[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2163":3,"related-2163":93},{"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":43,"search_phrases":69,"slug":72,"view_count":73,"doi":74,"paper":75,"created_at":92},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。",null,"Gazi Üniversitesi Fen Bilimleri Dergisi Part C Tasarım ve Teknoloji","2026-09-10T00:00:00Z","论文",10,false,66,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,18,16,8,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","遥感","作物监测","迁移学习",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":44,"locked":14,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51},[49,59,64],{"code":32,"score":50,"supported":51,"title":52,"abstract":53,"tags":58},11,true,[],[54,55,56,57],"深度学习","机器学习","machine learning","deep learning",[27,30],{"code":39,"score":20,"supported":51,"title":60,"abstract":61,"tags":63},[],[28,62],"remote sensing",[29,28],{"code":65,"score":21,"supported":14,"title":66,"abstract":67,"tags":68},"I1",[],[],[26],[70,71],"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","农业人工智能作物监测智慧农业迁移学习-2163",3,"10.29109\u002Fgujsc.1958797",{"doi":74,"openalex_id":76,"authors":77,"venue":10,"cited_by_count":84,"oa_url":6,"card":85,"direction":89,"ingested_from":91},"W7212161719",[78,81],{"name":79,"orcid":80},"Eyyüp YILDIZ","https:\u002F\u002Forcid.org\u002F0000-0002-7051-3368",{"name":82,"orcid":83},"Özge Aslan Yıldız","https:\u002F\u002Forcid.org\u002F0000-0001-7688-9326",0,{"tldr":86,"method":87,"finding":88,"direction":89,"opportunity":90},"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。","农业遥感与作物表型","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。","openalex","2026-09-11T23:30:29.840009Z",{"total":94,"page":21,"page_size":94,"items":95},6,[96,169,225,278,358,455],{"id":97,"title":98,"url":99,"summary":100,"summary_zh":101,"content":9,"source_name":102,"source_url":99,"published_at":103,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":104,"score_detail":105,"sources":109,"tags":111,"primary_topic":113,"secondary_topics":114,"classification":116,"search_phrases":135,"slug":137,"view_count":138,"doi":139,"paper":140,"created_at":168},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":18,"substance":106,"depth":18,"authority":107,"freshness":13,"relevant":21,"comment":108},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。",[110],{"name":102,"url":99},[26,27,28,29,112],"植被指数",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[115],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":117},{"candidates":118,"agricultural_evidence":51},[119,126,131],{"code":39,"score":120,"supported":51,"title":121,"abstract":122,"tags":125},20,[62],[28,62,123,124],"multispectral","hyperspectral",[29,112,28],{"code":32,"score":18,"supported":51,"title":127,"abstract":128,"tags":130},[56],[55,56,129],"artificial intelligence",[27],{"code":65,"score":21,"supported":14,"title":132,"abstract":133,"tags":134},[],[],[26],[136,71],"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":139,"openalex_id":141,"authors":142,"venue":102,"cited_by_count":84,"oa_url":99,"card":163,"direction":89,"ingested_from":91},"W7213344870",[143,146,148,151,154,157,160],{"name":144,"orcid":145},"Ignacio Fuentes","https:\u002F\u002Forcid.org\u002F0000-0001-7066-7482",{"name":147,"orcid":9},"Nikolas Hoskin",{"name":149,"orcid":150},"Patrick Filippi","https:\u002F\u002Forcid.org\u002F0000-0003-3573-084X",{"name":152,"orcid":153},"Abhasha Joshi","https:\u002F\u002Forcid.org\u002F0000-0002-1422-465X",{"name":155,"orcid":156},"Yi Yu","https:\u002F\u002Forcid.org\u002F0000-0002-1140-2713",{"name":158,"orcid":159},"Thomas F. A. Bishop","https:\u002F\u002Forcid.org\u002F0000-0002-6723-7323",{"name":161,"orcid":162},"Dhahi Al-Shammari","https:\u002F\u002Forcid.org\u002F0000-0001-6608-8322",{"tldr":164,"method":165,"finding":166,"direction":89,"opportunity":167},"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":170,"title":171,"url":172,"summary":173,"summary_zh":174,"content":9,"source_name":175,"source_url":172,"published_at":176,"category":12,"cover_url":9,"hotness":177,"is_selected":14,"score":178,"score_detail":179,"sources":181,"tags":185,"primary_topic":186,"secondary_topics":187,"classification":189,"search_phrases":208,"slug":210,"view_count":211,"doi":212,"paper":213,"created_at":224},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":20,"substance":20,"depth":13,"authority":138,"freshness":84,"relevant":21,"comment":180},"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。",[182,183],{"name":175,"url":172},{"name":175,"url":184},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22111394",[26,27,28,29],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[188],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":190},{"candidates":191,"agricultural_evidence":51},[192,197,204],{"code":39,"score":19,"supported":51,"title":193,"abstract":194,"tags":196},[123],[195,123],"无人机多光谱",[29,28],{"code":65,"score":198,"supported":51,"title":199,"abstract":201,"tags":203},15,[200],"precision agriculture",[202,200],"精准农业",[26],{"code":32,"score":198,"supported":51,"title":205,"abstract":206,"tags":207},[56],[55,56],[27],[209,71],"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",2,"10.5281\u002Fzenodo.22111395",{"doi":212,"openalex_id":214,"authors":215,"venue":175,"cited_by_count":84,"oa_url":172,"card":218,"direction":223,"ingested_from":91},"W7204220081",[216],{"name":217,"orcid":9},"Adedayo Oluwaseyi Alawode",{"tldr":219,"method":220,"finding":221,"direction":89,"opportunity":222},"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":226,"title":227,"url":228,"summary":229,"summary_zh":230,"content":9,"source_name":231,"source_url":228,"published_at":232,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":233,"score_detail":234,"sources":237,"tags":239,"primary_topic":242,"secondary_topics":243,"classification":245,"search_phrases":262,"slug":265,"view_count":84,"doi":266,"paper":267,"created_at":277},5570,"Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545079","Counterfactual explanations are increasingly proposed as the interface through which cooperative storage managers interrogate computer-vision yield-forecast models, yet their reliability under the satellite cloud-mask gaps that routinely afflict agricultural remote sensing has not been bounded. The bound is derived from a Lipschitz continuity assumption on the forecast model and a masking-operator perturbation analysis, and it is expressed as a function of the cloud-mask gap fraction, the model's local Lipschitz constant, and the counterfactual search tolerance. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The reliability bound degrades non-linearly as the gap fraction rises, and that the intraclass correlation of 0.64 implies that cooperative-level aggregation of counterfactual recourse is necessary to preserve explanation stability. The contribution is a decision-relevant reliability bound that storage cooperatives can use to set a maximum tolerable cloud-mask gap before counterfactual recourse is suspended.","反事实解释日益被提出作为合作式仓储管理者质询计算机视觉产量预测模型的接口，然而在农业遥感中经常出现的卫星云掩膜缺口条件下，其可靠性尚未得到界定。该界基于预测模型上的Lipschitz连续性假设和掩膜算子扰动分析推导得出，并表示为云掩膜缺口比例、模型局部Lipschitz常数和反事实搜索容差的函数。对前后对照影响估计量进行了基于重复的评估，以表征该设计在给定方差结构（组间标准差=6.0，残差标准差=4.5，组内相关系数=0.64）下的运行特征，并在2000个设计单元中估计了检验效能。可靠性界随缺口比例上升呈非线性退化，且组内相关系数0.64意味着需要对反事实救济进行合作社层面的聚合，以保持解释的稳定性。本文的贡献在于提供了一个与决策相关的可靠性界，仓储合作社可据此设定在暂停反事实救济之前可容忍的最大云掩膜缺口。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",67,{"impact":20,"substance":120,"depth":235,"authority":17,"freshness":13,"relevant":21,"comment":236},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。",[238],{"name":231,"url":228},[26,27,240,28,241],"产量预测","可解释性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[244],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":246},{"candidates":247,"agricultural_evidence":51},[248,254,258],{"code":32,"score":17,"supported":51,"title":249,"abstract":251,"tags":253},[250],"computer vision",[252],"计算机视觉",[27],{"code":39,"score":20,"supported":51,"title":255,"abstract":256,"tags":257},[],[28,62],[240,28],{"code":65,"score":21,"supported":14,"title":259,"abstract":260,"tags":261},[],[],[26],[263,264],"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":266,"openalex_id":268,"authors":269,"venue":231,"cited_by_count":84,"oa_url":228,"card":272,"direction":89,"ingested_from":91},"W7223114002",[270],{"name":271,"orcid":9},"Kadiatou Barry",{"tldr":273,"method":274,"finding":275,"direction":89,"opportunity":276},"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":279,"title":280,"url":281,"summary":282,"summary_zh":283,"content":9,"source_name":284,"source_url":281,"published_at":285,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":286,"score_detail":287,"sources":292,"tags":294,"primary_topic":297,"secondary_topics":298,"classification":306,"search_phrases":332,"slug":335,"view_count":84,"doi":336,"paper":337,"created_at":357},5006,"Mapping Soil Salinity in Arid Agricultural Regions Using Multi-Source Remote Sensing and a Long-Range Dependency-Enhanced Deep Learning Framework","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101966","Soil salinity estimation across arid agricultural regions is challenging due to pronounced environmental heterogeneity and complex dependencies among multi-source variables. This study proposes a Long-Range Dependency-Enhanced Network (LRD-Net) for regional-scale soil salinity estimation in multiple agricultural oasis regions using multi-source remote sensing and environmental covariates. A dataset comprising 180 field samples from three agricultural oasis regions in Xinjiang, China, was constructed with 25 covariates derived from spectral, topographic, climatic, and soil property data. LRD-Net consists of three stages: a multi-scale feature fusion backbone based on depthwise separable convolutions and cross-scale interaction; a Long-Range Dependency Enhancement Module (LDEM) with an asymmetric dual-branch architecture that captures forward and reverse-aware dependencies through reverse alignment and cross-directional fusion; and multi-scale attention refinement with a regression head for continuous salinity estimation. Under the region-stratified random-split evaluation, LRD-Net achieved an R2 of 0.905, an RMSE of 1.207 g·kg−1, and an MAE of 0.868 g·kg−1. Performance decreased under more spatially independent validation, with an R2 of 0.874 under spatial cross-validation and LORO test R2 values ranging from 0.739 to 0.801, indicating that prediction across spatially and geographically independent samples is more challenging than region-stratified random-split evaluation. The proposed framework effectively integrates multi-source information and captures complementary long-range dependencies, providing a potential approach for regional-scale soil salinity estimation within the studied arid agricultural oasis environments and supporting soil salinization monitoring and sustainable land management in the study region.","干旱农业区域土壤盐分估算面临显著的环境异质性和多源变量间复杂依赖关系的挑战。本研究提出了一种长程依赖增强网络（LRD-Net），利用多源遥感与环境协变量对多个农业绿洲区域进行区域尺度土壤盐分估算。研究构建了包含中国新疆三个农业绿洲区域180个野外样本的数据集，并衍生出25个协变量，涵盖光谱、地形、气候和土壤属性数据。LRD-Net由三个阶段组成：基于深度可分离卷积和跨尺度交互的多尺度特征融合骨干网络；具有非对称双分支架构的长程依赖增强模块（LDEM），通过反向对齐和跨方向融合捕获前向与反向感知依赖关系；以及多尺度注意力细化与回归头，用于连续盐分估算。在区域分层随机划分评估下，LRD-Net取得了R²为0.905、RMSE为1.207 g·kg⁻¹、MAE为0.868 g·kg⁻¹的性能。在空间独立性更强的验证下性能有所下降，空间交叉验证R²为0.874，留一区域测试R²范围为0.739至0.801，表明跨空间和地理独立样本的预测比区域分层随机划分评估更具挑战性。所提出的框架有效整合了多源信息并捕获了互补的长程依赖关系，为研究区干旱农业绿洲环境下的区域尺度土壤盐分估算提供了潜在方法，并支持研究区土壤盐渍化监测与可持续土地管理。","Land","2026-10-14T00:00:00Z",72,{"impact":17,"substance":288,"depth":235,"authority":289,"freshness":290,"relevant":21,"comment":291},21,13,9,"方法新颖、数据扎实的遥感土壤盐渍化估算研究，对干旱区盐碱地监测与治理有参考价值，但属细分领域学术进展，公共影响有限。",[293],{"name":284,"url":281},[26,27,28,295,296],"土壤盐渍化","新疆农业",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[299,305],{"code":300,"slug":301,"name":302,"path":303,"parent_code":304},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":307},{"candidates":308,"agricultural_evidence":51},[309,313,318,322,328],{"code":39,"score":198,"supported":51,"title":310,"abstract":311,"tags":312},[62],[28,62],[28],{"code":300,"score":107,"supported":51,"title":314,"abstract":315,"tags":317},[301],[316,301],"土壤",[],{"code":32,"score":290,"supported":51,"title":319,"abstract":320,"tags":321},[57],[],[27],{"code":323,"score":73,"supported":51,"title":324,"abstract":325,"tags":327},"H7",[],[326],"干旱",[],{"code":65,"score":21,"supported":14,"title":329,"abstract":330,"tags":331},[],[],[26],[333,334],"新疆 农业绿洲 土壤盐渍化 遥感","LRD-Net 土壤盐分 估算","新疆农业绿洲土壤盐渍化遥感-5006","10.3390\u002Fland15101966",{"doi":336,"openalex_id":338,"authors":339,"venue":284,"cited_by_count":84,"oa_url":281,"card":352,"direction":89,"ingested_from":91},"W7221816183",[340,342,345,347,349],{"name":341,"orcid":9},"Mingjie Qian",{"name":343,"orcid":344},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":346,"orcid":9},"Peng Wang",{"name":348,"orcid":9},"Hangyuan Liu",{"name":350,"orcid":351},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":353,"method":354,"finding":355,"direction":89,"opportunity":356},"提出LRD-Net深度学习框架，融合多源遥感与环境协变量，估算新疆干旱绿洲区土壤盐分。","构建180个样点、25个协变量的数据集，设计含长程依赖增强模块的多尺度网络。","区域分层随机划分下R²达0.905，但空间独立验证R²降至0.739-0.874，跨区域预测更难。","可探索跨区域迁移学习与不确定性量化，提升空间独立场景下盐分制图泛化能力。","2026-10-15T23:30:29.667342Z",{"id":359,"title":360,"url":361,"summary":362,"summary_zh":363,"content":9,"source_name":364,"source_url":361,"published_at":285,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":365,"score_detail":366,"sources":368,"tags":370,"primary_topic":372,"secondary_topics":373,"classification":381,"search_phrases":427,"slug":430,"view_count":21,"doi":431,"paper":432,"created_at":454},4988,"Hierarchical cross-modal spatiotemporal attention network for crop yield prediction in complex Himalayan agroecosystems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1933033","Introduction Accurate crop yield prediction is essential for precision agriculture, climate-resilient farm management, and timely decision-making. However, yield prediction remains challenging because of complex, nonlinear, and multi-scale interactions among crop physiology, soil conditions, weather variability, and management practices. Conventional statistical, machine learning, and deep learning approaches, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, often have limitations in capturing long-range temporal dependencies, hierarchical spatial relationships, heterogeneous data modalities, and predictive uncertainty. Methods This study proposes a Hierarchical Cross-Modal Spatio-Temporal Attention Network (HCM-STAN) for crop yield prediction using a unified attention-based framework for integrating heterogeneous agro-environmental data. The architecture incorporates four principal components: (i) hierarchical spatio-temporal attention operating across tree, block, orchard, and regional scales; (ii) a cross-modal attention module for adaptive fusion of IoT-based soil sensor observations, weather data, plant physiological measurements, and drone-based multispectral imagery; (iii) a soil-aware attention head that explicitly models soil memory, hysteresis effects, and root-zone dynamics; and (iv) an uncertainty-aware prediction module combining Monte Carlo dropout and quantile regression to generate calibrated prediction intervals. The framework was evaluated using a multi-source dataset collected from Himalayan apple orchards during 2023–2026, comprising 1,247 monitored trees across three commercial orchards. Results The proposed HCM-STAN achieved an R 2 of 0.89 for four-week-ahead crop yield prediction, representing a 15.6% improvement over the CNN-LSTM baseline. Furthermore, zero-shot transfer to mango orchards achieved an R 2 of 0.67, corresponding to approximately 75% of the performance obtained under full training. Discussion The results demonstrate that integrating hierarchical spatio-temporal representation learning, cross-modal attention, soil-aware modelling, and uncertainty estimation can effectively address the heterogeneous and dynamic nature of crop yield prediction in complex mountain agroecosystems. The observed zero-shot transfer performance further indicates the potential of HCM-STAN to generalize across crop systems without crop-specific retraining. Overall, the proposed framework provides a promising foundation for reliable, uncertainty-aware yield forecasting and can support precision agriculture, climate-smart farm management, and data-driven decision-making in heterogeneous agroecosystems.","引言 准确的作物产量预测对于精准农业、气候适应性农场管理以及及时决策至关重要。然而，由于作物生理、土壤条件、天气变异和管理措施之间复杂、非线性且多尺度的相互作用，产量预测仍然具有挑战性。传统的统计方法、机器学习以及深度学习方法，包括卷积神经网络（CNN）、循环神经网络（RNN）和长短期记忆（LSTM）网络，在捕捉长程时间依赖关系、层次化空间关系、异构数据模态以及预测不确定性方面往往存在局限。方法 本研究提出了一种用于作物产量预测的层次化跨模态时空注意力网络（HCM-STAN），采用统一的基于注意力的框架来整合异构农业环境数据。该架构包含四个主要组成部分：（i）在树、地块、果园和区域尺度上运行的层次化时空注意力；（ii）用于自适应融合基于物联网的土壤传感器观测、天气数据、植物生理测量和无人机多光谱影像的跨模态注意力模块；（iii）显式建模土壤记忆、滞后效应和根区动态的土壤感知注意力头；（iv）结合蒙特卡洛 dropout 和分位数回归以生成校准预测区间的不确定性感知预测模块。该框架使用2023—2026年在喜马拉雅苹果园采集的多源数据集进行了评估，该数据集包含三个商业果园中1，247棵监测树木。结果 所提出的 HCM-STAN 在提前四周作物产量预测中达到了0.89的 R²，相较于 CNN-LSTM 基线提升了15.6%。此外，零样本迁移至芒果园达到了0.67的 R²，相当于完整训练下所获性能的约75%。讨论 结果表明，整合层次化时空表征学习、跨模态注意力、土壤感知建模和不确定性估计，能够有效应对复杂山地农业生态系统中作物产量预测的异构性和动态性。观察到的零样本迁移性能进一步表明，HCM-STAN 具有在无需作物特定再训练的情况下跨作物系统泛化的潜力。总体而言，该 p","Frontiers in Plant Science",81,{"impact":18,"substance":106,"depth":18,"authority":107,"freshness":290,"relevant":21,"comment":367},"提出分层跨模态时空注意力网络，融合物联网土壤传感、气象、生理与无人机多光谱数据，苹果园四周产量预测R²达0.89并实现芒果园零样本迁移，方法新颖、数据扎实，对智慧农业产量预报有参考价值。",[369],{"name":364,"url":361},[26,27,240,371,28],"多模态融合",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[374,380],{"code":375,"slug":376,"name":377,"path":378,"parent_code":379},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":382},{"candidates":383,"agricultural_evidence":51},[384,388,396,400,405,409,416,422],{"code":32,"score":50,"supported":51,"title":385,"abstract":386,"tags":387},[],[54,55,56,57],[27,371],{"code":375,"score":290,"supported":51,"title":389,"abstract":390,"tags":395},[],[391,392,393,394],"苹果","芒果","orchard","apple",[],{"code":39,"score":20,"supported":51,"title":397,"abstract":398,"tags":399},[],[195,123],[240,28],{"code":65,"score":401,"supported":51,"title":402,"abstract":403,"tags":404},7,[],[202,200],[26],{"code":300,"score":94,"supported":51,"title":406,"abstract":407,"tags":408},[],[316,301],[],{"code":410,"score":94,"supported":51,"title":411,"abstract":412,"tags":415},"I5",[],[413,414],"物联网","IoT",[],{"code":417,"score":73,"supported":51,"title":418,"abstract":419,"tags":421},"H6",[],[420],"农业生态",[],{"code":323,"score":73,"supported":51,"title":423,"abstract":424,"tags":426},[],[425],"气候适应",[],[428,429],"喜马拉雅 苹果园 产量预测","HCM-STAN 作物产量预测","喜马拉雅苹果园产量预测-4988","10.3389\u002Ffpls.2026.1933033",{"doi":431,"openalex_id":433,"authors":434,"venue":364,"cited_by_count":84,"oa_url":361,"card":448,"direction":453,"ingested_from":91},"W7221795850",[435,438,440,442,444,446],{"name":436,"orcid":437},"Shabir Ahmad Mir","https:\u002F\u002Forcid.org\u002F0000-0003-2311-218X",{"name":439,"orcid":9},"Umarfarooque Momin",{"name":441,"orcid":9},"Sudeep Kumar E",{"name":443,"orcid":9},"Itfaq Ahmad Mir",{"name":445,"orcid":9},"Ab Rouf Khan",{"name":447,"orcid":9},"Ghulam Mohammad Mir",{"tldr":449,"method":450,"finding":451,"direction":223,"opportunity":452},"提出分层跨模态时空注意力网络HCM-STAN，融合多源农业数据预测喜马拉雅山区苹果产量。","用IoT土壤、气象、生理和无人机多光谱数据，结合分层时空与跨模态注意力及不确定性","四周前产量预测R²达0.89，比CNN-LSTM提升15.6%，零样本迁移芒果R²为0.67。","可探索跨作物零样本迁移的领域自适应机制，并验证模型在更多山地农业生态系统的泛化能力。","智慧农业 \u002F 农业物联网","2026-10-15T23:30:08.750140Z",{"id":456,"title":457,"url":458,"summary":459,"summary_zh":460,"content":9,"source_name":231,"source_url":458,"published_at":461,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":462,"score_detail":463,"sources":465,"tags":467,"primary_topic":470,"secondary_topics":471,"classification":473,"search_phrases":501,"slug":504,"view_count":84,"doi":505,"paper":506,"created_at":527},4505,"Application of Deep Learning Techniques for Vineyard Mapping and Characterization Using Multispectral UAV Imagery: A Case Study from Northern Greece","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23232412","Developments in unmanned aerial vehicle (UAV) technology and the rise of Deep Learning (DL) have drastically boosted the capacities of precision viticulture to monitor the environments of farms in a scalable and detailed manner. Vineyard mappings and extraction of vine-specific features are crucial for yield estimation, plant health assessment, and management decisions. This study examines the application of Deep Learning-based image analysis techniques for vineyard mapping and characterization using multispectral UAV image data (Deep Learning). The proposed approach integrates spatial and spectral information to delineate vineyard structures and capture key canopy characteristics relevant to precision agriculture. Multimodal datasets, including RGB and multispectral bands, facilitate improved discrimination between vines and surrounding elements. Geospatial analysis and data processing are further performed through ArcGIS Pro, enabling spatial validation of results, identifying extracted features, and creating appropriate interpretative thematic layers. The combination of UAV imagery, DL tools and GIS based analysis offers a strong and flexible workflow that can be adjusted for different grape cultivars, growth conditions and circumstances. Overall, the study demonstrates the potential benefits of the rigid union of UAV-based remote sensing with Deep Learning and GIS techniques to create a flexible, operational, and data-driven framework for vineyard management. Based on this, sustainable and fruitful management practices can be elaborated, thus optimizing resources and vineyard management decisions in precision viticulture,underlining the importance of such a technological integration for monitoring and managing modern vineyards.","无人机（UAV）技术的发展与深度学习（DL）的兴起，极大地提升了精准葡萄栽培以可扩展且精细的方式监测农场环境的能力。葡萄园制图与葡萄植株特异性特征的提取对于产量估算、植株健康评估和管理决策至关重要。本研究探讨了基于深度学习的图像分析技术在多光谱无人机影像数据（深度学习）用于葡萄园制图与表征中的应用。所提出的方法整合了空间与光谱信息，以 delineate 葡萄园结构并捕捉与精准农业相关的关键冠层特征。多模态数据集（包括RGB和多光谱波段）有助于更好地区分葡萄藤与周围元素。通过ArcGIS Pro进一步进行地理空间分析与数据处理，从而实现结果的空间验证、提取特征的识别以及适当的解释性专题图层的创建。无人机影像、深度学习工具与基于GIS的分析相结合，提供了一套强大且灵活的工作流程，可针对不同葡萄品种、生长条件和环境进行调整。总体而言，本研究展示了将基于无人机的遥感与深度学习和GIS技术 rigidly 结合，以创建灵活、可操作且数据驱动的葡萄园管理框架的潜在优势。在此基础上，可制定可持续且富有成效的管理实践，从而优化精准葡萄栽培中的资源与葡萄园管理决策，凸显了这种技术整合对现代葡萄园监测与管理的重要性。","2026-10-08T00:00:00Z",68,{"impact":17,"substance":18,"depth":19,"authority":289,"freshness":290,"relevant":21,"comment":464},"希腊北部葡萄园的多光谱无人机深度学习制图案例，方法新颖但属区域性研究，对国内智慧农业有参考价值。",[466],{"name":231,"url":458},[26,468,27,28,469],"无人机","葡萄园管理",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[472],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":474},{"candidates":475,"agricultural_evidence":51},[476,480,484,488,494],{"code":39,"score":18,"supported":51,"title":477,"abstract":478,"tags":479},[123],[28,62,123],[28],{"code":32,"score":198,"supported":51,"title":481,"abstract":482,"tags":483},[57],[54,57],[27],{"code":65,"score":401,"supported":51,"title":485,"abstract":486,"tags":487},[],[202,200],[26],{"code":375,"score":94,"supported":51,"title":489,"abstract":490,"tags":493},[],[491,492],"葡萄","grape",[],{"code":495,"score":94,"supported":51,"title":496,"abstract":497,"tags":500},"I4",[],[498,499],"空间分析","GIS",[],[502,503],"希腊 葡萄园 无人机 深度学习","多光谱 UAV 葡萄园 制图","希腊葡萄园无人机深度学习-4505","10.5281\u002Fzenodo.23232412",{"doi":505,"openalex_id":507,"authors":508,"venue":231,"cited_by_count":84,"oa_url":458,"card":522,"direction":89,"ingested_from":91},"W7220993218",[509,511,514,516,519],{"name":510,"orcid":9},"Dimitra Passa",{"name":512,"orcid":513},"Spyridon E. Detsikas","https:\u002F\u002Forcid.org\u002F0000-0002-3960-9361",{"name":515,"orcid":9},"Georgios Nektarios Tselos",{"name":517,"orcid":518},"George P. Petropoulos","https:\u002F\u002Forcid.org\u002F0000-0003-1442-1423",{"name":520,"orcid":521},"Antonis Litke","https:\u002F\u002Forcid.org\u002F0000-0002-7658-2559",{"tldr":523,"method":524,"finding":525,"direction":89,"opportunity":526},"用多光谱无人机影像和深度学习实现葡萄园制图与冠层特征提取。","多光谱无人机影像、RGB与多光谱融合、深度学习、ArcGIS Pro空间分析。","UAV+DL+GIS可灵活构建葡萄园精准管理的数据驱动框架。","可探索跨品种、跨生长阶段的迁移学习与轻量化模型，提升葡萄园实时监测泛化能力。","2026-10-09T23:30:25.912664Z"]