[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2169":3,"related-2169":89},{"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":88},2169,"GeoWombat: Scalable geospatial and remote sensing analysis in Python","https:\u002F\u002Fdoi.org\u002F10.21105\u002Fjoss.10812","GeoWombat is an open-source Python library that provides an end-to-end platform for geospatial raster data processing and remote sensing analysis at scale.Built on xarray (Hoyer & Hamman, 2017), Dask (Rocklin, 2015), and rasterio (Gillies & others, 2013--), GeoWombat simplifies common but complex operations-such as mosaicking multi-tile imagery, reprojecting across coordinate reference systems, aligning rasters of varying resolutions, and performing radiometric corrections-into concise, intuitive commands.The library includes built-in sensor profiles for Landsat 1-8, Sentinel-1 and Sentinel-2, MODIS, and NAIP that automate band naming, scaling, and metadata handling.It supports workflows spanning cloud-based data access via SpatioTemporal Asset Catalogs (STAC) from multiple providers, a full radiometric processing chain (DN-to-reflectance conversion, atmospheric correction, BRDF normalization, and topographic correction), vegetation index computation, raster-vector interoperability, Cython-accelerated moving window statistics, scikit-learn-based (Pedregosa et al., 2011) machine learning classification, deep learning with PyTorch (Paszke et al., 2019), and georeferenced object detection.By leveraging Dask's lazy evaluation and task graphs, GeoWombat enables out-of-core processing of raster datasets of any size on commodity hardware.","GeoWombat是一个开源Python库，为大规模地理空间栅格数据处理和遥感分析提供端到端平台。它构建于xarray（Hoyer & Hamman, 2017）、Dask（Rocklin, 2015）和rasterio（Gillies & others, 2013--）之上，将多瓦片影像镶嵌、跨坐标参考系重投影、不同分辨率栅格对齐以及辐射校正等常见但复杂的操作简化为简洁直观的命令。该库内置了Landsat 1-8、Sentinel-1和Sentinel-2、MODIS以及NAIP的传感器配置文件，可自动完成波段命名、缩放和元数据处理。它支持的工作流涵盖通过来自多个提供商的时空资产目录（STAC）进行云端数据访问、完整的辐射处理链（DN到反射率转换、大气校正、BRDF归一化和地形校正）、植被指数计算、栅格-矢量互操作、Cython加速的移动窗口统计、基于scikit-learn（Pedregosa et al., 2011）的机器学习分类、基于PyTorch（Paszke et al., 2019）的深度学习以及地理参考目标检测。通过利用Dask的惰性求值和任务图，GeoWombat能够在普通硬件上对任意大小的栅格数据集进行外存处理。",null,"The Journal of Open Source Software","2026-09-09T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,13,8,1,"开源遥感分析库，集成多源卫星与深度学习能力，对农业遥感监测有实用价值，但属工具类论文，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业人工智能","遥感","作物监测","开源工具","植被指数",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":56},[39,46],{"code":40,"score":19,"supported":41,"title":42,"abstract":44,"tags":45},"I3",true,[43],"remote sensing",[28,43],[29,31,28],{"code":47,"score":13,"supported":41,"title":48,"abstract":49,"tags":55},"I2",[],[50,51,52,53,54],"目标检测","深度学习","机器学习","machine learning","deep learning",[27],"农业相关性需要复核",[58,59],"农业人工智能 作物监测 开源工具 植被指数","农业人工智能 作物监测","农业人工智能作物监测开源工具植被指数-2169",2,"10.21105\u002Fjoss.10812",{"doi":62,"openalex_id":64,"authors":65,"venue":10,"cited_by_count":79,"oa_url":80,"card":81,"direction":85,"ingested_from":87},"W7212022895",[66,69,72,74,77],{"name":67,"orcid":68},"Jordan Graesser","https:\u002F\u002Forcid.org\u002F0000-0002-6137-7050",{"name":70,"orcid":71},"Michael Mann","https:\u002F\u002Forcid.org\u002F0000-0002-6268-6867",{"name":73,"orcid":9},"Leonardo Hardtke",{"name":75,"orcid":76},"Robert Denham","https:\u002F\u002Forcid.org\u002F0000-0002-3342-7970",{"name":78,"orcid":9},"Sharon Xu",0,"https:\u002F\u002Fjoss.theoj.org\u002Fpapers\u002F10.21105\u002Fjoss.10812.pdf",{"tldr":82,"method":83,"finding":84,"direction":85,"opportunity":86},"发布开源Python库GeoWombat，实现大规模地理空间栅格数据与遥感分析的一站式处理。","基于xarray、Dask、rasterio，集成STAC云数据访问、辐射校正、","可借助Dask惰性求值在普通硬件上核外处理任意大小栅格，并内置多传感器配置。","农业遥感与作物表型","可基于该库构建作物表型参数自动化提取与深度学习分类流程，降低大规模遥感分析门槛。","openalex","2026-09-11T23:30:31.360896Z",{"total":90,"page":22,"page_size":90,"items":91},6,[92,174,249,303,357,416],{"id":93,"title":94,"url":95,"summary":96,"summary_zh":97,"content":9,"source_name":98,"source_url":95,"published_at":99,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":100,"score_detail":101,"sources":106,"tags":108,"primary_topic":110,"secondary_topics":115,"classification":120,"search_phrases":140,"slug":142,"view_count":143,"doi":144,"paper":145,"created_at":173},2660,"Red-Edge Information in Agricultural Remote Sensing: From Spectral Theory to Explainable Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183180","The red-edge (RE) spectral region has become a central component of agricultural remote sensing because it captures physiologically meaningful changes in chlorophyll content, canopy structure and vegetation functioning. The availability of dedicated RE bands on modern multispectral satellites and advances in hyperspectral sensing have stimulated widespread applications for crop monitoring, nutrient assessment, stress detection and yield prediction. However, reported improvements over conventional visible–near-infrared (VIS–NIR) approaches remain highly variable, and the mechanisms governing when and why RE information provides additional value are often poorly synthesised. This review presents a conceptual framework that links the physical and physiological basis of RE reflectance with its condition-dependent agronomic performance and its emerging role within modern machine learning (ML) systems. We first examine how pigment absorption, canopy structure and sensor characteristics jointly determine the representation of RE information from hyperspectral measurements to operational multispectral observations. We then synthesise evidence demonstrating that the agronomic value of RE information is strongly dependent on crop characteristics, phenological stage, environmental conditions and observation geometry, explaining much of the variability reported across previous studies. Finally, we show how recent advances in ML and explainable artificial intelligence have changed the interpretation of RE information. Rather than evaluating RE-derived vegetation indices in isolation, contemporary predictive frameworks integrate RE observations with complementary spectral, climatic, structural and temporal predictors, allowing their physiological contribution to be quantified within multidimensional models. We conclude that future value of RE remote sensing will require not only continued advances in spectral measurement and vegetation index development, but also improved interpretation, transferability and operational integration of physiologically meaningful RE information within explainable, multi-source agricultural monitoring systems.","红边（RE）光谱区已成为农业遥感的核心组成部分，因为它能够捕捉叶绿素含量、冠层结构和植被功能等方面具有生理意义的变化。现代多光谱卫星上专用红边波段的可用性以及高光谱传感技术的进步，推动了其在作物监测、养分评估、胁迫检测和产量预测中的广泛应用。然而，相较于传统可见光—近红外（VIS–NIR）方法所报道的改进效果仍高度可变，而关于红边信息何时以及为何提供额外价值的机制往往缺乏系统梳理。本综述提出了一个概念框架，将红边反射率的物理与生理基础与其条件依赖的农学表现及其在现代机器学习（ML）系统中新兴的作用联系起来。我们首先探讨色素吸收、冠层结构和传感器特性如何共同决定从高光谱测量到业务化多光谱观测中红边信息的表征。随后，我们综合证据表明，红边信息的农学价值强烈依赖于作物特征、物候阶段、环境条件和观测几何，这解释了以往研究中报道的大部分变异性。最后，我们展示了机器学习和可解释人工智能的最新进展如何改变了对红边信息的解读。当代预测框架不再孤立地评估红边衍生的植被指数，而是将红边观测与互补的光谱、气候、结构和时间预测因子相结合，从而在多维模型中量化其生理贡献。我们得出结论：红边遥感的未来价值不仅需要光谱测量和植被指数开发的持续进步，还需要在可解释的多源农业监测系统中改进对具有生理意义的红边信息的解读、可迁移性和业务化整合。","Remote Sensing","2026-09-16T00:00:00Z",82,{"impact":102,"substance":103,"depth":102,"authority":104,"freshness":13,"relevant":22,"comment":105},18,22,14,"系统综述红边遥感从光谱机理到可解释机器学习的农学价值条件，方法框架新颖、结论可靠，对作物监测与产量预测有实质参考意义。",[107],{"name":98,"url":95},[109,27,28,29,31],"智慧农业",{"code":40,"slug":111,"name":112,"path":113,"parent_code":114},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[116],{"code":47,"slug":117,"name":118,"path":119,"parent_code":114},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":121,"locked":14,"version":35,"method":36,"evidence":122},"classified",{"candidates":123,"agricultural_evidence":41},[124,130,135],{"code":40,"score":18,"supported":41,"title":125,"abstract":126,"tags":129},[43],[28,43,127,128],"multispectral","hyperspectral",[29,31,28],{"code":47,"score":102,"supported":41,"title":131,"abstract":132,"tags":134},[53],[52,53,133],"artificial intelligence",[27],{"code":136,"score":22,"supported":14,"title":137,"abstract":138,"tags":139},"I1",[],[],[109],[141,59],"农业人工智能 作物监测 智慧农业 植被指数","农业人工智能作物监测智慧农业植被指数-2660",4,"10.3390\u002Frs18183180",{"doi":144,"openalex_id":146,"authors":147,"venue":98,"cited_by_count":79,"oa_url":95,"card":168,"direction":85,"ingested_from":87},"W7213344870",[148,151,153,156,159,162,165],{"name":149,"orcid":150},"Ignacio Fuentes","https:\u002F\u002Forcid.org\u002F0000-0001-7066-7482",{"name":152,"orcid":9},"Nikolas Hoskin",{"name":154,"orcid":155},"Patrick Filippi","https:\u002F\u002Forcid.org\u002F0000-0003-3573-084X",{"name":157,"orcid":158},"Abhasha Joshi","https:\u002F\u002Forcid.org\u002F0000-0002-1422-465X",{"name":160,"orcid":161},"Yi Yu","https:\u002F\u002Forcid.org\u002F0000-0002-1140-2713",{"name":163,"orcid":164},"Thomas F. A. Bishop","https:\u002F\u002Forcid.org\u002F0000-0002-6723-7323",{"name":166,"orcid":167},"Dhahi Al-Shammari","https:\u002F\u002Forcid.org\u002F0000-0001-6608-8322",{"tldr":169,"method":170,"finding":171,"direction":85,"opportunity":172},"综述红边遥感在农业中的物理基础、条件依赖性与可解释机器学习中的新角色。","文献综述，整合光谱理论、多光谱\u002F高光谱数据与可解释AI方法。","红边信息的农学价值高度依赖作物、物候、环境与观测几何，需多源融合与可解释建模。","可研究红边信息在不同作物-物候-环境组合下的可迁移性，并构建可解释多源融合模型。","2026-09-16T23:30:28.719858Z",{"id":175,"title":176,"url":177,"summary":178,"summary_zh":179,"content":9,"source_name":180,"source_url":177,"published_at":181,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":182,"score_detail":183,"sources":186,"tags":188,"primary_topic":9,"secondary_topics":191,"classification":192,"search_phrases":229,"slug":232,"view_count":79,"doi":233,"paper":234,"created_at":248},4893,"Systematic vegetation index discovery for phenology-based crop mapping","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrsen.2026.1911652","Introduction The Systematic Vegetation Index Discovery (SVID) framework generates and evaluates large numbers of vegetation indices (VIs) by combining spectral bands with basic arithmetic operations. We evaluated whether systematically discovered indices could improve agricultural land-cover classification compared with conventional indices such as NDVI while reducing the dimensionality of multitemporal spectral data. Methods We applied SVID to a 323.9 ha agricultural landscape in Andong, South Korea, containing rice paddies, pepper fields, and plastic-covered greenhouses, using 32 cloud‐free Sentinel‐2A images acquired during 2022. Of 1,328 candidate expressions, 1,144 produced numerically valid rasters. A separate Random Forest model was trained for each index using its 32‐date temporal profile and evaluated against 500 independent reference points withheld from training. Before ranking, 388 expressions were identified as algebraically identical to another expression, and a further 355 were strictly monotone transformations and therefore indistinguishable to a rank‐based classifier. Results The 1,144 indices consequently represented 401 distinct features. Several near‐infrared‐to‐visible ratio families outperformed NDVI. The best‐performing index, (NIR – Red)\u002F(Green × Blue), achieved 97.0% overall accuracy and a Kappa coefficient of 0.873, compared with 94.4% and 0.734 for NDVI; this difference was significant by McNemar's test (p = 0.002). The improvement was concentrated in the greenhouse class, whose producer's accuracy increased from 58.6% to 84.5%. A model using all four raw bands across all dates (128 features) achieved 96.0% accuracy and was statistically indistinguishable from the best single index, demonstrating a four‐fold reduction in dimensionality without a detectable loss in accuracy. Rank positions were nevertheless unstable: ten of the 401 features were statistically tied with the best, and mathematically identical formulations differed by up to 3.4 percentage points because of finite‐precision effects. Discussion SVID can identify compact spectral representations that perform as well as or better than conventional indices and full multiband temporal models. However, the instability among top‐ranked indices suggests that results should be reported as index families with confidence intervals rather than as a single winner. Limitations include the use of one site, one year, and four spectral bands; broader testing across sites, years, and spectral configurations is needed.","引言 系统性植被指数发现（Systematic Vegetation Index Discovery, SVID）框架通过将光谱波段与基本算术运算相结合，生成并评估大量植被指数（vegetation indices, VIs）。我们评估了系统性发现的指数能否在降低多时相光谱数据维度的同时，相比NDVI等传统指数提升农业土地覆盖分类性能。方法 我们将SVID应用于韩国安东一处323.9公顷的农业景观，该区域包含稻田、辣椒田和覆膜温室，使用2022年获取的32景无云Sentinel-2A影像。在1,328个候选表达式中，1,144个生成了数值有效栅格。针对每个指数，利用其32个时相的时间剖面分别训练随机森林模型，并在500个未参与训练的独立参考点上进行评估。在排序之前，388个表达式被识别为与另一表达式代数等价，另有355个为严格单调变换，因而对基于秩的分类器不可区分。结果 这1,144个指数因此代表401个不同特征。若干近红外与可见光比值族的表现优于NDVI。表现最佳的指数(NIR – Red)\u002F(Green × Blue)总体精度达到97.0%，Kappa系数为0.873，而NDVI分别为94.4%和0.734；该差异经McNemar检验显著（p = 0.002）。提升主要集中在温室类别，其生产者精度从58.6%提高至84.5%。使用所有时相全部四个原始波段（128个特征）的模型精度为96.0%，与最佳单一指数在统计上不可区分，表明在未检测到精度损失的情况下实现了四倍的维度缩减。然而，排名位置并不稳定：401个特征中有10个与最佳特征在统计上并列，且由于有限精度效应，数学上等价的公式之间差异可达3.4个百分点。讨论 SVID能够识别出性能与常规指数及全多波段时间模型相当或更优的紧凑光谱表示。然而，排名靠前指数之间的不稳定性表明，结果应以指数族及置信区间的形式报告，而非单一最优指数。局限性包括仅使用一个站点、一年数据和四个光谱波段；需要在不同站点、年份和s","Frontiers in Remote Sensing","2026-10-13T00:00:00Z",74,{"impact":17,"substance":103,"depth":102,"authority":20,"freshness":184,"relevant":22,"comment":185},9,"提出系统性植被指数发现框架，在韩国安洞农田验证优于NDVI，方法新颖但仅单点单年，属细分领域方法学进展。",[187],{"name":180,"url":177},[27,189,28,190,31],"精准农业","作物识别",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":193},{"candidates":194,"agricultural_evidence":41,"reason":228},[195,202,209,216,220,224],{"code":196,"score":90,"supported":41,"title":197,"abstract":198,"tags":201},"A1",[],[199,200],"稻田","rice",[],{"code":203,"score":90,"supported":41,"title":204,"abstract":205,"tags":208},"B7",[],[206,207],"温室","greenhouse",[],{"code":210,"score":211,"supported":41,"title":212,"abstract":213,"tags":215},"B1",3,[],[214],"辣椒",[],{"code":40,"score":61,"supported":14,"title":217,"abstract":218,"tags":219},[],[],[31,28],{"code":136,"score":22,"supported":14,"title":221,"abstract":222,"tags":223},[],[],[189],{"code":47,"score":22,"supported":14,"title":225,"abstract":226,"tags":227},[],[],[27],"多个产业证据相当，需要确认主归属",[230,231],"Sentinel-2 作物分类 植被指数","SVID 植被指数 水稻 辣椒","Sentinel-2作物分类植被指数-4893","10.3389\u002Ffrsen.2026.1911652",{"doi":233,"openalex_id":235,"authors":236,"venue":180,"cited_by_count":79,"oa_url":242,"card":243,"direction":85,"ingested_from":87},"W7221623694",[237,240],{"name":238,"orcid":239},"Ehsan Rahimi","https:\u002F\u002Forcid.org\u002F0000-0002-8401-472X",{"name":241,"orcid":9},"Chuleui Jung","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fremote-sensing\u002Farticles\u002F10.3389\u002Ffrsen.2026.1911652\u002Fpdf",{"tldr":244,"method":245,"finding":246,"direction":85,"opportunity":247},"系统生成并评估大量植被指数，用于基于物候的农作物分类，发现优于NDVI的指数。","Sentinel-2A时序影像，组合光谱波段生成1328个指数，随机森林分类。","最佳指数(NIR-Red)\u002F(Green×Blue)精度97.0%，优于NDVI的94.4%，且维度","可探索更多波段组合与多站点验证，并研究指数排名不稳定的置信区间报告方法。","2026-10-14T23:30:20.231693Z",{"id":250,"title":251,"url":252,"summary":253,"summary_zh":254,"content":9,"source_name":255,"source_url":252,"published_at":256,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":257,"score_detail":258,"sources":261,"tags":263,"primary_topic":265,"secondary_topics":266,"classification":268,"search_phrases":284,"slug":286,"view_count":211,"doi":287,"paper":288,"created_at":302},2163,"A Hybrid Transfer Learning Framework for Seasonal Classification of Satellite Images","https:\u002F\u002Fdoi.org\u002F10.29109\u002Fgujsc.1958797","Seasonal classification from satellite imagery is an important remote sensing task for monitoring vegetation dynamics, agricultural processes, environmental change, and climate-related spatial patterns. However, developing robust deep learning models for this task is challenging due to limited labeled data, regional variability, and the computational cost of training large-scale networks from scratch. This study proposes a hybrid transfer learning-based framework for seasonal classification using satellite images collected from 81 provinces of Türkiye. A custom dataset was constructed from monthly satellite images, and eight pretrained deep learning architectures were evaluated as feature extractors. The extracted deep representations were classified using seven machine learning algorithms. The experimental results showed that both the choice of pretrained feature extractor and the classifier affect seasonal classification performance. Among models, ConvNeXt combined with the Multi-Layer Perceptron achieved the best performance. Based on the comparative analysis, ConvNeXt, Vision Transformer, and Swin Transformer were selected as the top three feature extractors, while the Multi-Layer Perceptron was selected as the final classifier. The proposed framework provides an effective and computationally practical approach for seasonal classification and offers a promising basis for future environmental monitoring and agricultural remote sensing applications.","基于卫星影像的季节分类是一项重要的遥感任务，可用于监测植被动态、农业过程、环境变化以及与气候相关的空间格局。然而，由于标注数据有限、区域差异以及从零开始训练大规模网络的计算成本，开发用于该任务的稳健深度学习模型具有挑战性。本研究提出了一种基于混合迁移学习的框架，利用从土耳其81个省份收集的卫星影像进行季节分类。研究构建了一个由月度卫星影像组成的自定义数据集，并评估了八种预训练深度学习架构作为特征提取器的效果。提取出的深层表示使用七种机器学习算法进行分类。实验结果表明，预训练特征提取器和分类器的选择均会影响季节分类性能。在各类模型中，ConvNeXt结合多层感知机取得了最佳性能。基于对比分析，ConvNeXt、Vision Transformer和Swin Transformer被选为排名前三的特征提取器，而多层感知机被选为最终分类器。所提出的框架为季节分类提供了一种有效且计算上实用的方法，并为未来环境监测和农业遥感应用提供了有前景的基础。","Gazi Üniversitesi Fen Bilimleri Dergisi Part C Tasarım ve Teknoloji","2026-09-10T00:00:00Z",66,{"impact":17,"substance":102,"depth":259,"authority":17,"freshness":21,"relevant":22,"comment":260},16,"基于土耳其81省卫星影像的迁移学习季节分类框架，方法对比扎实、结论可靠，对农业遥感监测有参考价值，但属学术论文且非国内应用，影响力有限。",[262],{"name":255,"url":252},[109,27,28,29,264],"迁移学习",{"code":47,"slug":117,"name":118,"path":119,"parent_code":114},[267],{"code":40,"slug":111,"name":112,"path":113,"parent_code":114},{"status":121,"locked":14,"version":35,"method":36,"evidence":269},{"candidates":270,"agricultural_evidence":41},[271,276,280],{"code":47,"score":272,"supported":41,"title":273,"abstract":274,"tags":275},11,[],[51,52,53,54],[27,264],{"code":40,"score":21,"supported":41,"title":277,"abstract":278,"tags":279},[],[28,43],[29,28],{"code":136,"score":22,"supported":14,"title":281,"abstract":282,"tags":283},[],[],[109],[285,59],"农业人工智能 作物监测 智慧农业 迁移学习","农业人工智能作物监测智慧农业迁移学习-2163","10.29109\u002Fgujsc.1958797",{"doi":287,"openalex_id":289,"authors":290,"venue":255,"cited_by_count":79,"oa_url":252,"card":297,"direction":85,"ingested_from":87},"W7212161719",[291,294],{"name":292,"orcid":293},"Eyyüp YILDIZ","https:\u002F\u002Forcid.org\u002F0000-0002-7051-3368",{"name":295,"orcid":296},"Özge Aslan Yıldız","https:\u002F\u002Forcid.org\u002F0000-0001-7688-9326",{"tldr":298,"method":299,"finding":300,"direction":85,"opportunity":301},"提出混合迁移学习框架，用预训练模型提取特征并结合机器学习分类器实现卫星图像季节分类。","基于土耳其81省月度卫星图像构建数据集，评估8种预训练模型和7种分类器。","ConvNeXt结合多层感知机表现最佳，特征提取器和分类器选择均影响性能。","可探索该框架在作物物候监测、跨区域迁移及多时相农业遥感中的泛化能力。","2026-09-11T23:30:29.840009Z",{"id":304,"title":305,"url":306,"summary":307,"summary_zh":308,"content":9,"source_name":309,"source_url":306,"published_at":310,"category":12,"cover_url":9,"hotness":311,"is_selected":14,"score":312,"score_detail":313,"sources":315,"tags":319,"primary_topic":320,"secondary_topics":321,"classification":323,"search_phrases":341,"slug":343,"view_count":61,"doi":344,"paper":345,"created_at":356},1327,"PRECISION AGRICULTURE ANALYTICS USING UAV MULTISPECTRAL IMAGING AND MACHINE LEARNING FOR CROP STRESS DETECTION AND YIELD OPTIMIZATION MODELS","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22111395","PRECISION AGRICULTURE ANALYTICS USING UAV MULTISPECTRAL IMAGING AND MACHINE LEARNING FOR CROP STRESS DETECTION AND YIELD OPTIMIZATION MODELS。International Journal of Engineering Technology Research & Management","利用无人机多光谱成像与机器学习进行作物胁迫检测及产量优化模型的精准农业分析。《国际工程技术研究与管理杂志》","International Journal of Engineering Technology Research & Management","2026-12-21T00:00:00Z",25,30,{"impact":21,"substance":21,"depth":13,"authority":143,"freshness":79,"relevant":22,"comment":314},"论文摘要信息有限，且发表于未来日期，时效性差，但主题相关，内容涉及无人机多光谱与机器学习在作物胁迫检测和产量优化中的应用，有一定技术价值。",[316,317],{"name":309,"url":306},{"name":309,"url":318},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22111394",[109,27,28,29],{"code":40,"slug":111,"name":112,"path":113,"parent_code":114},[322],{"code":47,"slug":117,"name":118,"path":119,"parent_code":114},{"status":121,"locked":14,"version":35,"method":36,"evidence":324},{"candidates":325,"agricultural_evidence":41},[326,331,337],{"code":40,"score":259,"supported":41,"title":327,"abstract":328,"tags":330},[127],[329,127],"无人机多光谱",[29,28],{"code":136,"score":332,"supported":41,"title":333,"abstract":335,"tags":336},15,[334],"precision agriculture",[189,334],[109],{"code":47,"score":332,"supported":41,"title":338,"abstract":339,"tags":340},[53],[52,53],[27],[342,59],"农业人工智能 作物监测 智慧农业 遥感","农业人工智能作物监测智慧农业遥感-1327","10.5281\u002Fzenodo.22111395",{"doi":344,"openalex_id":346,"authors":347,"venue":309,"cited_by_count":79,"oa_url":306,"card":350,"direction":355,"ingested_from":87},"W7204220081",[348],{"name":349,"orcid":9},"Adedayo Oluwaseyi Alawode",{"tldr":351,"method":352,"finding":353,"direction":85,"opportunity":354},"利用无人机多光谱成像与机器学习检测作物胁迫并优化产量。","无人机多光谱成像结合机器学习算法。","该方法可有效检测作物胁迫并优化产量。","可探索多光谱数据与深度学习结合，提高胁迫检测精度，并开发实时决策系统。","农业人工智能与决策模型","2026-09-01T23:30:38.036910Z",{"id":358,"title":359,"url":360,"summary":361,"summary_zh":362,"content":9,"source_name":363,"source_url":360,"published_at":364,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":182,"score_detail":365,"sources":367,"tags":369,"primary_topic":372,"secondary_topics":373,"classification":374,"search_phrases":400,"slug":403,"view_count":79,"doi":404,"paper":405,"created_at":415},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":17,"substance":103,"depth":102,"authority":17,"freshness":13,"relevant":22,"comment":366},"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。",[368],{"name":363,"url":360},[109,27,370,28,371],"粮食安全","气候分析",{"code":47,"slug":117,"name":118,"path":119,"parent_code":114},[],{"status":121,"locked":14,"version":35,"method":36,"evidence":375},{"candidates":376,"agricultural_evidence":41},[377,381,387,392,396],{"code":47,"score":184,"supported":41,"title":378,"abstract":379,"tags":380},[133],[],[27],{"code":382,"score":211,"supported":41,"title":383,"abstract":384,"tags":386},"I5",[],[385],"农业传感",[],{"code":388,"score":22,"supported":14,"title":389,"abstract":390,"tags":391},"A8",[],[],[370],{"code":136,"score":22,"supported":14,"title":393,"abstract":394,"tags":395},[],[],[109],{"code":40,"score":22,"supported":14,"title":397,"abstract":398,"tags":399},[],[],[28],[401,402],"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":404,"openalex_id":406,"authors":407,"venue":363,"cited_by_count":79,"oa_url":360,"card":410,"direction":355,"ingested_from":87},"W7223111766",[408],{"name":409,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":411,"method":412,"finding":413,"direction":85,"opportunity":414},"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":417,"title":418,"url":419,"summary":420,"summary_zh":421,"content":9,"source_name":363,"source_url":419,"published_at":364,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":422,"score_detail":423,"sources":425,"tags":427,"primary_topic":430,"secondary_topics":431,"classification":433,"search_phrases":450,"slug":453,"view_count":79,"doi":454,"paper":455,"created_at":465},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":17,"freshness":13,"relevant":22,"comment":424},"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。",[426],{"name":363,"url":419},[109,27,428,28,429],"产量预测","可解释性",{"code":47,"slug":117,"name":118,"path":119,"parent_code":114},[432],{"code":40,"slug":111,"name":112,"path":113,"parent_code":114},{"status":121,"locked":14,"version":35,"method":36,"evidence":434},{"candidates":435,"agricultural_evidence":41},[436,442,446],{"code":47,"score":17,"supported":41,"title":437,"abstract":439,"tags":441},[438],"computer vision",[440],"计算机视觉",[27],{"code":40,"score":21,"supported":41,"title":443,"abstract":444,"tags":445},[],[28,43],[428,28],{"code":136,"score":22,"supported":14,"title":447,"abstract":448,"tags":449},[],[],[109],[451,452],"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":454,"openalex_id":456,"authors":457,"venue":363,"cited_by_count":79,"oa_url":419,"card":460,"direction":85,"ingested_from":87},"W7223114002",[458],{"name":459,"orcid":9},"Kadiatou Barry",{"tldr":461,"method":462,"finding":463,"direction":85,"opportunity":464},"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"]