[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4686":3,"related-4686":114},{"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":38,"search_phrases":67,"slug":70,"view_count":71,"doi":72,"paper":73,"created_at":113},4686,"SAMWater: a prompt-guided multimodal framework for transferable water mapping with the segment anything model","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15481603.2026.2745243","Accurate and transferable surface water mapping is critical for water resource assessment, flood monitoring, and aquatic ecosystem management, but remains difficult because SAR speckle, optical contamination, heterogeneous backgrounds, and scarce high-quality pixel-level annotations limit robust large-scale mapping. We propose SAMWater, a prompt-guided multimodal framework that integrates Sentinel-1 SAR, Sentinel-2 optical imagery, the Segment Anything Model (SAM), and Segment Anything Model 3 (SAM 3). Instead of using SAR and optical data as independent classifiers, SAMWater transforms their complementary information into structured prompts for coarse-to-refined water segmentation. A physically interpretable multisensor coarse water prior is first generated from Sentinel observations using an automatic threshold-based strategy. This prior is then refined through prompt-based boundary refinement, a multi-prompt excavation strategy that derives point, box, and Gaussian-style mask prompts, and Water-IoU fine-tuning for water-specific candidate ranking. Using globally distributed samples and manually interpreted PlanetScope reference labels, SAMWater achieved average Kappa coefficients of 90.71%, 85.63%, and 89.05% in evaluations stratified by water body type, land cover background, and climatic zone, respectively. It generally outperformed traditional thresholding and clustering methods, public deep learning models, and existing global water or land cover products, particularly in structurally complex river, reservoir, urban, agricultural, and mountainous scenes. Ablation experiments confirmed that multimodal prior fusion, structured prompt construction, and water-specific ranking jointly drive the performance gains. Moreover, SAMWater-generated refined labels improved the training of U-Net, DeepLabV3+, and SegFormer, indicating their utility for weakly supervised segmentation. SAMWater therefore demonstrates that integrating physically interpretable remote sensing priors with promptable foundation models provides a practical pathway for transferable surface water mapping, large-scale multisensor weak-label generation, and more consistent monitoring of surface water extent.","准确且具有可迁移性的地表水制图对水资源评估、洪水监测和水生生态系统管理至关重要，但由于合成孔径雷达（SAR）斑点噪声、光学影像污染、异质背景以及高质量像素级标注稀缺等因素，实现稳健的大尺度制图仍然困难。我们提出SAMWater，一种提示引导的多模态框架，集成了Sentinel-1 SAR、Sentinel-2光学影像、分割一切模型（SAM）和分割一切模型3（SAM 3）。SAMWater并非将SAR和光学数据用作独立分类器，而是将其互补信息转化为结构化提示，用于由粗到精的水体分割。首先，利用基于自动阈值的策略从Sentinel观测中生成具有物理可解释性的多传感器粗水体先验。随后，通过基于提示的边界细化、多提示挖掘策略（导出点、框和高斯式掩膜提示）以及面向水体的Water-IoU微调进行候选排序，对该先验进行精化。利用全球分布的样本和人工解译的PlanetScope参考标签，SAMWater在按水体类型、土地覆盖背景和气候带分层评估中分别取得了90.71%、85.63%和89.05%的平均Kappa系数。其总体优于传统阈值法和聚类方法、公开深度学习模型以及现有全球水体或土地覆盖产品，尤其是在结构复杂的河流、水库、城市、农业和山地场景中。消融实验证实，多模态先验融合、结构化提示构建和面向水体的排序共同驱动了性能提升。此外，SAMWater生成的精化标签改善了U-Net、DeepLabV3+和SegFormer的训练，表明其可用于弱监督分割。因此，SAMWater表明，将具有物理可解释性的遥感先验与可提示基础模型相结合，为可迁移地表水制图、大尺度多传感器弱标签生成以及更一致的地表水范围监测提供了一条实用路径。",null,"GIScience & Remote Sensing","2026-10-09T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"将SAM基础模型与多源遥感先验结合实现可迁移水体制图，方法新颖、验证充分，对农业水资源与洪涝监测有实用价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业人工智能","遥感","洪涝监测","多模态大模型","水资源监测",{"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",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,52,59],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":51},8,true,[],[49,50],"深度学习","deep learning",[26,29],{"code":53,"score":54,"supported":46,"title":55,"abstract":56,"tags":58},"I3",7,[],[27,57],"remote sensing",[27],{"code":60,"score":61,"supported":46,"title":62,"abstract":63,"tags":66},"H3",6,[],[64,65],"水资源","water resource",[],[68,69],"SAMWater 水体提取","Sentinel-1 Sentinel-2 水面制图","SAMWater水体提取-4686",2,"10.1080\u002F15481603.2026.2745243",{"doi":72,"openalex_id":74,"authors":75,"venue":10,"cited_by_count":104,"oa_url":105,"card":106,"direction":110,"ingested_from":112},"W7221158478",[76,79,82,85,88,90,92,94,97,99,101],{"name":77,"orcid":78},"Yuchen Liu","https:\u002F\u002Forcid.org\u002F0000-0002-6517-0022",{"name":80,"orcid":81},"Kang Zheng","https:\u002F\u002Forcid.org\u002F0000-0002-4811-0001",{"name":83,"orcid":84},"Yongze Song","https:\u002F\u002Forcid.org\u002F0000-0003-3420-9622",{"name":86,"orcid":87},"Lai Lai","https:\u002F\u002Forcid.org\u002F0000-0002-0744-6756",{"name":89,"orcid":9},"Ming Shen",{"name":91,"orcid":9},"Yanzhu Dong",{"name":93,"orcid":9},"Yuxuan Wang",{"name":95,"orcid":96},"Xiaoxiao Guo","https:\u002F\u002Forcid.org\u002F0000-0002-0068-8074",{"name":98,"orcid":9},"Dong Sheng",{"name":100,"orcid":9},"Zunyi Xie",{"name":102,"orcid":103},"Richard Iestyn Woolway","https:\u002F\u002Forcid.org\u002F0000-0003-0498-7968",0,"https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F15481603.2026.2745243?needAccess=true",{"tldr":107,"method":108,"finding":109,"direction":110,"opportunity":111},"提出SAMWater，用提示引导多模态基础模型实现可迁移地表水制图。","融合Sentinel-1\u002F2与SAM\u002FSAM 3，构建多提示与Water-IoU","在多地类气候区Kappa达85%以上，优于传统与深度学习及全球产品。","农业遥感与作物表型","可探索将物理先验提示框架迁移至农田灌溉水体与洪涝监测的弱标签生成。","openalex","2026-10-11T23:30:31.848575Z",{"total":61,"page":21,"page_size":61,"items":115},[116,182,237,352,413,475],{"id":117,"title":118,"url":119,"summary":120,"summary_zh":121,"content":9,"source_name":122,"source_url":119,"published_at":123,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":124,"score_detail":125,"sources":128,"tags":130,"primary_topic":134,"secondary_topics":135,"classification":136,"search_phrases":165,"slug":168,"view_count":104,"doi":169,"paper":170,"created_at":181},5588,"Certified Temporal Fusion of Artificial Intelligence Climate Analytics for Crop Failure Early Warning Under Sparse Agricultural Sensors: Error Bounds and Famine Relief Targeting","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545489","Sparse agricultural sensor networks across sub-Saharan Africa leave crop-failure early-warning systems with too little ground observation to support reliable famine relief targeting. The framework treats sensor sparsity as a missing-data problem with a known observation operator and propagates climate-analytics uncertainty through a temporal Kalman-Buys filter whose gain is constrained by a certified risk envelope. Using NASA POWER monthly reanalysis for Accra, Ghana (2000-2023, 312 observations), the analysis establishes a mean temperature of 26.521 C and a warming trend of 0.1622 C per decade, which calibrates the climate-analytics prior. The derived bounds show that relief-targeting error scales with the square root of sensor density and with the Lipschitz constant of the crop-response function, yielding an explicit sensor-placement rule for a target misclassification rate. The contribution is a certified, assumption-explicit bridge between climate analytics and famine relief targeting that states the conditions under which early warning is trustworthy.","撒哈拉以南非洲稀疏的农业传感器网络使得作物歉收预警系统缺乏足够的地面观测数据，难以支撑可靠的饥荒救援目标定位。该框架将传感器稀疏性视为具有已知观测算子的缺失数据问题，并通过时间Kalman-Buys滤波器传播气候分析不确定性，该滤波器的增益受经认证的风险包络约束。利用NASA POWER对加纳阿克拉的月度再分析数据（2000—2023年，312个观测值），分析确定了平均气温为26.521°C、每十年0.1622°C的增暖趋势，据此校准了气候分析先验。推导出的界限表明，救援目标定位误差随传感器密度的平方根以及作物响应函数的Lipschitz常数缩放，从而为目标误分类率给出了明确的传感器布设规则。该研究的贡献在于构建了一个经认证的、假设明确的气候分析与饥荒救援目标定位之间的桥梁，阐明了早期预警可信赖的条件。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",74,{"impact":126,"substance":18,"depth":17,"authority":126,"freshness":13,"relevant":21,"comment":127},12,"该研究提出一种带误差界认证的时序融合方法，用于稀疏传感器条件下的作物歉收预警与饥荒救援瞄准，方法新颖、结论明确，但案例局限于加纳阿克拉，产业影响有限，适合作为技术前沿条目收录。",[129],{"name":122,"url":119},[131,26,132,27,133],"智慧农业","粮食安全","气候分析",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":137},{"candidates":138,"agricultural_evidence":46},[139,144,151,156,161],{"code":32,"score":20,"supported":46,"title":140,"abstract":142,"tags":143},[141],"artificial intelligence",[],[26],{"code":145,"score":146,"supported":46,"title":147,"abstract":148,"tags":150},"I5",3,[],[149],"农业传感",[],{"code":152,"score":21,"supported":14,"title":153,"abstract":154,"tags":155},"A8",[],[],[132],{"code":157,"score":21,"supported":14,"title":158,"abstract":159,"tags":160},"I1",[],[],[131],{"code":53,"score":21,"supported":14,"title":162,"abstract":163,"tags":164},[],[],[27],[166,167],"NASA POWER 加纳 气候分析","作物歉收 早期预警 传感器","NASAPOWER加纳气候分析-5588","10.5281\u002Fzenodo.23545489",{"doi":169,"openalex_id":171,"authors":172,"venue":122,"cited_by_count":104,"oa_url":119,"card":175,"direction":180,"ingested_from":112},"W7223111766",[173],{"name":174,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":176,"method":177,"finding":178,"direction":110,"opportunity":179},"提出带误差界的时间融合框架，在稀疏传感器下用气候分析预警作物歉收。","卡尔曼-布西滤波+NASA POWER再分析数据，风险包络约束增益。","救济瞄准误差随传感器密度平方根和作物响应Lipschitz常数缩放，给出布点规则。","可将该认证误差界框架扩展到多作物多区域，结合卫星遥感弥补地面传感器稀疏。","农业人工智能与决策模型","2026-10-22T11:21:02.682497Z",{"id":183,"title":184,"url":185,"summary":186,"summary_zh":187,"content":9,"source_name":122,"source_url":185,"published_at":123,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":188,"score_detail":189,"sources":193,"tags":195,"primary_topic":198,"secondary_topics":199,"classification":204,"search_phrases":221,"slug":224,"view_count":104,"doi":225,"paper":226,"created_at":236},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。",67,{"impact":45,"substance":190,"depth":191,"authority":126,"freshness":13,"relevant":21,"comment":192},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。",[194],{"name":122,"url":185},[131,26,196,27,197],"产量预测","可解释性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[200],{"code":53,"slug":201,"name":202,"path":203,"parent_code":36},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":39,"locked":14,"version":40,"method":41,"evidence":205},{"candidates":206,"agricultural_evidence":46},[207,213,217],{"code":32,"score":126,"supported":46,"title":208,"abstract":210,"tags":212},[209],"computer vision",[211],"计算机视觉",[26],{"code":53,"score":45,"supported":46,"title":214,"abstract":215,"tags":216},[],[27,57],[196,27],{"code":157,"score":21,"supported":14,"title":218,"abstract":219,"tags":220},[],[],[131],[222,223],"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":225,"openalex_id":227,"authors":228,"venue":122,"cited_by_count":104,"oa_url":185,"card":231,"direction":110,"ingested_from":112},"W7223114002",[229],{"name":230,"orcid":9},"Kadiatou Barry",{"tldr":232,"method":233,"finding":234,"direction":110,"opportunity":235},"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":238,"title":239,"url":240,"summary":241,"summary_zh":242,"content":9,"source_name":243,"source_url":240,"published_at":123,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":244,"score_detail":245,"sources":247,"tags":249,"primary_topic":252,"secondary_topics":257,"classification":263,"search_phrases":322,"slug":325,"view_count":104,"doi":326,"paper":327,"created_at":351},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":17,"substance":18,"depth":17,"authority":126,"freshness":20,"relevant":21,"comment":246},"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。",[248],{"name":243,"url":240},[131,26,27,250,251],"作物水分胁迫","精准灌溉",{"code":60,"slug":253,"name":254,"path":255,"parent_code":256},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[258,259],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":145,"slug":260,"name":261,"path":262,"parent_code":36},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":39,"locked":14,"version":40,"method":41,"evidence":264},{"candidates":265,"agricultural_evidence":46},[266,272,278,285,291,295,302,309,316],{"code":60,"score":267,"supported":46,"title":268,"abstract":269,"tags":271},15,[253],[270,253],"灌溉",[251],{"code":32,"score":13,"supported":46,"title":273,"abstract":274,"tags":277},[],[275,276,141],"机器学习","machine learning",[26],{"code":145,"score":20,"supported":46,"title":279,"abstract":280,"tags":284},[],[281,282,283],"物联网","internet of things","IoT",[],{"code":157,"score":54,"supported":46,"title":286,"abstract":287,"tags":290},[],[288,289],"数字孪生","digital twin",[131],{"code":53,"score":54,"supported":46,"title":292,"abstract":293,"tags":294},[],[27,57],[27],{"code":296,"score":61,"supported":46,"title":297,"abstract":298,"tags":301},"A2",[],[299,300],"小麦","wheat",[],{"code":303,"score":61,"supported":46,"title":304,"abstract":305,"tags":308},"A3",[],[306,307],"玉米","maize",[],{"code":310,"score":61,"supported":46,"title":311,"abstract":312,"tags":315},"A5",[],[313,314],"马铃薯","potato",[],{"code":317,"score":146,"supported":46,"title":318,"abstract":319,"tags":321},"B2",[],[320],"葡萄",[],[323,324],"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":326,"openalex_id":328,"authors":329,"venue":243,"cited_by_count":104,"oa_url":240,"card":345,"direction":350,"ingested_from":112},"W7223088921",[330,333,336,339,342],{"name":331,"orcid":332},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":334,"orcid":335},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":337,"orcid":338},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":340,"orcid":341},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":343,"orcid":344},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":346,"method":347,"finding":348,"direction":110,"opportunity":349},"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":353,"title":354,"url":355,"summary":356,"summary_zh":357,"content":9,"source_name":358,"source_url":355,"published_at":359,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":360,"sources":362,"tags":364,"primary_topic":367,"secondary_topics":368,"classification":369,"search_phrases":380,"slug":383,"view_count":104,"doi":384,"paper":385,"created_at":412},5562,"pxGPT: A schema-constrained multimodal LLM framework for plant phenotyping","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbioinformatics\u002Fbtag781","Abstract Summary Deep learning can scale image-based plant phenotyping but typically targets predefined traits, whereas manual phenotypic description is more flexible but difficult to scale. We present Phenotype eXplorer Generative Plant Traits (pxGPT), a Python framework that converts multi-view plant images into structured phenotype records. The workflow combines free-text description, dataset-specific schema generation with human review, and schema-constrained information extraction. We evaluated the extraction stage on three Brassica datasets using 78,973 annotations from ten annotators who had no access to pxGPT predictions. For 38-45 qualitative traits per dataset, pxGPT-human agreement ranged from 0.572 to 0.712, compared with 0.620 to 0.739 agreement among human annotators. All 32,736 required trait fields were present; 93 values were empty, and no qualitative descriptor violations were recorded. For selected morphological structures, pooled false-positive rates were 0.5-8.0%, while sensitivity varied across structure types in one mature-plant dataset (37.5-100%). The curated schemas were applied unchanged with two commercial models and one locally hosted open-weight model, with differences in mean accuracy of 0.03 or less. In the evaluated Brassica datasets, these results support schema-guided extraction of visible plant traits without separate model training for each trait. Human-guided schema development offers a path to extend pxGPT to other crops and imaging conditions, with accuracy requiring validation in each new setting. Availability and Implementation The software is available at https:\u002F\u002Fgithub.com\u002FYaoChengLab\u002Fpxgpt and an archival version of the source code is available on Zenodo (https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22931745). Curated schemas used in this study, annotation guides, phenotype records and human annotations are available at https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22907554. Supplementary information Supplementary data are available at Bioinformatics online.","摘要 深度学习能够扩展基于图像的植物表型分析，但通常针对预定义性状，而人工表型描述更为灵活却难以规模化。我们提出Phenotype eXplorer Generative Plant Traits（pxGPT），一个将多视角植物图像转换为结构化表型记录的Python框架。该工作流程结合了自由文本描述、数据集特定模式生成与人工审核，以及模式约束的信息提取。我们在三个芸薹属（Brassica）数据集上评估了提取阶段，使用了来自十名标注者的78,973条标注，这些标注者无法获取pxGPT的预测结果。对于每个数据集38-45个定性性状，pxGPT与人工标注者之间的一致性范围为0.572至0.712，而人工标注者之间的一致性为0.620至0.739。所有32,736个所需性状字段均已存在；93个值为空，未记录到定性描述符违规。对于选定的形态结构，合并假阳性率为0.5-8.0%，而在一个成熟植株数据集中，灵敏度因结构类型而异（37.5-100%）。经整理的模式在未作修改的情况下应用于两个商业模型和一个本地部署的开放权重模型，平均准确率差异不超过0.03。在所评估的芸薹属数据集中，这些结果支持在无需为每个性状单独训练模型的情况下，对可见植物性状进行模式引导的提取。人工引导的模式开发为将pxGPT扩展至其他作物和成像条件提供了途径，其准确性需在每个新设置中加以验证。可用性与实现 该软件可在https:\u002F\u002Fgithub.com\u002FYaoChengLab\u002Fpxgpt获取，源代码的存档版本可在Zenodo（https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22931745）获取。本研究中使用的经整理模式、标注指南、表型记录和人工标注可在https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22907554获取。补充信息 补充数据可在Bioinformatics在线获取。","Bioinformatics","2026-10-20T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":361},"提出基于模式约束的多模态大模型植物表型提取框架，在近8万条标注上验证，方法新颖、数据规模可观，对智慧育种与表型自动化有参考价值。",[363],{"name":358,"url":355},[131,26,365,29,366],"植物表型","芸薹属",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":370},{"candidates":371,"agricultural_evidence":46},[372,376],{"code":32,"score":45,"supported":46,"title":373,"abstract":374,"tags":375},[],[49,50],[26,29],{"code":157,"score":21,"supported":14,"title":377,"abstract":378,"tags":379},[],[],[131],[381,382],"pxGPT 植物表型","Brassica 表型 多模态大模型","pxGPT植物表型-5562","10.1093\u002Fbioinformatics\u002Fbtag781",{"doi":384,"openalex_id":386,"authors":387,"venue":358,"cited_by_count":104,"oa_url":406,"card":407,"direction":350,"ingested_from":112},"W7223127960",[388,391,393,396,399,402,404],{"name":389,"orcid":390},"Po‐Xing Zheng","https:\u002F\u002Forcid.org\u002F0000-0001-5827-5865",{"name":392,"orcid":9},"Jheng-Yan Ou",{"name":394,"orcid":395},"Yu-Lin Wu","https:\u002F\u002Forcid.org\u002F0009-0003-8596-9047",{"name":397,"orcid":398},"Ruei-Shiuan Wang","https:\u002F\u002Forcid.org\u002F0009-0007-5159-1129",{"name":400,"orcid":401},"WU Ting-li","https:\u002F\u002Forcid.org\u002F0000-0001-7996-5515",{"name":403,"orcid":9},"Te-Chang Hsu",{"name":405,"orcid":9},"Yao-Cheng Lin","https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Fadvance-article-pdf\u002Fdoi\u002F10.1093\u002Fbioinformatics\u002Fbtag781\u002F72201337\u002Fbtag781.pdf",{"tldr":408,"method":409,"finding":410,"direction":110,"opportunity":411},"提出pxGPT框架，用多模态大模型将多视角植物图像转为结构化表型记录。","结合自由文本描述、人工审核的模式生成与模式约束信息抽取，在三个芸薹属数据集上评估","pxGPT与人工标注一致性0.572-0.712，接近人工间一致性，且无需逐性状训练模型。","可探索将模式引导抽取迁移至更多作物与复杂成像条件，并研究自动化模式生成以降低人工审核成本。","2026-10-22T11:20:22.173561Z",{"id":414,"title":415,"url":416,"summary":417,"summary_zh":418,"content":9,"source_name":419,"source_url":416,"published_at":420,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":421,"score_detail":422,"sources":424,"tags":426,"primary_topic":428,"secondary_topics":429,"classification":430,"search_phrases":446,"slug":449,"view_count":104,"doi":450,"paper":451,"created_at":474},5393,"IMPLEMENTATION OF META-HEURISTIC OPTIMIZATION ALGORITHMS IN CONVOLUTIONAL NEURAL NETWORKS FOR OBJECT DETECTION AND CLASSIFICATION","https:\u002F\u002Fdoi.org\u002F10.22190\u002Ffuacr251214006i","CNNs have grown to form the backbone of modern computer vision systems, producing outstanding object detection and classification performance within diverse application domains. However, the manual development of suitable CNN architectures and, correspondingly, the selection of optimal hyperparameter sets are still inconvenient and time-consuming processes. The aim of this survey is to contribute a comprehensive review regarding how meta-heuristic optimization algorithms such as Genetic Algorithms, Particle Swarm Optimization, and other swarm intelligence-based methods are embedded in the CNN-based pipelines with the intent to achieve better detection and classification results. We gather recent progress regarding automated architecture search, hyperparameter tuning, model compression, and ensemble learning. We identify how hybrid optimization approaches consistently yield superior accuracy with computational efficiency by systematically reviewing applications in medical imaging, agriculture, vehicular recognition, and remote sensing. Optimized CNN models outperform their baseline versions many times, especially in health diagnostics, where several fine-tuned architectures reach accuracies greater than 99%. This survey provides a structured and updated overview of methodological developments, implementation practices, and emerging research directions for those practitioners and researchers who will apply meta-heuristic optimization within deep learning-based computer vision systems.","卷积神经网络（CNNs）已发展成为现代计算机视觉系统的核心骨干，在多个应用领域中展现出卓越的目标检测与分类性能。然而，人工设计合适的CNN架构并相应地选择最优超参数集仍然是繁琐且耗时的过程。本综述旨在全面回顾遗传算法、粒子群优化及其他群体智能方法等元启发式优化算法如何嵌入基于CNN的流程中以获得更优的检测与分类结果。我们汇集了自动化架构搜索、超参数调优、模型压缩和集成学习方面的最新进展。通过系统梳理在医学影像、农业、车辆识别和遥感领域的应用，我们揭示了混合优化方法如何以计算效率持续实现更优的精度。优化后的CNN模型在多数情况下远超其基线版本，尤其在健康诊断领域，若干精细调优的架构达到了超过99%的准确率。本综述为将在基于深度学习的计算机视觉系统中应用元启发式优化的从业者和研究者，提供了关于方法学进展、实施实践和新兴研究方向的结构化且更新的概述。","Facta Universitatis Series Automatic Control and Robotics","2026-10-18T00:00:00Z",61,{"impact":45,"substance":17,"depth":267,"authority":126,"freshness":45,"relevant":21,"comment":423},"综述系统梳理元启发式优化在CNN视觉任务中的嵌入方法，含农业应用，但属通用方法综述，非农业专属突破。",[425],{"name":419,"url":416},[131,26,427,27],"目标检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":431},{"candidates":432,"agricultural_evidence":46},[433,438,442],{"code":32,"score":434,"supported":46,"title":435,"abstract":436,"tags":437},11,[],[427,49,211,50,209],[26,427],{"code":53,"score":54,"supported":46,"title":439,"abstract":440,"tags":441},[],[27,57],[27],{"code":157,"score":21,"supported":14,"title":443,"abstract":444,"tags":445},[],[],[131],[447,448],"元启发式优化 卷积神经网络","CNN 目标检测","元启发式优化卷积神经网络-5393","10.22190\u002Ffuacr251214006i",{"doi":450,"openalex_id":452,"authors":453,"venue":419,"cited_by_count":104,"oa_url":416,"card":469,"direction":110,"ingested_from":112},"W7222920697",[454,457,460,463,466],{"name":455,"orcid":456},"Nikola Ivačko","https:\u002F\u002Forcid.org\u002F0000-0003-4367-119X",{"name":458,"orcid":459},"Ivan S Ciric","https:\u002F\u002Forcid.org\u002F0000-0003-0430-8937",{"name":461,"orcid":462},"Milica Tasić","https:\u002F\u002Forcid.org\u002F0009-0001-9160-6379",{"name":464,"orcid":465},"Ljiljana Radović","https:\u002F\u002Forcid.org\u002F0000-0002-4099-1289",{"name":467,"orcid":468},"Žarko Ćojbašić","https:\u002F\u002Forcid.org\u002F0000-0002-4581-1048",{"tldr":470,"method":471,"finding":472,"direction":180,"opportunity":473},"综述元启发式算法嵌入CNN流程以优化目标检测与分类的研究进展。","系统综述遗传算法、粒子群等群智能方法用于CNN架构搜索与超参调优。","混合优化方法在医学影像、农业等领域兼顾精度与效率，部分模型准确率超99%。","农业场景下元启发式优化CNN的架构搜索与轻量化部署仍缺乏系统基准与田间验证。","2026-10-20T23:30:19.233167Z",{"id":476,"title":477,"url":478,"summary":479,"summary_zh":480,"content":9,"source_name":481,"source_url":478,"published_at":482,"category":12,"cover_url":9,"hotness":13,"is_selected":46,"score":483,"score_detail":484,"sources":489,"tags":491,"primary_topic":494,"secondary_topics":495,"classification":496,"search_phrases":523,"slug":526,"view_count":513,"doi":527,"paper":528,"created_at":554},5382,"Modeling the global potential of smart farming innovations","https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.2530081123","Smart farming innovations can improve agricultural productivity, resilience, and environmental sustainability, yet their real-world impact hinges on their diffusion dynamics, which in turn depend on their economic viability. We present here a global assessment of the profitability of smart farming innovation across croplands at a spatial resolution of ~5 km from the farmers’ and the societal perspectives. We cover application mapping for precision agriculture, automated application of inputs, autonomous weeding machines, GPS steering assistance systems, and autonomous irrigation systems. For this, we compiled a globally comprehensive dataset of smart farming innovation adoption locations (N = 4,422) based on a combination of a survey (N = 2,677) and a meta-analysis (N = 1,745) and combined them with remotely sensed and statistical data to train an ensemble of five geospatial machine learning models (generalized linear models, random forests, support vector machines, maximum entropy, and boosted regression trees), which show the global distribution of the different innovations’ profitability and its overlap with major environmental issues the innovations can help to mitigate. Simulations of future socioeconomic pathways combined with assumptions about future agri-environmental policies reveal that, especially in middle-income countries, the potential of smart farming will grow over time.","智慧农业创新能够提升农业生产力、韧性和环境可持续性，然而其现实影响取决于扩散动态，而扩散动态又取决于其经济可行性。本文从农民和社会两个视角，以约5公里的空间分辨率对全球农田范围内智慧农业创新的盈利能力进行了评估。研究涵盖精准农业的应用测绘、投入品的自动化施用、自主除草机械、GPS转向辅助系统以及自主灌溉系统。为此，我们基于一项调查（N = 2,677）和一项元分析（N = 1,745）相结合，编制了全球范围内较为全面的智慧农业创新采用地点数据集（N = 4,422），并将其与遥感和统计数据相结合，用于训练由五种地理空间机器学习模型（广义线性模型、随机森林、支持向量机、最大熵和提升回归树）组成的集成模型，这些模型揭示了不同创新盈利能力的全球分布及其与这些创新有助于缓解的主要环境问题的重叠情况。对未来社会经济路径的模拟结合对未来农业环境政策的假设表明，智慧农业的潜力将随时间推移而增长，尤其是在中等收入国家。","Proceedings of the National Academy of Sciences","2026-10-19T00:00:00Z",93,{"impact":485,"substance":486,"depth":487,"authority":267,"freshness":20,"relevant":21,"comment":488},26,24,19,"PNAS 全球尺度智慧农业盈利性评估，数据规模与方法新颖，结论对智慧农业推广具政策参考价值，值得进入每日精选。",[490],{"name":481,"url":478},[131,26,492,27,493],"精准农业","全球评估",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":497},{"candidates":498,"agricultural_evidence":46},[499,504,508,512,517],{"code":157,"score":434,"supported":46,"title":500,"abstract":501,"tags":503},[],[131,492,502],"precision agriculture",[131,492],{"code":32,"score":54,"supported":46,"title":505,"abstract":506,"tags":507},[],[275,276],[26],{"code":60,"score":61,"supported":46,"title":509,"abstract":510,"tags":511},[],[270,253],[],{"code":53,"score":513,"supported":46,"title":514,"abstract":515,"tags":516},4,[],[27],[27],{"code":518,"score":146,"supported":46,"title":519,"abstract":520,"tags":522},"F4",[],[521],"除草",[],[524,525],"农业人工智能 全球评估 智慧农业 精准农业","农业人工智能 全球评估","农业人工智能全球评估智慧农业精准农业-5382","10.1073\u002Fpnas.2530081123",{"doi":527,"openalex_id":529,"authors":530,"venue":481,"cited_by_count":104,"oa_url":478,"card":549,"direction":350,"ingested_from":112},"W7222974188",[531,534,537,540,543,546],{"name":532,"orcid":533},"Shahrear Roman","https:\u002F\u002Forcid.org\u002F0009-0006-5659-4292",{"name":535,"orcid":536},"Hadi Hadi","https:\u002F\u002Forcid.org\u002F0000-0002-2410-9751",{"name":538,"orcid":539},"Wyclife Agumba Oluoch","https:\u002F\u002Forcid.org\u002F0000-0003-0803-9279",{"name":541,"orcid":542},"David Zilberman","https:\u002F\u002Forcid.org\u002F0000-0001-5743-5134",{"name":544,"orcid":545},"Robert Finger","https:\u002F\u002Forcid.org\u002F0000-0002-0634-5742",{"name":547,"orcid":548},"David Wuepper","https:\u002F\u002Forcid.org\u002F0000-0002-1344-6023",{"tldr":550,"method":551,"finding":552,"direction":350,"opportunity":553},"全球5公里尺度评估五类智慧农业创新在农田的盈利性与扩散潜力。","整合4422个采用点调查与元分析数据，用五种地理空间机器学习集成建模。","智慧农业盈利性空间差异显著，中等收入国家未来潜力将随社会经济路径增长。","可结合区域政策与农户异质性，细化盈利性预测并评估环境协同效益。","2026-10-20T23:30:08.754594Z"]