[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3504":3,"related-3504":108},{"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":31,"secondary_topics":37,"classification":49,"search_phrases":75,"slug":78,"view_count":79,"doi":80,"paper":81,"created_at":107},3504,"WaterFormer-MS: A multi-stream deep learning framework for surface water segmentation from satellite imagery","https:\u002F\u002Fdoi.org\u002F10.24850\u002Fj-tyca-18-03-08","Accurate mapping of surface water bodies is essential for dam monitoring, irrigation planning, and sustainable water resource management. Conventional methods based on spectral indices, such as NDWI, can face challenges in complex environments due to spectral ambiguity, seasonal variability, and urban or vegetated surfaces. Deep learning approaches such as U-Net and DeepLabv3+ have improved water-body segmentation; however, accurately delineating fine boundaries and detecting small water bodies remain challenging. To address these limitations, this study proposes WaterFormer-MS, a multi-stream deep learning framework for surface water segmentation from Sentinel-2 imagery. The architecture integrates spectral, geometric, and contextual information through dedicated processing streams and combines the extracted features using a convolutional fusion module enhanced with CBAM attention. This design aims to improve feature representation and support accurate water-body delineation. The proposed model was trained and evaluated using Sentinel-2 imagery from a publicly available Kaggle dataset containing diverse land-cover conditions, including urban, vegetated, and agricultural environments. At 300 epochs, WaterFormer-MS achieved an overall accuracy of 97.4%, a precision of 93.2%, a recall of 94.2%, an F1-score of 93.7%, and an IoU of 89.7%. Within the experimental framework, WaterFormer-MS achieved higher values than the other proposed variants. Comparisons with conventional NDWI-based approaches and published deep learning methods suggest competitive performance; however, these comparisons should be interpreted cautiously because datasets, preprocessing procedures, training protocols, and evaluation settings may differ. The results support the potential of the proposed framework for surface water segmentation and environmental monitoring applications in diverse remote sensing scenarios.","准确绘制地表水体分布对于大坝监测、灌溉规划和水资源可持续管理至关重要。基于NDWI等光谱指数的传统方法在复杂环境中可能因光谱模糊性、季节性变化以及城市或植被覆盖地表而面临挑战。U-Net和DeepLabv3+等深度学习方法改善了水体分割效果，但精确描绘精细边界和检测小型水体仍然具有挑战性。为解决这些局限，本研究提出WaterFormer-MS，一种用于Sentinel-2影像地表水分割的多流深度学习框架。该架构通过专门的处理流整合光谱、几何和上下文信息，并利用经CBAM注意力增强的卷积融合模块将提取的特征进行融合。该设计旨在改善特征表征并支持精确的水体描绘。所提模型使用来自公开Kaggle数据集的Sentinel-2影像进行训练和评估，该数据集包含城市、植被和农业环境等多种土地覆盖条件。在300个训练轮次时，WaterFormer-MS取得了97.4%的总体精度、93.2%的精确率、94.2%的召回率、93.7%的F1分数和89.7%的IoU。在实验框架内，WaterFormer-MS取得了高于其他所提变体的数值。与传统基于NDWI的方法及已发表的深度学习方法进行比较，结果表明其性能具有竞争力；然而，这些比较应谨慎解读，因为数据集、预处理流程、训练方案和评估设置可能存在差异。研究结果支持所提框架在多样化遥感场景中用于地表水分割和环境监测应用的潜力。",null,"Tecnología y Ciencias del Agua","2026-09-24T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},15,22,18,9,1,"提出多流深度学习框架用于卫星影像地表水提取，精度指标明确，对灌溉规划与水资源监测有参考价值，但属方法类论文，产业影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业人工智能","Sentinel-2","遥感","水资源管理","地表水体提取",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[38,44],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"code":45,"slug":46,"name":47,"path":48,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":50,"locked":14,"version":51,"method":52,"evidence":53},"classified","2026-10-22.2","rules",{"candidates":54,"agricultural_evidence":56},[55,62,69],{"code":32,"score":17,"supported":56,"title":57,"abstract":59,"tags":61},true,[58],"deep learning",[60,58],"深度学习",[26],{"code":39,"score":13,"supported":56,"title":63,"abstract":64,"tags":68},[],[65,66,40,67],"灌溉","水资源","water resource",[29],{"code":45,"score":70,"supported":56,"title":71,"abstract":72,"tags":74},8,[],[28,73],"remote sensing",[27,28],[76,77],"WaterFormer-MS 地表水 分割","Sentinel-2 水体提取 深度学习","WaterFormer-MS地表水分割-3504",2,"10.24850\u002Fj-tyca-18-03-08",{"doi":80,"openalex_id":82,"authors":83,"venue":10,"cited_by_count":99,"oa_url":6,"card":100,"direction":104,"ingested_from":106},"W7214228825",[84,87,90,93,96],{"name":85,"orcid":86},"Ayoub Benali","https:\u002F\u002Forcid.org\u002F0000-0002-8989-755X",{"name":88,"orcid":89},"Yesma Bendaha","https:\u002F\u002Forcid.org\u002F0000-0002-1347-856X",{"name":91,"orcid":92},"Malika Seddik Bouchouicha","https:\u002F\u002Forcid.org\u002F0009-0001-6092-784X",{"name":94,"orcid":95},"Zakaria Bellahcene","https:\u002F\u002Forcid.org\u002F0000-0003-4353-7871",{"name":97,"orcid":98},"Rachid Rimani","https:\u002F\u002Forcid.org\u002F0000-0002-1923-4312",0,{"tldr":101,"method":102,"finding":103,"direction":104,"opportunity":105},"提出多流深度学习框架WaterFormer-MS，从Sentinel-2影像中精准分割地表水体。","多流架构融合光谱、几何与上下文特征，CBAM注意力卷积融合，Sentinel-2","300轮训练下总体精度97.4%、IoU 89.7%，优于NDWI及对比深度学习方法。","农业遥感与作物表型","可探索多流框架在农业灌溉水体与小型水塘精细提取中的迁移，并引入时序Sentinel-2提升季节鲁棒性。","openalex","2026-09-25T23:30:31.338437Z",{"total":109,"page":21,"page_size":109,"items":110},6,[111,217,271,377,440,514],{"id":112,"title":113,"url":114,"summary":115,"summary_zh":116,"content":9,"source_name":117,"source_url":114,"published_at":118,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":119,"score_detail":120,"sources":124,"tags":126,"primary_topic":130,"secondary_topics":131,"classification":134,"search_phrases":180,"slug":183,"view_count":122,"doi":184,"paper":185,"created_at":216},1895,"Continuous monitoring of deficit irrigation in avocado across two contrasting rainfall years using sensor networks, telemetry, and machine learning","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.09.03.745400","Water scarcity and increasingly irregular rainfall threaten avocado production in Mediterranean regions, yet the long term physiological responses of mature trees to sustained deficit irrigation remain poorly understood. We conducted a two-year field study integrating continuous monitoring of the soil plant atmosphere continuum, drone-based multispectral imaging, canopy structural analysis, and fruit phenotyping in a mature avocado orchard subjected to three irrigation regimes. The two study years differed markedly in rainfall, providing a unique opportunity to evaluate how environmental conditions modulate tree responses to water limitation. Trees under severe deficit irrigation showed depletion of water in deeper soil layers and a flattened physiological profile, with near-zero diel variation in leaf thickness and trunk water potential, indicating minimal transpiration and decoupling of tree water status from environmental demand. Drone telemetry via NDVI detected stress during fruit growth and maturation, but not during flowering or the new summer leaf flush, revealing greater drought sensitivity at later maturation stages. Although canopy area did not differ among irrigation treatments, canopy surface roughness increased significantly under deficit irrigation, thereby identifying a novel structural indicator of drought stress. Despite large physiological differences among treatments, fruit number remained stable, while fruit weight decreased significantly under severe deficit irrigation, particularly in the wetter year, suggesting that annual rainfall modulates the trade-off between fruit retention and fruit growth. This study provides the first continuous, multi-scale characterization of avocado performance under sustained deficit irrigation in Mediterranean conditions. By integrating plant-based sensors, remote sensing, and artificial intelligence, we reveal previously undescribed stress dynamics and identify new indicators for precision irrigation management in fruit crops.","水资源短缺和日益不规律的降雨威胁着地中海地区鳄梨的生产，然而，长期亏缺灌溉下成熟树木的生理响应仍知之甚少。我们开展了一项为期两年的田间研究，在成熟鳄梨园中设置了三种灌溉制度，整合了土壤-植物-大气连续体的连续监测、无人机多光谱成像、冠层结构分析以及果实表型鉴定。两个研究年份的降雨量差异显著，为评估环境条件如何调节树木对水分限制的响应提供了独特机会。严重亏缺灌溉下的树木表现出深层土壤水分耗竭和生理特征趋于平缓的现象，叶片厚度和树干水势的昼夜变化接近于零，表明蒸腾作用极低，树木水分状态与环境需求脱钩。通过NDVI进行的无人机遥测在果实生长和成熟期检测到胁迫，但在开花期或夏季新梢生长期未检测到，揭示了后期成熟阶段对干旱的敏感性更高。尽管各灌溉处理间冠层面积无显著差异，但亏缺灌溉下冠层表面粗糙度显著增加，从而识别出一种新的干旱胁迫结构指标。尽管各处理间生理差异显著，果实数量保持稳定，而严重亏缺灌溉下果实重量显著下降，尤其在较湿润的年份更为明显，表明年降雨量调节着果实保留与果实生长之间的权衡。本研究首次提供了地中海条件下持续亏缺灌溉下鳄梨表现的多尺度连续表征。通过整合基于植物的传感器、遥感技术和人工智能，我们揭示了此前未被描述的胁迫动态，并为果树精准灌溉管理识别了新指标。","bioRxiv (Cold Spring Harbor Laboratory)","2026-09-07T00:00:00Z",73,{"impact":19,"substance":18,"depth":19,"authority":121,"freshness":122,"relevant":21,"comment":123},12,3,"研究利用传感器网络与机器学习持续监测牛油果缺水胁迫，揭示新结构指标，对精准灌溉有重要参考价值。",[125],{"name":117,"url":114},[127,26,28,128,129],"智慧农业","精准灌溉","牛油果",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[132,133],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":45,"slug":46,"name":47,"path":48,"parent_code":36},{"status":50,"locked":14,"version":51,"method":52,"evidence":135},{"candidates":136,"agricultural_evidence":56},[137,141,147,153,161,168,175],{"code":39,"score":19,"supported":56,"title":138,"abstract":139,"tags":140},[40],[65,66,40],[128],{"code":32,"score":121,"supported":56,"title":142,"abstract":144,"tags":146},[143],"machine learning",[145],"artificial intelligence",[26],{"code":45,"score":13,"supported":56,"title":148,"abstract":149,"tags":152},[],[28,150,73,151],"无人机多光谱","multispectral",[28],{"code":154,"score":155,"supported":56,"title":156,"abstract":157,"tags":160},"B2",7,[],[158,159],"果树","orchard",[129],{"code":162,"score":109,"supported":56,"title":163,"abstract":164,"tags":167},"H2",[],[165,166],"土壤","soil",[],{"code":169,"score":109,"supported":56,"title":170,"abstract":171,"tags":174},"H7",[],[172,173],"干旱","drought",[],{"code":176,"score":21,"supported":14,"title":177,"abstract":178,"tags":179},"I1",[],[],[127],[181,182],"农业人工智能 智慧农业 精准灌溉 牛油果","农业人工智能 智慧农业","农业人工智能智慧农业精准灌溉牛油果-1895","10.64898\u002F2026.09.03.745400",{"doi":184,"openalex_id":186,"authors":187,"venue":117,"cited_by_count":99,"oa_url":209,"card":210,"direction":104,"ingested_from":106},"W7211887459",[188,191,194,197,200,203,206],{"name":189,"orcid":190},"Alberto Férez-Gómez","https:\u002F\u002Forcid.org\u002F0000-0002-8545-9237",{"name":192,"orcid":193},"Lucia Soler-Escamez","https:\u002F\u002Forcid.org\u002F0009-0004-1361-6928",{"name":195,"orcid":196},"Cristina Ferrer-Blanco","https:\u002F\u002Forcid.org\u002F0000-0002-3456-917X",{"name":198,"orcid":199},"Adrián Pérez Aguilar","https:\u002F\u002Forcid.org\u002F0000-0001-7012-1551",{"name":201,"orcid":202},"J.I. Hormaza","https:\u002F\u002Forcid.org\u002F0000-0001-5449-7444",{"name":204,"orcid":205},"Almudena Díaz Zayas","https:\u002F\u002Forcid.org\u002F0000-0002-1226-6135",{"name":207,"orcid":208},"Juan M. Losada","https:\u002F\u002Forcid.org\u002F0000-0002-7966-5018","https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2026\u002F09\u002F07\u002F2026.09.03.745400.full.pdf",{"tldr":211,"method":212,"finding":213,"direction":214,"opportunity":215},"通过传感器、遥感和机器学习连续监测两年不同降雨下鳄梨的亏缺灌溉响应。","集成土壤-植物-大气连续体监测、无人机多光谱、冠层分析及机器学习。","严重亏缺灌溉导致深层土壤水分耗尽、生理活动减弱，果实重量下降，冠层表面粗糙度增加。","智慧农业 \u002F 农业物联网","可研究不同降雨年份下亏缺灌溉对果实品质的影响，或开发基于冠层粗糙度的干旱胁迫预警模型。","2026-09-08T23:30:14.886372Z",{"id":218,"title":219,"url":220,"summary":221,"summary_zh":222,"content":9,"source_name":223,"source_url":220,"published_at":224,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":225,"score_detail":226,"sources":230,"tags":232,"primary_topic":235,"secondary_topics":236,"classification":238,"search_phrases":255,"slug":258,"view_count":99,"doi":259,"paper":260,"created_at":270},5570,"Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545079","Counterfactual explanations are increasingly proposed as the interface through which cooperative storage managers interrogate computer-vision yield-forecast models, yet their reliability under the satellite cloud-mask gaps that routinely afflict agricultural remote sensing has not been bounded. The bound is derived from a Lipschitz continuity assumption on the forecast model and a masking-operator perturbation analysis, and it is expressed as a function of the cloud-mask gap fraction, the model's local Lipschitz constant, and the counterfactual search tolerance. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The reliability bound degrades non-linearly as the gap fraction rises, and that the intraclass correlation of 0.64 implies that cooperative-level aggregation of counterfactual recourse is necessary to preserve explanation stability. The contribution is a decision-relevant reliability bound that storage cooperatives can use to set a maximum tolerable cloud-mask gap before counterfactual recourse is suspended.","反事实解释日益被提出作为合作式仓储管理者质询计算机视觉产量预测模型的接口，然而在农业遥感中经常出现的卫星云掩膜缺口条件下，其可靠性尚未得到界定。该界基于预测模型上的Lipschitz连续性假设和掩膜算子扰动分析推导得出，并表示为云掩膜缺口比例、模型局部Lipschitz常数和反事实搜索容差的函数。对前后对照影响估计量进行了基于重复的评估，以表征该设计在给定方差结构（组间标准差=6.0，残差标准差=4.5，组内相关系数=0.64）下的运行特征，并在2000个设计单元中估计了检验效能。可靠性界随缺口比例上升呈非线性退化，且组内相关系数0.64意味着需要对反事实救济进行合作社层面的聚合，以保持解释的稳定性。本文的贡献在于提供了一个与决策相关的可靠性界，仓储合作社可据此设定在暂停反事实救济之前可容忍的最大云掩膜缺口。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",67,{"impact":70,"substance":227,"depth":228,"authority":121,"freshness":13,"relevant":21,"comment":229},20,17,"该论文提出反事实解释在卫星云掩膜缺口下的可靠性边界，方法新颖且结论可用于合作社决策，但属细分方法学研究，产业影响有限。",[231],{"name":223,"url":220},[127,26,233,28,234],"产量预测","可解释性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[237],{"code":45,"slug":46,"name":47,"path":48,"parent_code":36},{"status":50,"locked":14,"version":51,"method":52,"evidence":239},{"candidates":240,"agricultural_evidence":56},[241,247,251],{"code":32,"score":121,"supported":56,"title":242,"abstract":244,"tags":246},[243],"computer vision",[245],"计算机视觉",[26],{"code":45,"score":70,"supported":56,"title":248,"abstract":249,"tags":250},[],[28,73],[233,28],{"code":176,"score":21,"supported":14,"title":252,"abstract":253,"tags":254},[],[],[127],[256,257],"农业人工智能 产量预测 可解释性 智慧农业","农业人工智能 产量预测","农业人工智能产量预测可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":259,"openalex_id":261,"authors":262,"venue":223,"cited_by_count":99,"oa_url":220,"card":265,"direction":104,"ingested_from":106},"W7223114002",[263],{"name":264,"orcid":9},"Kadiatou Barry",{"tldr":266,"method":267,"finding":268,"direction":104,"opportunity":269},"推导卫星云掩膜缺口下反事实解释可靠性的理论界，并评估其对合作社储粮决策的影响。","基于Lipschitz连续性假设与掩膜扰动分析推导可靠性界，并用2000个设计单","缺口比例上升使可靠性非线性下降；ICC=0.64表明需合作社层级聚合以保持解释稳定。","可研究不同作物区云掩膜统计特性下的自适应解释暂停阈值与聚合策略。","2026-10-22T11:20:39.240791Z",{"id":272,"title":273,"url":274,"summary":275,"summary_zh":276,"content":9,"source_name":277,"source_url":274,"published_at":224,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":278,"score_detail":279,"sources":281,"tags":283,"primary_topic":285,"secondary_topics":286,"classification":293,"search_phrases":348,"slug":351,"view_count":99,"doi":352,"paper":353,"created_at":376},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",79,{"impact":19,"substance":18,"depth":19,"authority":121,"freshness":20,"relevant":21,"comment":280},"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。",[282],{"name":277,"url":274},[127,26,28,284,128],"作物水分胁迫",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[287,288],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":289,"slug":290,"name":291,"path":292,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":50,"locked":14,"version":51,"method":52,"evidence":294},{"candidates":295,"agricultural_evidence":56},[296,300,305,312,318,322,329,336,343],{"code":39,"score":17,"supported":56,"title":297,"abstract":298,"tags":299},[40],[65,40],[128],{"code":32,"score":13,"supported":56,"title":301,"abstract":302,"tags":304},[],[303,143,145],"机器学习",[26],{"code":289,"score":20,"supported":56,"title":306,"abstract":307,"tags":311},[],[308,309,310],"物联网","internet of things","IoT",[],{"code":176,"score":155,"supported":56,"title":313,"abstract":314,"tags":317},[],[315,316],"数字孪生","digital twin",[127],{"code":45,"score":155,"supported":56,"title":319,"abstract":320,"tags":321},[],[28,73],[28],{"code":323,"score":109,"supported":56,"title":324,"abstract":325,"tags":328},"A2",[],[326,327],"小麦","wheat",[],{"code":330,"score":109,"supported":56,"title":331,"abstract":332,"tags":335},"A3",[],[333,334],"玉米","maize",[],{"code":337,"score":109,"supported":56,"title":338,"abstract":339,"tags":342},"A5",[],[340,341],"马铃薯","potato",[],{"code":154,"score":122,"supported":56,"title":344,"abstract":345,"tags":347},[],[346],"葡萄",[],[349,350],"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":352,"openalex_id":354,"authors":355,"venue":277,"cited_by_count":99,"oa_url":274,"card":371,"direction":214,"ingested_from":106},"W7223088921",[356,359,362,365,368],{"name":357,"orcid":358},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":360,"orcid":361},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":363,"orcid":364},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":366,"orcid":367},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":369,"orcid":370},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":372,"method":373,"finding":374,"direction":104,"opportunity":375},"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。","2026-10-22T11:20:24.526683Z",{"id":378,"title":379,"url":380,"summary":381,"summary_zh":382,"content":9,"source_name":223,"source_url":380,"published_at":224,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":383,"score_detail":384,"sources":387,"tags":389,"primary_topic":393,"secondary_topics":394,"classification":397,"search_phrases":424,"slug":427,"view_count":99,"doi":428,"paper":429,"created_at":439},5552,"INTEGRATING IoT, REMOTE SENSING, AND ARTIFICIAL INTELLIGENCE FOR SMART IRRIGATION: A REVIEW OF ARCHITECTURES, METHODS, AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531020","This research paper reviews the integration of Internet of Things (IoT), remote sensing, and Artificial Intelligence (AI) in smart irrigation systems. It discusses system architectures, intelligent irrigation techniques, automated water management, key challenges, and future research opportunities to improve water-use efficiency and promote sustainable agriculture.","本文综述了物联网（Internet of Things, IoT）、遥感与人工智能（Artificial Intelligence, AI）在智能灌溉系统中的融合应用，探讨了系统架构、智能灌溉技术、自动化水资源管理、关键挑战及未来研究方向，以提升用水效率并促进可持续农业发展。",75,{"impact":19,"substance":227,"depth":385,"authority":121,"freshness":20,"relevant":21,"comment":386},16,"系统综述物联网、遥感与AI融合的智能灌溉架构与方法，属智慧农业细分领域有实质增量的国际综述，值得入选。",[388],{"name":223,"url":380},[26,390,28,391,392],"农业物联网","智慧灌溉","节水农业",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[395,396],{"code":289,"slug":290,"name":291,"path":292,"parent_code":36},{"code":45,"slug":46,"name":47,"path":48,"parent_code":36},{"status":50,"locked":14,"version":51,"method":52,"evidence":398},{"candidates":399,"agricultural_evidence":56},[400,405,409,413,417],{"code":39,"score":401,"supported":56,"title":402,"abstract":403,"tags":404},19,[40],[65,66,40],[391,392],{"code":289,"score":19,"supported":56,"title":406,"abstract":407,"tags":408},[310],[308,309,310],[390],{"code":45,"score":17,"supported":56,"title":410,"abstract":411,"tags":412},[73],[28,73],[28],{"code":32,"score":121,"supported":56,"title":414,"abstract":415,"tags":416},[145],[145],[26],{"code":418,"score":109,"supported":56,"title":419,"abstract":420,"tags":423},"H8",[],[421,422],"可持续农业","sustainable agriculture",[],[425,426],"IoT 遥感 AI 智能灌溉","农业人工智能 农业物联网 智慧灌溉 节水农业","IoT遥感AI智能灌溉-5552","10.5281\u002Fzenodo.23531020",{"doi":428,"openalex_id":430,"authors":431,"venue":223,"cited_by_count":99,"oa_url":380,"card":434,"direction":214,"ingested_from":106},"W7223090389",[432],{"name":433,"orcid":9},"Patil",{"tldr":435,"method":436,"finding":437,"direction":214,"opportunity":438},"综述物联网、遥感与人工智能融合的智能灌溉系统架构、方法与挑战。","文献综述，梳理IoT、遥感与AI集成的系统架构与智能灌溉技术。","融合三者可提升用水效率并推动可持续农业，但仍面临关键挑战。","可针对多源数据融合的实时灌溉决策模型与边缘部署开展实证研究。","2026-10-22T11:20:18.028252Z",{"id":441,"title":442,"url":443,"summary":444,"summary_zh":445,"content":9,"source_name":446,"source_url":443,"published_at":447,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":448,"score_detail":449,"sources":453,"tags":455,"primary_topic":458,"secondary_topics":459,"classification":464,"search_phrases":488,"slug":491,"view_count":99,"doi":492,"paper":493,"created_at":513},5006,"Mapping Soil Salinity in Arid Agricultural Regions Using Multi-Source Remote Sensing and a Long-Range Dependency-Enhanced Deep Learning Framework","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101966","Soil salinity estimation across arid agricultural regions is challenging due to pronounced environmental heterogeneity and complex dependencies among multi-source variables. This study proposes a Long-Range Dependency-Enhanced Network (LRD-Net) for regional-scale soil salinity estimation in multiple agricultural oasis regions using multi-source remote sensing and environmental covariates. A dataset comprising 180 field samples from three agricultural oasis regions in Xinjiang, China, was constructed with 25 covariates derived from spectral, topographic, climatic, and soil property data. LRD-Net consists of three stages: a multi-scale feature fusion backbone based on depthwise separable convolutions and cross-scale interaction; a Long-Range Dependency Enhancement Module (LDEM) with an asymmetric dual-branch architecture that captures forward and reverse-aware dependencies through reverse alignment and cross-directional fusion; and multi-scale attention refinement with a regression head for continuous salinity estimation. Under the region-stratified random-split evaluation, LRD-Net achieved an R2 of 0.905, an RMSE of 1.207 g·kg−1, and an MAE of 0.868 g·kg−1. Performance decreased under more spatially independent validation, with an R2 of 0.874 under spatial cross-validation and LORO test R2 values ranging from 0.739 to 0.801, indicating that prediction across spatially and geographically independent samples is more challenging than region-stratified random-split evaluation. The proposed framework effectively integrates multi-source information and captures complementary long-range dependencies, providing a potential approach for regional-scale soil salinity estimation within the studied arid agricultural oasis environments and supporting soil salinization monitoring and sustainable land management in the study region.","干旱农业区域土壤盐分估算面临显著的环境异质性和多源变量间复杂依赖关系的挑战。本研究提出了一种长程依赖增强网络（LRD-Net），利用多源遥感与环境协变量对多个农业绿洲区域进行区域尺度土壤盐分估算。研究构建了包含中国新疆三个农业绿洲区域180个野外样本的数据集，并衍生出25个协变量，涵盖光谱、地形、气候和土壤属性数据。LRD-Net由三个阶段组成：基于深度可分离卷积和跨尺度交互的多尺度特征融合骨干网络；具有非对称双分支架构的长程依赖增强模块（LDEM），通过反向对齐和跨方向融合捕获前向与反向感知依赖关系；以及多尺度注意力细化与回归头，用于连续盐分估算。在区域分层随机划分评估下，LRD-Net取得了R²为0.905、RMSE为1.207 g·kg⁻¹、MAE为0.868 g·kg⁻¹的性能。在空间独立性更强的验证下性能有所下降，空间交叉验证R²为0.874，留一区域测试R²范围为0.739至0.801，表明跨空间和地理独立样本的预测比区域分层随机划分评估更具挑战性。所提出的框架有效整合了多源信息并捕获了互补的长程依赖关系，为研究区干旱农业绿洲环境下的区域尺度土壤盐分估算提供了潜在方法，并支持研究区土壤盐渍化监测与可持续土地管理。","Land","2026-10-14T00:00:00Z",72,{"impact":121,"substance":450,"depth":228,"authority":451,"freshness":20,"relevant":21,"comment":452},21,13,"方法新颖、数据扎实的遥感土壤盐渍化估算研究，对干旱区盐碱地监测与治理有参考价值，但属细分领域学术进展，公共影响有限。",[454],{"name":446,"url":443},[127,26,28,456,457],"土壤盐渍化","新疆农业",{"code":45,"slug":46,"name":47,"path":48,"parent_code":36},[460,463],{"code":162,"slug":166,"name":461,"path":462,"parent_code":43},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":50,"locked":14,"version":51,"method":52,"evidence":465},{"candidates":466,"agricultural_evidence":56},[467,471,476,480,484],{"code":45,"score":17,"supported":56,"title":468,"abstract":469,"tags":470},[73],[28,73],[28],{"code":162,"score":472,"supported":56,"title":473,"abstract":474,"tags":475},14,[166],[165,166],[],{"code":32,"score":20,"supported":56,"title":477,"abstract":478,"tags":479},[58],[],[26],{"code":169,"score":122,"supported":56,"title":481,"abstract":482,"tags":483},[],[172],[],{"code":176,"score":21,"supported":14,"title":485,"abstract":486,"tags":487},[],[],[127],[489,490],"新疆 农业绿洲 土壤盐渍化 遥感","LRD-Net 土壤盐分 估算","新疆农业绿洲土壤盐渍化遥感-5006","10.3390\u002Fland15101966",{"doi":492,"openalex_id":494,"authors":495,"venue":446,"cited_by_count":99,"oa_url":443,"card":508,"direction":104,"ingested_from":106},"W7221816183",[496,498,501,503,505],{"name":497,"orcid":9},"Mingjie Qian",{"name":499,"orcid":500},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":502,"orcid":9},"Peng Wang",{"name":504,"orcid":9},"Hangyuan Liu",{"name":506,"orcid":507},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":509,"method":510,"finding":511,"direction":104,"opportunity":512},"提出LRD-Net深度学习框架，融合多源遥感与环境协变量，估算新疆干旱绿洲区土壤盐分。","构建180个样点、25个协变量的数据集，设计含长程依赖增强模块的多尺度网络。","区域分层随机划分下R²达0.905，但空间独立验证R²降至0.739-0.874，跨区域预测更难。","可探索跨区域迁移学习与不确定性量化，提升空间独立场景下盐分制图泛化能力。","2026-10-15T23:30:29.667342Z",{"id":515,"title":516,"url":517,"summary":518,"summary_zh":519,"content":9,"source_name":520,"source_url":517,"published_at":447,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":521,"score_detail":522,"sources":524,"tags":526,"primary_topic":528,"secondary_topics":529,"classification":535,"search_phrases":579,"slug":582,"view_count":21,"doi":583,"paper":584,"created_at":606},4988,"Hierarchical cross-modal spatiotemporal attention network for crop yield prediction in complex Himalayan agroecosystems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1933033","Introduction Accurate crop yield prediction is essential for precision agriculture, climate-resilient farm management, and timely decision-making. However, yield prediction remains challenging because of complex, nonlinear, and multi-scale interactions among crop physiology, soil conditions, weather variability, and management practices. Conventional statistical, machine learning, and deep learning approaches, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, often have limitations in capturing long-range temporal dependencies, hierarchical spatial relationships, heterogeneous data modalities, and predictive uncertainty. Methods This study proposes a Hierarchical Cross-Modal Spatio-Temporal Attention Network (HCM-STAN) for crop yield prediction using a unified attention-based framework for integrating heterogeneous agro-environmental data. The architecture incorporates four principal components: (i) hierarchical spatio-temporal attention operating across tree, block, orchard, and regional scales; (ii) a cross-modal attention module for adaptive fusion of IoT-based soil sensor observations, weather data, plant physiological measurements, and drone-based multispectral imagery; (iii) a soil-aware attention head that explicitly models soil memory, hysteresis effects, and root-zone dynamics; and (iv) an uncertainty-aware prediction module combining Monte Carlo dropout and quantile regression to generate calibrated prediction intervals. The framework was evaluated using a multi-source dataset collected from Himalayan apple orchards during 2023–2026, comprising 1,247 monitored trees across three commercial orchards. Results The proposed HCM-STAN achieved an R 2 of 0.89 for four-week-ahead crop yield prediction, representing a 15.6% improvement over the CNN-LSTM baseline. Furthermore, zero-shot transfer to mango orchards achieved an R 2 of 0.67, corresponding to approximately 75% of the performance obtained under full training. Discussion The results demonstrate that integrating hierarchical spatio-temporal representation learning, cross-modal attention, soil-aware modelling, and uncertainty estimation can effectively address the heterogeneous and dynamic nature of crop yield prediction in complex mountain agroecosystems. The observed zero-shot transfer performance further indicates the potential of HCM-STAN to generalize across crop systems without crop-specific retraining. Overall, the proposed framework provides a promising foundation for reliable, uncertainty-aware yield forecasting and can support precision agriculture, climate-smart farm management, and data-driven decision-making in heterogeneous agroecosystems.","引言 准确的作物产量预测对于精准农业、气候适应性农场管理以及及时决策至关重要。然而，由于作物生理、土壤条件、天气变异和管理措施之间复杂、非线性且多尺度的相互作用，产量预测仍然具有挑战性。传统的统计方法、机器学习以及深度学习方法，包括卷积神经网络（CNN）、循环神经网络（RNN）和长短期记忆（LSTM）网络，在捕捉长程时间依赖关系、层次化空间关系、异构数据模态以及预测不确定性方面往往存在局限。方法 本研究提出了一种用于作物产量预测的层次化跨模态时空注意力网络（HCM-STAN），采用统一的基于注意力的框架来整合异构农业环境数据。该架构包含四个主要组成部分：（i）在树、地块、果园和区域尺度上运行的层次化时空注意力；（ii）用于自适应融合基于物联网的土壤传感器观测、天气数据、植物生理测量和无人机多光谱影像的跨模态注意力模块；（iii）显式建模土壤记忆、滞后效应和根区动态的土壤感知注意力头；（iv）结合蒙特卡洛 dropout 和分位数回归以生成校准预测区间的不确定性感知预测模块。该框架使用2023—2026年在喜马拉雅苹果园采集的多源数据集进行了评估，该数据集包含三个商业果园中1，247棵监测树木。结果 所提出的 HCM-STAN 在提前四周作物产量预测中达到了0.89的 R²，相较于 CNN-LSTM 基线提升了15.6%。此外，零样本迁移至芒果园达到了0.67的 R²，相当于完整训练下所获性能的约75%。讨论 结果表明，整合层次化时空表征学习、跨模态注意力、土壤感知建模和不确定性估计，能够有效应对复杂山地农业生态系统中作物产量预测的异构性和动态性。观察到的零样本迁移性能进一步表明，HCM-STAN 具有在无需作物特定再训练的情况下跨作物系统泛化的潜力。总体而言，该 p","Frontiers in Plant Science",81,{"impact":19,"substance":18,"depth":19,"authority":472,"freshness":20,"relevant":21,"comment":523},"提出分层跨模态时空注意力网络，融合物联网土壤传感、气象、生理与无人机多光谱数据，苹果园四周产量预测R²达0.89并实现芒果园零样本迁移，方法新颖、数据扎实，对智慧农业产量预报有参考价值。",[525],{"name":520,"url":517},[127,26,233,527,28],"多模态融合",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[530,534],{"code":154,"slug":531,"name":158,"path":532,"parent_code":533},"fruit","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",{"code":45,"slug":46,"name":47,"path":48,"parent_code":36},{"status":50,"locked":14,"version":51,"method":52,"evidence":536},{"candidates":537,"agricultural_evidence":56},[538,543,550,554,560,564,568,574],{"code":32,"score":539,"supported":56,"title":540,"abstract":541,"tags":542},11,[],[60,303,143,58],[26,527],{"code":154,"score":20,"supported":56,"title":544,"abstract":545,"tags":549},[],[546,547,159,548],"苹果","芒果","apple",[],{"code":45,"score":70,"supported":56,"title":551,"abstract":552,"tags":553},[],[150,151],[233,28],{"code":176,"score":155,"supported":56,"title":555,"abstract":556,"tags":559},[],[557,558],"精准农业","precision agriculture",[127],{"code":162,"score":109,"supported":56,"title":561,"abstract":562,"tags":563},[],[165,166],[],{"code":289,"score":109,"supported":56,"title":565,"abstract":566,"tags":567},[],[308,310],[],{"code":569,"score":122,"supported":56,"title":570,"abstract":571,"tags":573},"H6",[],[572],"农业生态",[],{"code":169,"score":122,"supported":56,"title":575,"abstract":576,"tags":578},[],[577],"气候适应",[],[580,581],"喜马拉雅 苹果园 产量预测","HCM-STAN 作物产量预测","喜马拉雅苹果园产量预测-4988","10.3389\u002Ffpls.2026.1933033",{"doi":583,"openalex_id":585,"authors":586,"venue":520,"cited_by_count":99,"oa_url":517,"card":600,"direction":214,"ingested_from":106},"W7221795850",[587,590,592,594,596,598],{"name":588,"orcid":589},"Shabir Ahmad Mir","https:\u002F\u002Forcid.org\u002F0000-0003-2311-218X",{"name":591,"orcid":9},"Umarfarooque Momin",{"name":593,"orcid":9},"Sudeep Kumar E",{"name":595,"orcid":9},"Itfaq Ahmad Mir",{"name":597,"orcid":9},"Ab Rouf Khan",{"name":599,"orcid":9},"Ghulam Mohammad Mir",{"tldr":601,"method":602,"finding":603,"direction":604,"opportunity":605},"提出分层跨模态时空注意力网络HCM-STAN，融合多源农业数据预测喜马拉雅山区苹果产量。","用IoT土壤、气象、生理和无人机多光谱数据，结合分层时空与跨模态注意力及不确定性","四周前产量预测R²达0.89，比CNN-LSTM提升15.6%，零样本迁移芒果R²为0.67。","农业人工智能与决策模型","可探索跨作物零样本迁移的领域自适应机制，并验证模型在更多山地农业生态系统的泛化能力。","2026-10-15T23:30:08.750140Z"]