备","自动控制","超声测距",{"code":503,"slug":504,"name":505,"path":506,"parent_code":507},"F5","machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery","F",[],{"status":119,"locked":14,"version":34,"method":35,"evidence":510},{"candidates":511,"agricultural_evidence":53},[512,519,525,529],{"code":503,"score":13,"supported":53,"title":513,"abstract":514,"tags":518},[],[515,516,517],"农业机械","agricultural machinery","tractor",[499],{"code":107,"score":87,"supported":53,"title":520,"abstract":521,"tags":524},[],[522,523],"计算机视觉","computer vision",[],{"code":44,"score":21,"supported":14,"title":526,"abstract":527,"tags":528},[],[],[26],{"code":49,"score":21,"supported":14,"title":530,"abstract":531,"tags":532},[],[],[29],[534,535],"农业机械 自动清洗 定位","超声波传感器 农机 几何参数","农业机械自动清洗定位-4995","10.15276\u002Fict.03.2026.76",{"doi":537,"openalex_id":539,"authors":540,"venue":490,"cited_by_count":77,"oa_url":541,"card":542,"direction":293,"ingested_from":84},"W7221711885",[],"https:\u002F\u002Fict.od.ua\u002Findex.php\u002Fjournal\u002Fuk\u002Farticle\u002Fdownload\u002F186\u002F185\u002F620",{"tldr":543,"method":544,"finding":545,"direction":293,"opportunity":546},"开发并实验验证一种基于光电与超声传感器确定农机位置和几何参数以自动控制清洗过程的方法。","光电传感器验证位置，超声传感器测距，中值滤波降噪，转换为几何参数供控制器使用。","定位子系统运行正确，超声测量最大绝对误差不超过2毫米，可自适应调整清洗周期。","可融合计算机视觉与自适应轨迹生成，提升农机清洗的智能化与柔性化水平。","2026-10-15T23:30:11.641686Z",{"id":549,"title":550,"url":551,"summary":552,"summary_zh":553,"content":9,"source_name":554,"source_url":551,"published_at":555,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":556,"sources":558,"tags":560,"primary_topic":562,"secondary_topics":563,"classification":564,"search_phrases":593,"slug":596,"view_count":597,"doi":598,"paper":599,"created_at":616},4810,"Smart water management in agriculture: convergence of sensors, imaging, AI, precision irrigation, and robotics systems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrwa.2026.1973897","Agricultural irrigation systems can bear the heavy losses of water through runoff, evaporation, and deep percolation. Crop water requirements vary with crop species, growth stage, soil properties, evapotranspiration, weather, and plant water status; therefore, irrigation scheduling based on a single parameter is insufficient. Conventional water management systems often rely on uniform application, fixed scheduling, and limited real-time information, which overlooks spatial and temporal variation in soil, crop, and weather conditions and can result in water loss, over-irrigation, and under-irrigation. This review highlights studies on the integration of AI-driven sensing and imaging technologies, IoT-based precision irrigation, variable-rate technologies, digital twins, and agricultural robotics for crop water management. These approaches integrate field, crop, weather, and spatial information to support water-demand assessment, crop-stress monitoring, irrigation scheduling, site-specific application, and automated water management. The reviewed studies report water savings, improved water-use efficiency, crop performance, and more site-specific irrigation and input application across different crops and irrigation systems. The review also highlights the limitations of these systems, including sensing reliability, model transferability, connectivity, interoperability, cybersecurity, investment costs, and environmental effects. Overall, the integration of these technologies provides potential for timely and field-specific irrigation decisions and supports efficient and climate-resilient water management in agriculture.","农业灌溉系统会因径流、蒸发和深层渗漏而承受严重的水量损失。作物需水量随作物种类、生长阶段、土壤性质、蒸散量、天气和植株水分状况而变化；因此，仅基于单一参数的灌溉调度是不够的。传统的水管理系统往往依赖均匀灌溉、固定调度和有限的实时信息，忽视了土壤、作物和天气条件的时空变异，可能导致水分损失、过度灌溉和灌溉不足。本文综述了将人工智能驱动的传感与成像技术、基于物联网的精准灌溉、变量灌溉技术、数字孪生和农业机器人集成应用于作物水分管理的研究。这些方法整合了田块、作物、天气和空间信息，以支持需水评估、作物胁迫监测、灌溉调度、定点变量灌溉和自动化水分管理。所综述的研究报告了在不同作物和灌溉系统中实现的节水、水分利用效率提升、作物表现改善以及更具田块针对性的灌溉和投入品施用。本文还指出了这些系统的局限性，包括传感可靠性、模型可迁移性、连通性、互操作性、网络安全、投资成本和环境影响。总体而言，这些技术的集成可为及时且针对具体田块的灌溉决策提供可能，并支持农业中高效且具有气候韧性的水分管理。","Frontiers in Water","2026-10-12T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":308,"freshness":99,"relevant":21,"comment":557},"系统综述AI传感、物联网精准灌溉与农业机器人集成，方法视角新、结论可靠，对智慧农业水资源管理有参考价值。",[559],{"name":554,"url":551},[26,104,561,228,27],"农业机器人",{"code":39,"slug":114,"name":115,"path":116,"parent_code":117},[],{"status":119,"locked":14,"version":34,"method":35,"evidence":565},{"candidates":566,"agricultural_evidence":53},[567,572,576,580,584,589],{"code":39,"score":17,"supported":53,"title":568,"abstract":569,"tags":571},[114],[134,114,570],"evapotranspiration",[27],{"code":391,"score":87,"supported":53,"title":573,"abstract":574,"tags":575},[],[402,392],[],{"code":49,"score":87,"supported":53,"title":577,"abstract":578,"tags":579},[],[221,223],[],{"code":44,"score":59,"supported":53,"title":581,"abstract":582,"tags":583},[],[228],[228,26],{"code":585,"score":161,"supported":53,"title":586,"abstract":587,"tags":588},"F6",[],[561],[561],{"code":107,"score":21,"supported":14,"title":590,"abstract":591,"tags":592},[],[],[104],[594,595],"精准灌溉 传感器 人工智能","农业机器人 水资源管理","精准灌溉传感器人工智能-4810",2,"10.3389\u002Ffrwa.2026.1973897",{"doi":598,"openalex_id":600,"authors":601,"venue":554,"cited_by_count":77,"oa_url":610,"card":611,"direction":293,"ingested_from":84},"W7221563526",[602,605,607],{"name":603,"orcid":604},"Sachin Ghanghas","https:\u002F\u002Forcid.org\u002F0000-0001-9231-2527",{"name":606,"orcid":9},"Naveen Sihag",{"name":608,"orcid":609},"Parveen Dhanger","https:\u002F\u002Forcid.org\u002F0000-0001-5250-0275","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fwater\u002Farticles\u002F10.3389\u002Ffrwa.2026.1973897\u002Fpdf",{"tldr":612,"method":613,"finding":614,"direction":293,"opportunity":615},"综述AI传感、成像、物联网精准灌溉、数字孪生与机器人集成用于作物水分管理。","综述融合AI传感成像、IoT精准灌溉、变量技术、数字孪生与农业机器人。","集成方案可节水、提升水分利用效率与作物表现，但存在传感可靠性、模型迁移等局限。","可研究多源传感与数字孪生融合的跨区域模型迁移及低成本、安全互操作的灌溉决策系统。","2026-10-13T23:30:10.375774Z"]