[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2676":3,"related-2676":68},{"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":18,"tags":20,"primary_topic":9,"secondary_topics":24,"classification":25,"search_phrases":46,"slug":49,"view_count":41,"doi":50,"paper":51,"created_at":67},2676,"Prompt Engineering in the Segment Anything Model: Methodologies, Applications, and Emerging Challenges","https:\u002F\u002Fdoi.org\u002F10.34133\u002Ficomputing.1194","The Segment Anything Model (SAM) has transformed image segmentation by introducing a prompt-based paradigm that enables strong zero-shot generalization. In this framework, prompts serve as a semantic interface between human intent and machine perception, making prompt engineering a central factor in model performance. Despite its importance, prompt engineering within SAM and its variants has not yet been systematically reviewed in the literature. This survey addresses that gap by providing a structured and comprehensive overview of prompt engineering techniques developed for SAM and its rapidly growing ecosystem. We introduce a hierarchical taxonomy that organizes methods into geometric prompts, textual semantic prompts, and multimodal fusion prompts, and analyze how these categories reflect different design principles and application goals. In addition, we examine the transition from manually crafted prompts to more advanced, automated approaches based on detector outputs, prototype learning, reinforcement learning, and vision-language models. Beyond categorizing existing work, we trace how prompt engineering has enabled SAM to generalize across domains such as medical imaging, remote sensing, industrial inspection, and anomaly detection. We further identify key challenges---including prompt sensitivity, cross-modal misalignment, and computational inefficiency---and highlight promising research directions such as causal prompt reasoning, collaborative multi-agent prompting, and diffusion-based progressive refinement. By consolidating these developments into a unified perspective, our survey provides a timely reference for understanding the role of prompt engineering in segmentation foundation models and lays the groundwork for future advances in this evolving field.","分割一切模型（Segment Anything Model, SAM）通过引入基于提示的范式实现了强大的零样本泛化能力，从而变革了图像分割领域。在该框架中，提示充当人类意图与机器感知之间的语义接口，使提示工程成为影响模型性能的核心因素。尽管其重要性不言而喻，但SAM及其变体中的提示工程尚未在文献中得到系统性综述。本综述填补了这一空白，对为SAM及其快速发展的生态系统所开发的提示工程技术进行了结构化且全面的梳理。我们提出了一种层次化分类体系，将相关方法组织为几何提示、文本语义提示和多模态融合提示，并分析了这些类别如何反映不同的设计原则和应用目标。此外，我们考察了从人工设计提示向基于检测器输出、原型学习、强化学习和视觉语言模型的更先进自动化方法的转变。除了对现有工作进行归类之外，我们还追溯了提示工程如何使SAM泛化至医学影像、遥感、工业检测和异常检测等领域。我们进一步识别了关键挑战——包括提示敏感性、跨模态失配和计算低效——并指出了具有前景的研究方向，如因果提示推理、协作式多智能体提示和基于扩散的渐进式精化。通过将这些进展整合为统一视角，本综述为理解提示工程在分割基础模型中的作用提供了及时参考，并为这一不断演进领域的未来进展奠定了基础。",null,"Intelligent Computing","2026-09-14T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"SAM提示工程综述属计算机视觉通用方法研究，未涉及三农或农业信息化场景，相关性不足，不宜进入每日精选。",[19],{"name":10,"url":6},[21,22,23],"农业人工智能","遥感","图像分割",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":29},"needs_review","2026-10-22.2","rules",{"candidates":30,"agricultural_evidence":14,"reason":45},[31,39],{"code":32,"score":33,"supported":34,"title":35,"abstract":36,"tags":38},"I3",7,true,[],[22,37],"remote sensing",[22],{"code":40,"score":41,"supported":14,"title":42,"abstract":43,"tags":44},"I2",2,[],[],[21,23],"农业相关性需要复核",[47,48],"农业人工智能 图像分割 遥感","农业人工智能 图像分割","农业人工智能图像分割遥感-2676","10.34133\u002Ficomputing.1194",{"doi":50,"openalex_id":52,"authors":53,"venue":10,"cited_by_count":15,"oa_url":6,"card":60,"direction":64,"ingested_from":66},"W4414696217",[54,56,58],{"name":55,"orcid":9},"Yidong Jiang",{"name":57,"orcid":9},"Jiangtong Li",{"name":59,"orcid":9},"Dawei Cheng",{"tldr":61,"method":62,"finding":63,"direction":64,"opportunity":65},"系统综述了SAM及其变体中的提示工程方法、应用与挑战。","提出几何、文本语义、多模态融合提示的分类体系，并分析自动化提示方法。","提示工程使SAM泛化至医学、遥感等领域，但存在提示敏感、跨模态错位等挑战。","农业遥感与作物表型","可探索面向农业遥感场景的自动化提示生成与跨模态对齐方法，提升作物分割泛化性。","openalex","2026-09-16T23:30:31.001724Z",{"total":69,"page":70,"page_size":69,"items":71},6,1,[72,137,196,267,319,434],{"id":73,"title":74,"url":75,"summary":76,"summary_zh":77,"content":9,"source_name":78,"source_url":75,"published_at":79,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":80,"score_detail":81,"sources":88,"tags":90,"primary_topic":9,"secondary_topics":92,"classification":93,"search_phrases":110,"slug":113,"view_count":15,"doi":114,"paper":115,"created_at":136},5245,"BTSR-Net: Boundary–topology guided structural refinement for high-resolution remote sensing image segmentation","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0360524","Semantic segmentation of high-resolution remote sensing imagery in complex urban scenes often suffers from insufficient modeling of boundary neighborhoods, causing ambiguity near class interfaces and weakening the continuity of elongated structures. To address this issue, we propose a Boundary-Topology-Guided Structural Refinement Network (BTSR-Net). Built upon HRNet-OCR, BTSR-Net introduces a Geometric Boundary-Distance Branch (GBD) with joint boundary-distance supervision to capture spatial transitions around class boundaries, a Multi-scale Topology-Guided Structural Fusion (MTSF) module to enhance connectivity representation of elongated structures, and a dual-stream structural refinement mechanism to perform local residual correction within a boundary-distance-defined refinement narrow-band. Experiments on the ISPRS Potsdam and Vaihingen datasets demonstrate improvements in overall segmentation accuracy, boundary quality, and structural connectivity. On Potsdam, BTSR-Net achieves 84.70% mIoU, 43.17% mBIoU, and 81.63% clDice, exceeding the baseline by 2.91, 5.66, and 3.74 percentage points, respectively. On Vaihingen, it achieves 93.94% mIoU, 73.90% mBIoU, and 95.46% clDice, with corresponding improvements of 2.27, 9.39, and 2.12 percentage points. These results demonstrate the effectiveness of BTSR-Net for structure-sensitive semantic segmentation in high-resolution remote sensing imagery.","复杂城市场景下高分辨率遥感影像的语义分割常因边界邻域建模不足，导致类别交界处出现模糊、细长结构连续性减弱。针对该问题，提出一种边界—拓扑引导的结构精化网络（BTSR-Net）。BTSR-Net以HRNet-OCR为基础，引入联合边界—距离监督的几何边界距离分支（GBD），以刻画类别边界周围的空间过渡；构建多尺度拓扑引导结构融合（MTSF）模块，以增强细长结构的连通性表征；并设计双流结构精化机制，在由边界距离定义的精化窄带内进行局部残差修正。在ISPRS Potsdam和Vaihingen数据集上的实验表明，该方法在整体分割精度、边界质量和结构连通性方面均有提升。在Potsdam数据集上，BTSR-Net取得84.70%的mIoU、43.17%的mBIoU和81.63%的clDice，分别较基线提高2.91、5.66和3.74个百分点；在Vaihingen数据集上，取得93.94%的mIoU、73.90%的mBIoU和95.46%的clDice，相应提高2.27、9.39和2.12个百分点。结果表明，BTSR-Net对高分辨率遥感影像中结构敏感的语义分割具有有效性。","PLoS ONE","2026-10-16T00:00:00Z",67,{"impact":82,"substance":83,"depth":84,"authority":85,"freshness":86,"relevant":70,"comment":87},8,20,17,13,9,"方法新颖、指标提升明确，但属遥感分割算法论文，与农业场景的直接关联有限，公共影响范围较小。",[89],{"name":78,"url":75},[91,21,22,23],"智慧农业",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":94},{"candidates":95,"agricultural_evidence":14,"reason":45},[96,101,105],{"code":32,"score":97,"supported":34,"title":98,"abstract":99,"tags":100},15,[37],[22,37],[22],{"code":40,"score":41,"supported":14,"title":102,"abstract":103,"tags":104},[],[],[21,23],{"code":106,"score":70,"supported":14,"title":107,"abstract":108,"tags":109},"I1",[],[],[91],[111,112],"BTSR-Net 遥感 分割","农业人工智能 图像分割 智慧农业 遥感","BTSR-Net遥感分割-5245","10.1371\u002Fjournal.pone.0360524",{"doi":114,"openalex_id":116,"authors":117,"venue":78,"cited_by_count":15,"oa_url":130,"card":131,"direction":64,"ingested_from":66},"W7222443080",[118,121,123,125,128],{"name":119,"orcid":120},"Ziheng Wang","https:\u002F\u002Forcid.org\u002F0000-0001-8873-732X",{"name":122,"orcid":9},"Wu Wang",{"name":124,"orcid":9},"Zhe Hu",{"name":126,"orcid":127},"Yang Li","https:\u002F\u002Forcid.org\u002F0000-0002-8314-6448",{"name":129,"orcid":9},"Kejia Ma","https:\u002F\u002Fjournals.plos.org\u002Fplosone\u002Farticle\u002Ffile?id=10.1371\u002Fjournal.pone.0360524&type=printable",{"tldr":132,"method":133,"finding":134,"direction":64,"opportunity":135},"提出BTSR-Net，用边界与拓扑引导提升高分辨率遥感影像分割的结构连续性。","基于HRNet-OCR，加入边界距离监督、多尺度拓扑融合与双流窄带细化，在ISP","Potsdam与Vaihingen上mIoU、边界IoU和clDice均显著提升，边界与细长结构更连","可将边界-拓扑引导细化迁移到农田地块与道路沟渠等细长地物分割，探索农业场景结构连续性评价。","2026-10-18T23:30:31.300711Z",{"id":138,"title":139,"url":140,"summary":141,"summary_zh":142,"content":9,"source_name":143,"source_url":140,"published_at":144,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":80,"score_detail":145,"sources":147,"tags":149,"primary_topic":9,"secondary_topics":150,"classification":151,"search_phrases":166,"slug":169,"view_count":70,"doi":170,"paper":171,"created_at":195},5097,"A lightweight hybrid network with inverted residuals and state space model for remote sensing image segmentation","https:\u002F\u002Fdoi.org\u002F10.1088\u002F1361-6501\u002Faeb43c","Abstract In current research on semantic segmentation, the majority of studies primarily focus on utilizing traditional CNNs and Transformer-based architectures. However, CNNs are limited in capturing long-range dependencies and accurately modeling global context information because their receptive fields are confined to local regions. Although Transformer can effectively achieve global context modeling through the self-attention mechanism, overcoming the limitations of local receptive fields, they inevitably introduce a computational burden that grows quadratically with the input size. In recent years, Mamba based on state-space models (SSMs) has received increasing attention due to its strong ability to capture global context while maintaining low computational complexity. However, in the field of remote sensing image segmentation, existing Mamba-based network architectures seldom achieve a balance between modeling long-range dependencies and extracting finegrained local features. Therefore, this paper proposed IRUM-Net, a novel hybrid architecture that integrates Mamba and CNN for semantic segmentation of remote sensing images. Specifically, the encoder of IRUM-Net adopted inverted residual blocks (IR Block) as its basic building units, it is designed to extract rich image features with low computational cost. Subsequently, we proposed a decoder module named Multi-Scale Local and Global Mamba Module (MLGM). This hybrid module effectively combines Mamba and CNN, allowing for the simultaneous modeling of global contextual dependencies and multi-scale local spatial details. At the same time, a Gated Enhanced Fusion (GEF) module was designed. It can connect and fuse the output of the previous decoder with the corresponding feature map in the encoder's skip connection at the same stage through a gating mechanism. This process yielded a refined feature representation enriched with multi-level information, which is then fed into the subsequent decoder stage. We conducted extensive experiments on two public datasets, Vaihingen and Potsdam, achieving 81.9% mIoU and 92.2% OA on the Vaihingen dataset, and 84.5% mIoU and 94.2% OA on the Potsdam dataset. Moreover, the overall network only has 1.98M parameters and 6.53G floating-point operations. The experiments demonstrate that the proposed IRUM-Net outperforms most semantic segmentation networks while maintaining low computational overhead, achieving excellent segmentation accuracy.","在当前的语义分割研究中，大多数研究主要集中于利用传统卷积神经网络（CNN）和基于Transformer的架构。然而，由于CNN的感受野局限于局部区域，其在捕获长程依赖关系和准确建模全局上下文信息方面存在局限。尽管Transformer能够通过自注意力机制有效实现全局上下文建模，克服局部感受野的限制，但其不可避免地引入了随输入尺寸呈二次方增长的计算负担。近年来，基于状态空间模型（SSM）的Mamba因其在保持低计算复杂度的同时具备强大的全局上下文捕获能力而受到越来越多的关注。然而，在遥感图像分割领域，现有的基于Mamba的网络架构很少能在建模长程依赖关系与提取细粒度局部特征之间取得平衡。因此，本文提出了IRUM-Net，一种融合Mamba和CNN的新型混合架构，用于遥感图像的语义分割。具体而言，IRUM-Net的编码器采用倒残差块（IR Block）作为基本构建单元，旨在以低计算成本提取丰富的图像特征。随后，我们提出了一种名为多尺度局部与全局Mamba模块（MLGM）的解码器模块。该混合模块有效结合了Mamba和CNN，能够同时建模全局上下文依赖关系和多尺度局部空间细节。同时，设计了门控增强融合（GEF）模块。它能够通过门控机制将前一解码器的输出与编码器跳跃连接中同一阶段的对应特征图进行连接和融合。该过程产生了富含多层级信息的精细化特征表示，随后被送入后续解码阶段。我们在Vaihingen和Potsdam两个公开数据集上进行了大量实验，在Vaihingen数据集上达到了81.9%的mIoU和92.2%的OA，在Potsdam数据集上达到了84.5%的mIoU和94.2%的OA。此外，整个网络仅有1.98M参数和6.53G浮点运算量。实验表明，所提出的IRUM-Net在保持低计算开销的同时优于大多数语义分割网络，实现了优异的分割精度。","Measurement Science and Technology","2026-10-14T00:00:00Z",{"impact":82,"substance":83,"depth":84,"authority":85,"freshness":86,"relevant":70,"comment":146},"提出融合Mamba与CNN的轻量遥感分割网络，参数少精度高，对农业遥感监测有参考价值，但属方法类论文，产业影响有限。",[148],{"name":143,"url":140},[91,21,22,23],[],{"status":26,"locked":14,"version":27,"method":28,"evidence":152},{"candidates":153,"agricultural_evidence":14,"reason":45},[154,158,162],{"code":32,"score":97,"supported":34,"title":155,"abstract":156,"tags":157},[37],[22,37],[22],{"code":40,"score":41,"supported":14,"title":159,"abstract":160,"tags":161},[],[],[21,23],{"code":106,"score":70,"supported":14,"title":163,"abstract":164,"tags":165},[],[],[91],[167,168],"IRUM-Net 遥感图像分割","Vaihingen Potsdam 语义分割","IRUM-Net遥感图像分割-5097","10.1088\u002F1361-6501\u002Faeb43c",{"doi":170,"openalex_id":172,"authors":173,"venue":143,"cited_by_count":15,"oa_url":189,"card":190,"direction":64,"ingested_from":66},"W7221793801",[174,177,180,183,186],{"name":175,"orcid":176},"Qixuan Tang","https:\u002F\u002Forcid.org\u002F0009-0008-2888-7930",{"name":178,"orcid":179},"Ming Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-2638-9002",{"name":181,"orcid":182},"Liuye Yu","https:\u002F\u002Forcid.org\u002F0009-0005-7458-5077",{"name":184,"orcid":185},"Yinghao Fan","https:\u002F\u002Forcid.org\u002F0009-0005-9915-4134",{"name":187,"orcid":188},"Xuanwen Liu","https:\u002F\u002Forcid.org\u002F0000-0003-0783-2427","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F1361-6501\u002Faeb43c\u002Fpdf",{"tldr":191,"method":192,"finding":193,"direction":64,"opportunity":194},"提出IRUM-Net混合网络，结合Mamba与CNN实现遥感图像轻量高精度语义分割。","倒残差块编码器+多尺度局部全局Mamba解码器+门控增强融合，在Vaihinge","Vaihingen达81.9% mIoU、92.2% OA，Potsdam达84.5% mIoU、9","可将该轻量混合架构迁移至农田遥感分割，探索多时相作物表型与边缘部署的精度-效率平衡。","2026-10-16T23:30:28.782677Z",{"id":197,"title":198,"url":199,"summary":200,"summary_zh":201,"content":9,"source_name":202,"source_url":199,"published_at":203,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":204,"score_detail":205,"sources":210,"tags":212,"primary_topic":215,"secondary_topics":220,"classification":221,"search_phrases":250,"slug":253,"view_count":15,"doi":254,"paper":255,"created_at":266},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":206,"substance":207,"depth":208,"authority":206,"freshness":13,"relevant":70,"comment":209},12,22,18,"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。",[211],{"name":202,"url":199},[91,21,213,22,214],"粮食安全","气候分析",{"code":40,"slug":216,"name":217,"path":218,"parent_code":219},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":222,"locked":14,"version":27,"method":28,"evidence":223},"classified",{"candidates":224,"agricultural_evidence":34},[225,230,237,242,246],{"code":40,"score":86,"supported":34,"title":226,"abstract":228,"tags":229},[227],"artificial intelligence",[],[21],{"code":231,"score":232,"supported":34,"title":233,"abstract":234,"tags":236},"I5",3,[],[235],"农业传感",[],{"code":238,"score":70,"supported":14,"title":239,"abstract":240,"tags":241},"A8",[],[],[213],{"code":106,"score":70,"supported":14,"title":243,"abstract":244,"tags":245},[],[],[91],{"code":32,"score":70,"supported":14,"title":247,"abstract":248,"tags":249},[],[],[22],[251,252],"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":254,"openalex_id":256,"authors":257,"venue":202,"cited_by_count":15,"oa_url":199,"card":260,"direction":265,"ingested_from":66},"W7223111766",[258],{"name":259,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":261,"method":262,"finding":263,"direction":64,"opportunity":264},"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":268,"title":269,"url":270,"summary":271,"summary_zh":272,"content":9,"source_name":202,"source_url":270,"published_at":203,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":80,"score_detail":273,"sources":275,"tags":277,"primary_topic":280,"secondary_topics":281,"classification":286,"search_phrases":303,"slug":306,"view_count":15,"doi":307,"paper":308,"created_at":318},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。",{"impact":82,"substance":83,"depth":84,"authority":206,"freshness":13,"relevant":70,"comment":274},"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。",[276],{"name":202,"url":270},[91,21,278,22,279],"产量预测","可解释性",{"code":40,"slug":216,"name":217,"path":218,"parent_code":219},[282],{"code":32,"slug":283,"name":284,"path":285,"parent_code":219},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":222,"locked":14,"version":27,"method":28,"evidence":287},{"candidates":288,"agricultural_evidence":34},[289,295,299],{"code":40,"score":206,"supported":34,"title":290,"abstract":292,"tags":294},[291],"computer vision",[293],"计算机视觉",[21],{"code":32,"score":82,"supported":34,"title":296,"abstract":297,"tags":298},[],[22,37],[278,22],{"code":106,"score":70,"supported":14,"title":300,"abstract":301,"tags":302},[],[],[91],[304,305],"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":307,"openalex_id":309,"authors":310,"venue":202,"cited_by_count":15,"oa_url":270,"card":313,"direction":64,"ingested_from":66},"W7223114002",[311],{"name":312,"orcid":9},"Kadiatou Barry",{"tldr":314,"method":315,"finding":316,"direction":64,"opportunity":317},"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":320,"title":321,"url":322,"summary":323,"summary_zh":324,"content":9,"source_name":325,"source_url":322,"published_at":203,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":326,"score_detail":327,"sources":329,"tags":331,"primary_topic":334,"secondary_topics":340,"classification":346,"search_phrases":404,"slug":407,"view_count":15,"doi":408,"paper":409,"created_at":433},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":208,"substance":207,"depth":208,"authority":206,"freshness":86,"relevant":70,"comment":328},"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。",[330],{"name":325,"url":322},[91,21,22,332,333],"作物水分胁迫","精准灌溉",{"code":335,"slug":336,"name":337,"path":338,"parent_code":339},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[341,342],{"code":40,"slug":216,"name":217,"path":218,"parent_code":219},{"code":231,"slug":343,"name":344,"path":345,"parent_code":219},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":222,"locked":14,"version":27,"method":28,"evidence":347},{"candidates":348,"agricultural_evidence":34},[349,354,360,367,373,377,384,391,398],{"code":335,"score":97,"supported":34,"title":350,"abstract":351,"tags":353},[336],[352,336],"灌溉",[333],{"code":40,"score":13,"supported":34,"title":355,"abstract":356,"tags":359},[],[357,358,227],"机器学习","machine learning",[21],{"code":231,"score":86,"supported":34,"title":361,"abstract":362,"tags":366},[],[363,364,365],"物联网","internet of things","IoT",[],{"code":106,"score":33,"supported":34,"title":368,"abstract":369,"tags":372},[],[370,371],"数字孪生","digital twin",[91],{"code":32,"score":33,"supported":34,"title":374,"abstract":375,"tags":376},[],[22,37],[22],{"code":378,"score":69,"supported":34,"title":379,"abstract":380,"tags":383},"A2",[],[381,382],"小麦","wheat",[],{"code":385,"score":69,"supported":34,"title":386,"abstract":387,"tags":390},"A3",[],[388,389],"玉米","maize",[],{"code":392,"score":69,"supported":34,"title":393,"abstract":394,"tags":397},"A5",[],[395,396],"马铃薯","potato",[],{"code":399,"score":232,"supported":34,"title":400,"abstract":401,"tags":403},"B2",[],[402],"葡萄",[],[405,406],"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":408,"openalex_id":410,"authors":411,"venue":325,"cited_by_count":15,"oa_url":322,"card":427,"direction":432,"ingested_from":66},"W7223088921",[412,415,418,421,424],{"name":413,"orcid":414},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":416,"orcid":417},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":419,"orcid":420},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":422,"orcid":423},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":425,"orcid":426},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":428,"method":429,"finding":430,"direction":64,"opportunity":431},"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":435,"title":436,"url":437,"summary":438,"summary_zh":439,"content":9,"source_name":202,"source_url":437,"published_at":203,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":440,"score_detail":441,"sources":444,"tags":446,"primary_topic":450,"secondary_topics":451,"classification":454,"search_phrases":482,"slug":485,"view_count":15,"doi":486,"paper":487,"created_at":497},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":208,"substance":83,"depth":442,"authority":206,"freshness":86,"relevant":70,"comment":443},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。",[445],{"name":202,"url":437},[21,447,22,448,449],"农业物联网","智慧灌溉","节水农业",{"code":335,"slug":336,"name":337,"path":338,"parent_code":339},[452,453],{"code":231,"slug":343,"name":344,"path":345,"parent_code":219},{"code":32,"slug":283,"name":284,"path":285,"parent_code":219},{"status":222,"locked":14,"version":27,"method":28,"evidence":455},{"candidates":456,"agricultural_evidence":34},[457,463,467,471,475],{"code":335,"score":458,"supported":34,"title":459,"abstract":460,"tags":462},19,[336],[352,461,336],"水资源",[448,449],{"code":231,"score":208,"supported":34,"title":464,"abstract":465,"tags":466},[365],[363,364,365],[447],{"code":32,"score":97,"supported":34,"title":468,"abstract":469,"tags":470},[37],[22,37],[22],{"code":40,"score":206,"supported":34,"title":472,"abstract":473,"tags":474},[227],[227],[21],{"code":476,"score":69,"supported":34,"title":477,"abstract":478,"tags":481},"H8",[],[479,480],"可持续农业","sustainable agriculture",[],[483,484],"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":486,"openalex_id":488,"authors":489,"venue":202,"cited_by_count":15,"oa_url":437,"card":492,"direction":432,"ingested_from":66},"W7223090389",[490],{"name":491,"orcid":9},"Patil",{"tldr":493,"method":494,"finding":495,"direction":432,"opportunity":496},"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"]