[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2280":3,"related-2280":88},{"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":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":57,"slug":60,"view_count":61,"doi":62,"paper":63,"created_at":87},2280,"Predicting plant leaf functional traits using 2D spectral representation and multi-task learning with multi-gate mixture-of-experts","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112390","Predicting plant leaf functional traits using 2D spectral representation and multi-task learning with multi-gate mixture-of-experts。Computers and Electronics in Agriculture","利用二维光谱表示和多任务学习结合多门混合专家模型预测植物叶片功能性状。",null,"Computers and Electronics in Agriculture","2026-09-12T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,20,17,14,8,1,"核心期刊论文，提出二维光谱表征与多门专家混合多任务学习预测叶片功能性状，方法新颖、对作物表型与遥感监测有参考价值，但属细分方法进展，未达重大突破层级。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","遥感","作物表型","多任务学习",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"unclassified","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":55,"reason":56},[39,45,50],{"code":40,"score":41,"supported":14,"title":42,"abstract":43,"tags":44},"I3",2,[],[],[30,29],{"code":46,"score":22,"supported":14,"title":47,"abstract":48,"tags":49},"I1",[],[],[27],{"code":51,"score":22,"supported":14,"title":52,"abstract":53,"tags":54},"I2",[],[],[28],true,"缺少足够的主题证据",[58,59],"农业人工智能 多任务学习 作物表型 智慧农业","农业人工智能 多任务学习","农业人工智能多任务学习作物表型智慧农业-2280",3,"10.1016\u002Fj.compag.2026.112390",{"doi":62,"openalex_id":64,"authors":65,"venue":10,"cited_by_count":79,"oa_url":9,"card":80,"direction":84,"ingested_from":86},"W7212395422",[66,68,70,72,74,76],{"name":67,"orcid":9},"Jianping Huang",{"name":69,"orcid":9},"Xin Zhang",{"name":71,"orcid":9},"Guanglai Wang",{"name":73,"orcid":9},"Chong Mo",{"name":75,"orcid":9},"Zhenghang Wang",{"name":77,"orcid":78},"Wenlong Song","https:\u002F\u002Forcid.org\u002F0000-0002-8810-532X",0,{"tldr":81,"method":82,"finding":83,"direction":84,"opportunity":85},"用二维光谱表示与多门混合专家多任务学习预测植物叶片功能性状。","二维光谱表示、多任务学习、多门混合专家模型。","该方法能同时准确预测多种叶片功能性状，优于单任务模型。","农业遥感与作物表型","可探索将该多任务框架迁移到多作物、多时相的高光谱表型监测中。","openalex","2026-09-13T23:30:01.702074Z",{"total":89,"page":22,"page_size":89,"items":90},6,[91,175,234,320,380,430],{"id":92,"title":93,"url":94,"summary":95,"summary_zh":96,"content":9,"source_name":97,"source_url":94,"published_at":98,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":99,"score_detail":100,"sources":106,"tags":108,"primary_topic":110,"secondary_topics":115,"classification":125,"search_phrases":153,"slug":156,"view_count":41,"doi":157,"paper":158,"created_at":174},3164,"Spatiotemporal Deep Learning for Rice Plant Height Estimation from Multi-Temporal UAV RGB Imagery","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16182034","Accurate plant height estimation is important for monitoring crop growth and supporting precision agricultural management. Manual measurements are labor-intensive, while LiDAR-based methods are expensive and require complex processing. UAV photogrammetry provides a lower-cost alternative but remains challenging in flooded rice paddies because of canopy deformation and difficulties in terrain extraction. This study proposes Rice-STNet, a spatiotemporal deep learning framework for end-to-end rice plant height estimation using multi-temporal UAV RGB imagery. Rice-STNet integrates a convolutional neural network for spatial feature extraction, Time2Vec for temporal encoding, and a gated recurrent unit network for modeling temporal dependencies across observation dates. The framework was evaluated using field data collected from rice paddies over two growing seasons. Rice-STNet achieved an R2 of 0.97, a root mean squared error of 1.97 cm, and a mean absolute error of 1.14 cm. It outperformed random forest, support vector regression, a CNN-only baseline, and a UAV photogrammetry-based point-cloud approach. In addition, the framework generated high-resolution plant height maps for field-scale analysis of spatial growth variability. These results underscore the importance of jointly modeling spatial and temporal characteristics for continuously evolving crop traits. The proposed framework offers an accurate, scalable, and non-destructive solution for large-scale crop phenotyping and precision agriculture.","准确的株高估算对于监测作物生长和支持精准农业管理具有重要意义。人工测量劳动强度大，而基于激光雷达（LiDAR）的方法成本高昂且需要复杂的处理。无人机摄影测量提供了一种成本较低的替代方案，但在淹水稻田中仍面临挑战，原因在于冠层变形和地形提取困难。本研究提出了Rice-STNet，一种时空深度学习框架，用于利用多时相无人机RGB影像进行端到端水稻株高估算。Rice-STNet集成了用于空间特征提取的卷积神经网络、用于时间编码的Time2Vec，以及用于建模观测日期之间时间依赖关系的门控循环单元网络。该框架利用两个生长季从稻田采集的田间数据进行了评估。Rice-STNet取得了R²为0.97、均方根误差为1.97 cm、平均绝对误差为1.14 cm的结果。其性能优于随机森林、支持向量回归、仅使用CNN的基线方法以及基于无人机摄影测量的点云方法。此外，该框架生成了高分辨率株高图，用于田块尺度空间生长变异性分析。这些结果凸显了联合建模空间与时间特征对于持续变化的作物性状的重要性。所提出的框架为大规模作物表型分析和精准农业提供了一种准确、可扩展且非破坏性的解决方案。","Agriculture","2026-09-21T00:00:00Z",80,{"impact":101,"substance":102,"depth":101,"authority":103,"freshness":104,"relevant":22,"comment":105},18,22,13,9,"提出时空深度学习框架Rice-STNet，用多时相无人机RGB影像实现水稻株高高精度估算，方法新颖、数据跨两个生长季，对作物表型与精准农业有实用价值。",[107],{"name":97,"url":94},[27,28,109,29,30],"水稻",{"code":111,"slug":112,"name":109,"path":113,"parent_code":114},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[116,121],{"code":51,"slug":117,"name":118,"path":119,"parent_code":120},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":40,"slug":122,"name":123,"path":124,"parent_code":120},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":126,"locked":14,"version":35,"method":36,"evidence":127},"classified",{"candidates":128,"agricultural_evidence":55},[129,134,141,146],{"code":111,"score":101,"supported":55,"title":130,"abstract":131,"tags":133},[112],[109,132,112],"稻田",[109],{"code":51,"score":135,"supported":55,"title":136,"abstract":138,"tags":140},15,[137],"deep learning",[139,137],"深度学习",[28],{"code":40,"score":21,"supported":55,"title":142,"abstract":143,"tags":145},[],[30,144],"crop phenotyping",[30,29],{"code":46,"score":147,"supported":55,"title":148,"abstract":149,"tags":152},7,[],[150,151],"精准农业","precision agriculture",[27],[154,155],"无人机 RGB 水稻株高","Rice-STNet 水稻表型","无人机RGB水稻株高-3164","10.3390\u002Fagriculture16182034",{"doi":157,"openalex_id":159,"authors":160,"venue":97,"cited_by_count":79,"oa_url":94,"card":169,"direction":84,"ingested_from":86},"W7213887432",[161,164,166],{"name":162,"orcid":163},"Weiguo Wang","https:\u002F\u002Forcid.org\u002F0009-0003-4028-9363",{"name":165,"orcid":9},"Noboru Noguchi",{"name":167,"orcid":168},"Liangliang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-5055-3987",{"tldr":170,"method":171,"finding":172,"direction":84,"opportunity":173},"提出Rice-STNet时空深度学习框架，用多时相无人机RGB影像估算水稻株高。","CNN提取空间特征，Time2Vec编码时间，GRU建模时序依赖，两季稻田数据验","R²达0.97、RMSE 1.97cm，优于随机森林、SVR、纯CNN及点云方法。","可迁移至其他作物与多源遥感融合，探索轻量化模型及实时田间部署。","2026-09-22T23:30:18.545958Z",{"id":176,"title":177,"url":178,"summary":179,"summary_zh":180,"content":9,"source_name":181,"source_url":182,"published_at":183,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":184,"score_detail":185,"sources":187,"tags":189,"primary_topic":191,"secondary_topics":192,"classification":193,"search_phrases":208,"slug":211,"view_count":212,"doi":213,"paper":214,"created_at":233},2781,"Evaluating Mesh Reconstruction Methods for Crop Phenotyping","https:\u002F\u002Fdoi.org\u002F10.48550\u002Farxiv.2609.16926","Phenotyping an agricultural crop is crucial for studying its entire life cycle, as it provides vital insights to improve yield and, ultimately, food production. Doing the same for crops grown on remote sites is a challenge for the specialists who cannot be available on-site. 3D reconstruction techniques offer a promising solution to this problem by enabling crop digitization, allowing specialists to access the resulting 3D crop models from anywhere at any time. In this work, we evaluate recent 3D reconstruction pipelines for crop phenotyping. We focus on 7 mesh reconstruction pipelines and measure the fidelity and consistency of their outputs qualitatively and quantitatively. Our results suggest that the meshes produced by the GGGS, PGSR, and 2DGS are preferable to the other pipelines, owing to their quantitative metrics and visually pleasing outputs. The GGGS pipeline is better than the second-best pipeline (2DGS) by about 27\\% on the radar chart with 5 dimensions, namely, User ratings, Chamfer distance, LPIPS, PSNR, and SSIM.","对农作物进行表型分析对于研究其整个生命周期至关重要，因为它为提高产量并最终提升粮食生产提供了关键见解。对于生长在偏远地区的作物而言，由于专家无法亲临现场，开展同样的表型分析是一项挑战。三维重建技术通过实现作物数字化，使专家能够随时随地访问生成的作物三维模型，从而为这一问题提供了有前景的解决方案。在本研究中，我们评估了近期用于作物表型分析的三维重建流程。我们聚焦于7种网格重建流程，并对其输出的保真度和一致性进行了定性和定量评估。结果表明，GGGS、PGSR和2DGS生成的网格在定量指标和视觉输出方面优于其他流程。在包含5个维度（用户评分、倒角距离、LPIPS、PSNR和SSIM）的雷达图上，GGGS流程比排名第二的2DGS流程高出约27%。","arXiv (Cornell University)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.16926","2026-09-15T00:00:00Z",74,{"impact":17,"substance":18,"depth":19,"authority":103,"freshness":21,"relevant":22,"comment":186},"系统评测7种网格重建流程用于作物表型数字化，结论明确、指标可量化，对远程作物表型与三维数字化研究有实质参考价值。",[188],{"name":181,"url":182},[27,28,29,30,190],"三维重建",{"code":40,"slug":122,"name":123,"path":124,"parent_code":120},[],{"status":126,"locked":14,"version":35,"method":36,"evidence":194},{"candidates":195,"agricultural_evidence":55},[196,200,204],{"code":40,"score":17,"supported":55,"title":197,"abstract":198,"tags":199},[144],[30,144],[30,29],{"code":51,"score":41,"supported":14,"title":201,"abstract":202,"tags":203},[],[],[190,28],{"code":46,"score":22,"supported":14,"title":205,"abstract":206,"tags":207},[],[],[27],[209,210],"农业人工智能 三维重建 作物表型 智慧农业","农业人工智能 三维重建","农业人工智能三维重建作物表型智慧农业-2781",4,"10.48550\u002Farxiv.2609.16926",{"doi":213,"openalex_id":215,"authors":216,"venue":181,"cited_by_count":79,"oa_url":227,"card":228,"direction":84,"ingested_from":86},"W7213397688",[217,220,222,225],{"name":218,"orcid":219},"Karanvir Singh","https:\u002F\u002Forcid.org\u002F0009-0003-0484-119X",{"name":221,"orcid":9},"Theo Morales",{"name":223,"orcid":224},"Binh‐Son Hua","https:\u002F\u002Forcid.org\u002F0000-0002-5706-8634",{"name":226,"orcid":9},"Mukesh Saini","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2609.16926",{"tldr":229,"method":230,"finding":231,"direction":84,"opportunity":232},"评估7种网格重建流程在作物表型三维数字化中的保真度与一致性。","对比7种3D重建流程，用Chamfer距离、LPIPS、PSNR、SSIM及用户","GGGS、PGSR和2DGS输出更优，GGGS在五维雷达图上比2DGS高约27%。","可探索轻量化、田间实时三维重建，并建立作物表型专用网格质量评价标准。","2026-09-17T23:30:27.096274Z",{"id":235,"title":236,"url":237,"summary":238,"summary_zh":239,"content":9,"source_name":240,"source_url":237,"published_at":241,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":242,"score_detail":243,"sources":246,"tags":248,"primary_topic":250,"secondary_topics":251,"classification":252,"search_phrases":268,"slug":271,"view_count":41,"doi":272,"paper":273,"created_at":319},1303,"PhenoStream: A Cyberinfrastructure for Automated and AI-Based Crop Trait Extraction from Aerial Imagery","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.08.26.747008","The integration of digital technologies for high-throughput field phenotyping is critical for accelerating crop improvement in agriculture. However, extracting traits from remote sensing data remains constrained by fragmented workflows, manual intervention, and limited interoperability among existing tools, resulting in delays that hinder timely biological insight and decision-making. To address these challenges, we present PhenoStream (Phenotyping Streaming), a scalable, end-to-end cyberinfrastructure designed to automate the full lifecycle of aerial imagery-based phenotyping, from data acquisition to plot- and genotype-level inference. The framework integrates automated data ingestion from distributed field sites, geospatial processing, and AI-enabled trait extraction within a unified, user-accessible graphical interface. Its modular and extensible architecture supports adaptable trait modeling and seamless integration of new data sources, enabling deployment across diverse crops, environments, and experimental designs. We demonstrate the system across a large multi-location field trial network of bioenergy crops, where it enables high-throughput characterization of spatiotemporal growth dynamics, genotype-by-environment (GxE) interactions, and predictive modeling of key agronomic traits. By significantly reducing processing latency and manual effort, the platform facilitates near-real-time analysis and reproducible workflows. This work establishes a generalizable and scalable pathway for operationalizing very-high-spatial resolution aerial phenotyping in agricultural research. By bridging data acquisition and analytics, the end-to-end cyberinfrastructure provides a foundation for integrating heterogeneous and unstructured data streams--including remote sensing, environmental, and management data--toward data-driven decision making in agriculture.","将数字技术整合用于高通量田间表型分析，对于加速农业作物改良至关重要。然而，从遥感数据中提取性状仍受制于碎片化的工作流程、人工干预以及现有工具间有限的互操作性，导致处理延迟，阻碍了及时的生物学洞察与决策制定。为应对这些挑战，我们提出了PhenoStream（表型流），一种可扩展的端到端网络基础设施，旨在自动化基于航空影像的表型分析全生命周期，从数据采集到小区及基因型水平推断。该框架在统一且用户友好的图形界面中集成了来自分布式田间站点的自动化数据摄取、地理空间处理及人工智能驱动的性状提取。其模块化和可扩展架构支持适应性性状建模及新数据源的无缝整合，使其能够部署于多种作物、环境及实验设计之中。我们在一个大型多地点生物能源作物田间试验网络中展示了该系统，实现了时空生长动态的高通量表征、基因型-环境（GxE）互作分析以及关键农艺性状的预测建模。通过显著降低处理延迟和人工投入，该平台支持近实时分析和可重复的工作流程。本研究为农业研究中超高空间分辨率航空表型分析的操作化提供了一条通用且可扩展的路径。通过连接数据采集与分析，该端到端网络基础设施为整合异构和非结构化数据流——包括遥感、环境及管理数据——奠定了面向农业数据驱动决策的基础。","bioRxiv (Cold Spring Harbor Laboratory)","2026-08-30T00:00:00Z",82,{"impact":102,"substance":102,"depth":101,"authority":244,"freshness":21,"relevant":22,"comment":245},12,"提出端到端自动化表型提取平台，显著提升效率，具推广价值。",[247],{"name":240,"url":237},[27,28,29,30,249],"田间表型",{"code":40,"slug":122,"name":123,"path":124,"parent_code":120},[],{"status":126,"locked":14,"version":35,"method":36,"evidence":253},{"candidates":254,"agricultural_evidence":55},[255,260,264],{"code":40,"score":21,"supported":55,"title":256,"abstract":257,"tags":259},[],[29,258],"remote sensing",[30,29],{"code":46,"score":22,"supported":14,"title":261,"abstract":262,"tags":263},[],[],[27],{"code":51,"score":22,"supported":14,"title":265,"abstract":266,"tags":267},[],[],[28],[269,270],"农业人工智能 作物表型 智慧农业 田间表型","农业人工智能 作物表型","农业人工智能作物表型智慧农业田间表型-1303","10.64898\u002F2026.08.26.747008",{"doi":272,"openalex_id":274,"authors":275,"venue":240,"cited_by_count":79,"oa_url":311,"card":312,"direction":318,"ingested_from":86},"W7204800500",[276,279,281,284,286,288,290,292,295,297,300,302,304,306,308],{"name":277,"orcid":278},"Sebastián Varela","https:\u002F\u002Forcid.org\u002F0000-0001-8064-3636",{"name":280,"orcid":9},"Jeremy Ruhter",{"name":282,"orcid":283},"Erik J. Sacks","https:\u002F\u002Forcid.org\u002F0000-0001-7537-4558",{"name":285,"orcid":9},"Xuying Zheng",{"name":287,"orcid":9},"Dylan Allen",{"name":289,"orcid":9},"Anna Hale",{"name":291,"orcid":9},"Cory Landry",{"name":293,"orcid":294},"Xianyan Kuang","https:\u002F\u002Forcid.org\u002F0000-0002-5978-647X",{"name":296,"orcid":9},"B. J. Long",{"name":298,"orcid":299},"Yan Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-5804-8926",{"name":301,"orcid":9},"Shatabdi Proma",{"name":303,"orcid":9},"Sehijpreet Kaur",{"name":305,"orcid":9},"Diego Jarquin",{"name":307,"orcid":9},"Jesse Morrison",{"name":309,"orcid":310},"Andrew D. B. Leakey","https:\u002F\u002Forcid.org\u002F0000-0001-6251-024X","https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2026\u002F08\u002F30\u002F2026.08.26.747008.full.pdf",{"tldr":313,"method":314,"finding":315,"direction":316,"opportunity":317},"提出 PhenoStream 网络基础设施，自动化航空影像作物性状提取全流程。","集成数据采集、地理空间处理与 AI 性状提取的模块化架构。","在多地点生物能源作物试验中实现高通量表型分析，显著降低延迟。","农业人工智能与决策模型","可扩展至多源数据融合与实时决策，探索跨作物通用性状模型及边缘计算部署。","智慧农业 \u002F 农业物联网","2026-09-01T23:30:11.619805Z",{"id":321,"title":322,"url":323,"summary":324,"summary_zh":325,"content":9,"source_name":326,"source_url":323,"published_at":327,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":184,"score_detail":328,"sources":330,"tags":332,"primary_topic":335,"secondary_topics":336,"classification":337,"search_phrases":364,"slug":367,"view_count":79,"doi":368,"paper":369,"created_at":379},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",{"impact":244,"substance":102,"depth":101,"authority":244,"freshness":13,"relevant":22,"comment":329},"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。",[331],{"name":326,"url":323},[27,28,333,29,334],"粮食安全","气候分析",{"code":51,"slug":117,"name":118,"path":119,"parent_code":120},[],{"status":126,"locked":14,"version":35,"method":36,"evidence":338},{"candidates":339,"agricultural_evidence":55},[340,345,351,356,360],{"code":51,"score":104,"supported":55,"title":341,"abstract":343,"tags":344},[342],"artificial intelligence",[],[28],{"code":346,"score":61,"supported":55,"title":347,"abstract":348,"tags":350},"I5",[],[349],"农业传感",[],{"code":352,"score":22,"supported":14,"title":353,"abstract":354,"tags":355},"A8",[],[],[333],{"code":46,"score":22,"supported":14,"title":357,"abstract":358,"tags":359},[],[],[27],{"code":40,"score":22,"supported":14,"title":361,"abstract":362,"tags":363},[],[],[29],[365,366],"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":368,"openalex_id":370,"authors":371,"venue":326,"cited_by_count":79,"oa_url":323,"card":374,"direction":316,"ingested_from":86},"W7223111766",[372],{"name":373,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":375,"method":376,"finding":377,"direction":84,"opportunity":378},"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。","2026-10-22T11:21:02.682497Z",{"id":381,"title":382,"url":383,"summary":384,"summary_zh":385,"content":9,"source_name":326,"source_url":383,"published_at":327,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":386,"score_detail":387,"sources":389,"tags":391,"primary_topic":394,"secondary_topics":395,"classification":397,"search_phrases":414,"slug":417,"view_count":79,"doi":418,"paper":419,"created_at":429},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":21,"substance":18,"depth":19,"authority":244,"freshness":13,"relevant":22,"comment":388},"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。",[390],{"name":326,"url":383},[27,28,392,29,393],"产量预测","可解释性",{"code":51,"slug":117,"name":118,"path":119,"parent_code":120},[396],{"code":40,"slug":122,"name":123,"path":124,"parent_code":120},{"status":126,"locked":14,"version":35,"method":36,"evidence":398},{"candidates":399,"agricultural_evidence":55},[400,406,410],{"code":51,"score":244,"supported":55,"title":401,"abstract":403,"tags":405},[402],"computer vision",[404],"计算机视觉",[28],{"code":40,"score":21,"supported":55,"title":407,"abstract":408,"tags":409},[],[29,258],[392,29],{"code":46,"score":22,"supported":14,"title":411,"abstract":412,"tags":413},[],[],[27],[415,416],"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":418,"openalex_id":420,"authors":421,"venue":326,"cited_by_count":79,"oa_url":383,"card":424,"direction":84,"ingested_from":86},"W7223114002",[422],{"name":423,"orcid":9},"Kadiatou Barry",{"tldr":425,"method":426,"finding":427,"direction":84,"opportunity":428},"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":431,"title":432,"url":433,"summary":434,"summary_zh":435,"content":9,"source_name":436,"source_url":433,"published_at":327,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":437,"score_detail":438,"sources":440,"tags":442,"primary_topic":445,"secondary_topics":451,"classification":457,"search_phrases":515,"slug":518,"view_count":79,"doi":519,"paper":520,"created_at":543},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":101,"substance":102,"depth":101,"authority":244,"freshness":104,"relevant":22,"comment":439},"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。",[441],{"name":436,"url":433},[27,28,29,443,444],"作物水分胁迫","精准灌溉",{"code":446,"slug":447,"name":448,"path":449,"parent_code":450},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[452,453],{"code":51,"slug":117,"name":118,"path":119,"parent_code":120},{"code":346,"slug":454,"name":455,"path":456,"parent_code":120},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":126,"locked":14,"version":35,"method":36,"evidence":458},{"candidates":459,"agricultural_evidence":55},[460,465,471,478,484,488,495,502,509],{"code":446,"score":135,"supported":55,"title":461,"abstract":462,"tags":464},[447],[463,447],"灌溉",[444],{"code":51,"score":13,"supported":55,"title":466,"abstract":467,"tags":470},[],[468,469,342],"机器学习","machine learning",[28],{"code":346,"score":104,"supported":55,"title":472,"abstract":473,"tags":477},[],[474,475,476],"物联网","internet of things","IoT",[],{"code":46,"score":147,"supported":55,"title":479,"abstract":480,"tags":483},[],[481,482],"数字孪生","digital twin",[27],{"code":40,"score":147,"supported":55,"title":485,"abstract":486,"tags":487},[],[29,258],[29],{"code":489,"score":89,"supported":55,"title":490,"abstract":491,"tags":494},"A2",[],[492,493],"小麦","wheat",[],{"code":496,"score":89,"supported":55,"title":497,"abstract":498,"tags":501},"A3",[],[499,500],"玉米","maize",[],{"code":503,"score":89,"supported":55,"title":504,"abstract":505,"tags":508},"A5",[],[506,507],"马铃薯","potato",[],{"code":510,"score":61,"supported":55,"title":511,"abstract":512,"tags":514},"B2",[],[513],"葡萄",[],[516,517],"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":519,"openalex_id":521,"authors":522,"venue":436,"cited_by_count":79,"oa_url":433,"card":538,"direction":318,"ingested_from":86},"W7223088921",[523,526,529,532,535],{"name":524,"orcid":525},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":527,"orcid":528},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":530,"orcid":531},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":533,"orcid":534},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":536,"orcid":537},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":539,"method":540,"finding":541,"direction":84,"opportunity":542},"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"]