[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2855":3,"related-2855":79},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":8,"secondary_topics":31,"classification":32,"search_phrases":63,"slug":66,"view_count":67,"doi":8,"paper":68,"created_at":78},2855,"UAV无人机高光谱图像土壤盐度制图(湿度校正)——MDPI Agronomy","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1812","研究评估了6种光谱变换方法(原始反射率Ref、一阶导数FDR、PDS、OSC、FDR+PDS、FDR+OSC),结合3种机器学习算法(KNN、SVR、MLP)。进一步开发了集成这些基础学习者的Stacking集成模型,以提高湿度干扰下土壤盐度反演的精度。结果表明,Stacking模型在评估模型中达到最高的精度和稳定性。FDR+OSC-Stacking组合实现最佳验证性能,R²p=0.87,RMSEP=0.67 mS·cm⁻¹,RPD=2.93。FDR+OSC-Stacking组合成功应用于UAV高光谱图像,用于EC1:5的空间制图。来自吉林大学。",null,"MDPI Agronomy","2026-09-15T00:00:00Z","论文",10,false,75,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,21,17,13,8,1,"方法组合新颖、验证指标扎实的无人机高光谱盐分制图研究，属细分领域实质进展，值得精选。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","无人机","机器学习","遥感","土壤盐渍化",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":13,"reason":62},[38,46,52,57],{"code":39,"score":40,"supported":41,"title":42,"abstract":44,"tags":45},"H2",11,true,[43],"土壤",[43],[],{"code":47,"score":48,"supported":41,"title":49,"abstract":50,"tags":51},"I2",4,[],[28],[28],{"code":53,"score":21,"supported":13,"title":54,"abstract":55,"tags":56},"I1",[],[],[26],{"code":58,"score":21,"supported":13,"title":59,"abstract":60,"tags":61},"I3",[],[],[29],"农业相关性需要复核",[64,65],"土壤盐渍化 智慧农业 机器学习 无人机","土壤盐渍化 智慧农业","土壤盐渍化智慧农业机器学习无人机-2855",2,{"doi":8,"openalex_id":8,"authors":69,"venue":8,"cited_by_count":70,"oa_url":8,"card":71,"direction":75,"ingested_from":77},[],0,{"tldr":72,"method":73,"finding":74,"direction":75,"opportunity":76},"用无人机高光谱结合Stacking集成模型实现湿度干扰下的土壤盐度制图。","6种光谱变换与KNN、SVR、MLP及Stacking集成，基于UAV高光谱数据","FDR+OSC-Stacking最优，R²p=0.87、RMSEP=0.67 mS·cm⁻¹、RPD","农业遥感与作物表型","可探索多时相\u002F多传感器融合与迁移学习，提升不同湿度与区域下盐度反演泛化性。","agent","2026-09-18T00:03:30.822732Z",{"total":80,"page":21,"page_size":80,"items":81},6,[82,191,273,329,404,460],{"id":83,"title":84,"url":85,"summary":86,"summary_zh":87,"content":8,"source_name":88,"source_url":85,"published_at":89,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":90,"score_detail":91,"sources":97,"tags":99,"primary_topic":102,"secondary_topics":108,"classification":114,"search_phrases":142,"slug":145,"view_count":70,"doi":146,"paper":147,"created_at":190},5567,"Capturing Spatial Heterogeneity Using UAV-Derived Multispectral Phenotypes and Random Regression Models Improves Genetic Evaluation in Alfalfa Breeding Trials","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.10.14.759249","Spatial heterogeneity is a major source of environmental noise in field trials, reducing genetic evaluation accuracy. Conventional spatial correction methods use field layout and plot proximity but often fail to capture in-season dynamics or leverage temporal data from high-throughput phenotyping. We hypothesized that longitudinal UAV-derived multispectral measurements could characterize field-scale environmental heterogeneity and improve genetic evaluation in breeding trials. Multispectral imagery was collected throughout the growing season across eight alfalfa trials in New York and New Mexico (20222023). Vegetation indices were evaluated for heritability and association with biomass yield using univariate and multi-trait models. Longitudinal measurements were modeled with random regression (RR) to estimate plot-specific permanent environmental effects (PEE), incorporated into genomic mixed models within a two-stage framework. Model performance was compared with conventional autoregressive (AR) and baseline (no correction) models using metrics of consistency and accuracy of estimated genetic effects across replicates. Vegetation indices showed moderate to high heritability, with GNDVI consistently outperforming NDVI and NDRE. Both univariate and multi-trait analyses revealed substantial spatial variability, particularly in New Mexico trials. RR-derived PEE captured field-scale spatial patterns and improved genetic signal recovery relative to baseline and AR models, with the largest gains under strong spatial heterogeneity and comparable performance where variability was limited. The RR framework improved reproducibility of genetic effect estimates across independent field subsets without compromising agreement with full-trial evaluations. These findings show that integrating UAV data via random regression enhances genetic signal recovery and selection accuracy, with broader potential for precision agriculture and biosensor-driven crop management.","空间异质性是田间试验中环境噪声的主要来源，会降低遗传评估的准确性。传统空间校正方法利用田间布局和小区邻近性，但往往无法捕捉生育期内的动态变化，也未能充分利用高通量表型分析产生的时间序列数据。我们假设，基于无人机（UAV）的纵向多光谱测量能够表征田块尺度的环境异质性，并改善育种试验中的遗传评估。在2022—2023年纽约州和新墨西哥州的8个紫花苜蓿试验中，于整个生长季收集了多光谱影像。采用单性状和多性状模型评估了植被指数的遗传力及其与生物量产量的关联。纵向测量数据采用随机回归（RR）建模，以估计小区特异性永久环境效应（PEE），并在两阶段框架中将其纳入基因组混合模型。利用重复间估计遗传效应的一致性和准确性指标，将模型性能与传统的自回归（AR）模型和基线（无校正）模型进行比较。植被指数表现出中到高的遗传力，其中GNDVI始终优于NDVI和NDRE。单性状和多性状分析均揭示了显著的空间变异，尤其在新墨西哥州的试验中更为明显。RR衍生的PEE捕捉到了田块尺度的空间格局，相对于基线模型和AR模型改善了遗传信号的恢复，在强空间异质性条件下增益最大，而在变异有限时性能相当。RR框架提高了遗传效应估计在独立田间子集间的可重复性，同时未损害与全试验评估的一致性。这些结果表明，通过随机回归整合UAV数据可增强遗传信号恢复和选择准确性，在精准农业和生物传感器驱动的作物管理方面具有更广泛的潜力。","bioRxiv (Cold Spring Harbor Laboratory)","2026-10-20T00:00:00Z",79,{"impact":92,"substance":93,"depth":92,"authority":94,"freshness":95,"relevant":21,"comment":96},18,22,12,9,"该研究利用无人机多光谱表型与随机回归模型校正田间空间异质性，显著提升苜蓿育种遗传评估精度，方法新颖、数据覆盖两州八组试验，对智慧育种有实质参考价值。",[98],{"name":88,"url":85},[26,27,100,29,101],"育种","紫花苜蓿",{"code":103,"slug":104,"name":105,"path":106,"parent_code":107},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[109],{"code":58,"slug":110,"name":111,"path":112,"parent_code":113},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":115,"locked":13,"version":34,"method":35,"evidence":116},"classified",{"candidates":117,"agricultural_evidence":41},[118,125,130,137],{"code":103,"score":119,"supported":41,"title":120,"abstract":122,"tags":124},15,[121],"alfalfa",[123,121],"苜蓿",[101],{"code":58,"score":94,"supported":41,"title":126,"abstract":128,"tags":129},[127],"multispectral",[127],[29],{"code":53,"score":131,"supported":41,"title":132,"abstract":133,"tags":136},7,[],[134,135],"精准农业","precision agriculture",[26],{"code":138,"score":48,"supported":41,"title":139,"abstract":140,"tags":141},"C4",[],[123],[101],[143,144],"无人机 多光谱 苜蓿 育种","随机回归 空间异质性 遗传评估","无人机多光谱苜蓿育种-5567","10.64898\u002F2026.10.14.759249",{"doi":146,"openalex_id":148,"authors":149,"venue":88,"cited_by_count":70,"oa_url":85,"card":184,"direction":75,"ingested_from":189},"W7223083966",[150,153,156,159,162,164,166,169,172,175,178,181],{"name":151,"orcid":152},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":154,"orcid":155},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":157,"orcid":158},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":160,"orcid":161},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":163,"orcid":8},"Julie Hansen",{"name":165,"orcid":8},"Christopher Pierce",{"name":167,"orcid":168},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":170,"orcid":171},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":173,"orcid":174},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":176,"orcid":177},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":179,"orcid":180},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":182,"orcid":183},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":185,"method":186,"finding":187,"direction":75,"opportunity":188},"用无人机多光谱时序表型和随机回归模型校正田间空间异质性，提升苜蓿育种遗传评估精度。","八项苜蓿试验的无人机多光谱植被指数，随机回归估计小区永久环境效应并嵌入基因组混合","随机回归提取的环境效应能捕捉田块尺度空间格局，在强异质性下显著提升遗传信号恢复与选择准确性。","可将无人机时序表型与随机回归框架推广到其他作物育种试验，并探索多源遥感与基因组选择的联合建模。","openalex","2026-10-22T11:20:32.655394Z",{"id":192,"title":193,"url":194,"summary":195,"summary_zh":196,"content":8,"source_name":197,"source_url":194,"published_at":198,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":199,"score_detail":200,"sources":202,"tags":204,"primary_topic":207,"secondary_topics":208,"classification":218,"search_phrases":247,"slug":250,"view_count":70,"doi":251,"paper":252,"created_at":272},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":94,"substance":17,"depth":18,"authority":19,"freshness":95,"relevant":21,"comment":201},"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。",[203],{"name":197,"url":194},[26,205,29,30,206],"农业人工智能","新疆农业",{"code":58,"slug":110,"name":111,"path":112,"parent_code":113},[209,214],{"code":39,"slug":210,"name":211,"path":212,"parent_code":213},"soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":47,"slug":215,"name":216,"path":217,"parent_code":113},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":115,"locked":13,"version":34,"method":35,"evidence":219},{"candidates":220,"agricultural_evidence":41},[221,226,231,236,243],{"code":58,"score":119,"supported":41,"title":222,"abstract":224,"tags":225},[223],"remote sensing",[29,223],[29],{"code":39,"score":227,"supported":41,"title":228,"abstract":229,"tags":230},14,[210],[43,210],[],{"code":47,"score":95,"supported":41,"title":232,"abstract":234,"tags":235},[233],"deep learning",[],[205],{"code":237,"score":238,"supported":41,"title":239,"abstract":240,"tags":242},"H7",3,[],[241],"干旱",[],{"code":53,"score":21,"supported":13,"title":244,"abstract":245,"tags":246},[],[],[26],[248,249],"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":251,"openalex_id":253,"authors":254,"venue":197,"cited_by_count":70,"oa_url":194,"card":267,"direction":75,"ingested_from":189},"W7221816183",[255,257,260,262,264],{"name":256,"orcid":8},"Mingjie Qian",{"name":258,"orcid":259},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":261,"orcid":8},"Peng Wang",{"name":263,"orcid":8},"Hangyuan Liu",{"name":265,"orcid":266},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":268,"method":269,"finding":270,"direction":75,"opportunity":271},"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":274,"title":275,"url":276,"summary":277,"summary_zh":8,"content":8,"source_name":278,"source_url":276,"published_at":198,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":279,"sources":282,"tags":284,"primary_topic":286,"secondary_topics":290,"classification":291,"search_phrases":311,"slug":314,"view_count":21,"doi":315,"paper":316,"created_at":328},4973,"Barley head detection using UAV imagery and YOLOv10","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10468-w","Barley head detection using UAV imagery and YOLOv10。Precision Agriculture","Precision Agriculture",{"impact":16,"substance":280,"depth":18,"authority":227,"freshness":20,"relevant":21,"comment":281},20,"将YOLOv10用于无人机大麦穗检测，方法新颖、数据可靠，属细分领域技术进展，值得精选。",[283],{"name":278,"url":276},[26,27,205,29,285],"大麦",{"code":53,"slug":287,"name":288,"path":289,"parent_code":113},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":115,"locked":13,"version":34,"method":35,"evidence":292},{"candidates":293,"agricultural_evidence":41},[294,298,303,307],{"code":53,"score":48,"supported":41,"title":295,"abstract":296,"tags":297},[],[135],[26],{"code":299,"score":21,"supported":13,"title":300,"abstract":301,"tags":302},"A5",[],[],[285],{"code":47,"score":21,"supported":13,"title":304,"abstract":305,"tags":306},[],[],[205],{"code":58,"score":21,"supported":13,"title":308,"abstract":309,"tags":310},[],[],[29],[312,313],"无人机 大麦 穗检测","YOLOv10 大麦 遥感","无人机大麦穗检测-4973","10.1007\u002Fs11119-026-10468-w",{"doi":315,"openalex_id":317,"authors":318,"venue":278,"cited_by_count":70,"oa_url":8,"card":8,"direction":8,"ingested_from":189},"W7221758985",[319,322,325],{"name":320,"orcid":321},"Ameer Tamoor Khan","https:\u002F\u002Forcid.org\u002F0000-0001-6838-992X",{"name":323,"orcid":324},"Saiful Azim","https:\u002F\u002Forcid.org\u002F0000-0002-2686-7150",{"name":326,"orcid":327},"Signe Marie Jensen","https:\u002F\u002Forcid.org\u002F0000-0002-7738-5231","2026-10-15T23:30:02.421798Z",{"id":330,"title":331,"url":332,"summary":333,"summary_zh":334,"content":8,"source_name":335,"source_url":332,"published_at":336,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":337,"score_detail":338,"sources":340,"tags":342,"primary_topic":344,"secondary_topics":345,"classification":347,"search_phrases":377,"slug":380,"view_count":70,"doi":381,"paper":382,"created_at":403},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-08T00:00:00Z",68,{"impact":94,"substance":92,"depth":16,"authority":19,"freshness":95,"relevant":21,"comment":339},"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。",[341],{"name":335,"url":332},[26,27,205,29,343],"葡萄园管理",{"code":58,"slug":110,"name":111,"path":112,"parent_code":113},[346],{"code":47,"slug":215,"name":216,"path":217,"parent_code":113},{"status":115,"locked":13,"version":34,"method":35,"evidence":348},{"candidates":349,"agricultural_evidence":41},[350,354,359,363,370],{"code":58,"score":92,"supported":41,"title":351,"abstract":352,"tags":353},[127],[29,223,127],[29],{"code":47,"score":119,"supported":41,"title":355,"abstract":356,"tags":358},[233],[357,233],"深度学习",[205],{"code":53,"score":131,"supported":41,"title":360,"abstract":361,"tags":362},[],[134,135],[26],{"code":364,"score":80,"supported":41,"title":365,"abstract":366,"tags":369},"B2",[],[367,368],"葡萄","grape",[],{"code":371,"score":80,"supported":41,"title":372,"abstract":373,"tags":376},"I4",[],[374,375],"空间分析","GIS",[],[378,379],"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":381,"openalex_id":383,"authors":384,"venue":335,"cited_by_count":70,"oa_url":332,"card":398,"direction":75,"ingested_from":189},"W7220993218",[385,387,390,392,395],{"name":386,"orcid":8},"Dimitra Passa",{"name":388,"orcid":389},"Spyridon E. Detsikas","https:\u002F\u002Forcid.org\u002F0000-0002-3960-9361",{"name":391,"orcid":8},"Georgios Nektarios Tselos",{"name":393,"orcid":394},"George P. Petropoulos","https:\u002F\u002Forcid.org\u002F0000-0003-1442-1423",{"name":396,"orcid":397},"Antonis Litke","https:\u002F\u002Forcid.org\u002F0000-0002-7658-2559",{"tldr":399,"method":400,"finding":401,"direction":75,"opportunity":402},"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",{"id":405,"title":406,"url":407,"summary":408,"summary_zh":8,"content":8,"source_name":409,"source_url":8,"published_at":8,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":410,"score_detail":411,"sources":413,"tags":415,"primary_topic":418,"secondary_topics":422,"classification":423,"search_phrases":447,"slug":450,"view_count":67,"doi":8,"paper":451,"created_at":459},4477,"玉米生产精准农业系统综述：技术、应用与产量优化（2020—2026）","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37500368","依据PRISMA 2020指南，系统检索Scopus与Web of Science共464篇文献，最终纳入129项研究；样本构成为田间试验（28.6%）、遥感与精准农业整合研究（25.5%）、机器学习应用（19.4%）、气候知情精准农业策略（23.4%）和土壤退化研究（3.1%）；研究表明精准农业技术通过定点管理和数据驱动决策支持，提升了玉米生产力、养分利用效率、水利用效率和产量预测准确性；指出采用精准农业仍受制于实施成本高、技术复杂、数据有限等因素。","Precision Agriculture in Maize Production Systematic Review 2026",74,{"impact":92,"substance":93,"depth":92,"authority":19,"freshness":238,"relevant":21,"comment":412},"基于PRISMA 2020的系统综述，纳入129项研究，数据规模与方法规范性强，对玉米精准农业技术路径与瓶颈有实质参考价值，但发布日期不明、时效性偏弱。",[414],{"name":409,"url":407},[26,416,28,417,134,29],"产量预测","玉米",{"code":419,"slug":420,"name":417,"path":421,"parent_code":107},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize",[],{"status":115,"locked":13,"version":34,"method":35,"evidence":424},{"candidates":425,"agricultural_evidence":41},[426,430,434,439,443],{"code":53,"score":19,"supported":41,"title":427,"abstract":428,"tags":429},[134],[134],[26,134],{"code":419,"score":94,"supported":41,"title":431,"abstract":432,"tags":433},[417],[417],[417],{"code":58,"score":435,"supported":41,"title":436,"abstract":437,"tags":438},5,[],[29],[416,29],{"code":47,"score":48,"supported":41,"title":440,"abstract":441,"tags":442},[],[28],[28],{"code":39,"score":238,"supported":41,"title":444,"abstract":445,"tags":446},[],[43],[],[448,449],"玉米 精准农业 系统综述","玉米 遥感 机器学习 产量","玉米精准农业系统综述-4477",{"doi":8,"openalex_id":8,"authors":452,"venue":8,"cited_by_count":70,"oa_url":8,"card":453,"direction":457,"ingested_from":77},[],{"tldr":454,"method":455,"finding":456,"direction":457,"opportunity":458},"系统综述2020—2026年玉米精准农业技术、应用与产量优化研究。","按PRISMA 2020检索Scopus与WoS，纳入129项研究做分类分析。","精准农业通过定点管理与数据决策提升玉米产量、养分和水利用效率。","智慧农业 \u002F 农业物联网","成本高、技术复杂与数据不足制约推广，低成本轻量化方案与本地化数据共享是研究空白。","2026-10-09T00:05:02.451957Z",{"id":461,"title":462,"url":463,"summary":464,"summary_zh":465,"content":8,"source_name":466,"source_url":463,"published_at":467,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":468,"score_detail":469,"sources":471,"tags":473,"primary_topic":8,"secondary_topics":476,"classification":477,"search_phrases":508,"slug":511,"view_count":70,"doi":512,"paper":513,"created_at":534},4311,"Cross-Regional Digital Soil Mapping of OpenLandMap-Derived USDA Soil Texture Classes Using Remote Sensing and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16192147","Soil is a fundamental environmental resource that supports food production and sustainable agricultural systems. Soil’s texture is a key soil property that affects its capacity to hold water, infiltration rate, fertility, erosion risk, and crop productivity. Traditionally, it is determined using hydrometer or pipette analysis. These laboratory procedures are costly and time-consuming, typically requiring approximately two working days, which limits their practicality for large-scale assessments. This paper proposes a cross-regional digital soil texture classification system based on remote sensing data and machine learning in Malaysia and Pakistan. The two countries were chosen because they represent contrasting environmental conditions: Malaysia has humid tropical conditions, and Pakistan has arid and semi-arid conditions. The OpenLandMap United States Department of Agriculture (USDA) soil texture classes served as the reference labels, and the spectral bands from Landsat, together with spectral indices and Shuttle Radar Topography Mission (SRTM) elevation, were used as predictor variables. The three ensemble models, including Random Forest (RF), XGBoost, and CatBoost, were evaluated for multi-class classification of USDA soil texture. The main Landsat-based Malaysia–Pakistan dataset produced the best results, with RF achieving the highest accuracy of 71.45%, XGBoost 71.31%, and CatBoost 67.33%. Additional experiments using 2025 Landsat 8\u002F9 and Sentinel-2 imagery achieved lower accuracies of 63.00% and 56.10%, respectively, while five-fold spatial block cross-validation produced a mean accuracy of 59.27 ± 1.84%. To strengthen dataset reliability, representative OpenLandMap-derived soil texture classes were externally validated using PCRWR sand–silt–clay fractions plotted on the USDA soil texture triangle and independent Malaysian soil resource references. The results of the feature importance analysis showed that elevation, Bare Soil Index (BSI), Normalized Difference Water Index (NDWI), and Normalized Difference Vegetation Index (NDVI) were important predictors for classifying OpenLandMap-derived USDA soil texture classes. These show that the Landsat-based spectral and terrain features, when used in conjunction with ensemble learning, could assist cross-regional soil texture classification for the USDA. However, class imbalance, spectral overlap between soil texture classes, temporal variability, and resolution mismatch between satellite predictors and soil labels remain important challenges. The findings of this study provide a useful foundation for large-scale soil texture mapping and future spatial validation across different environmental regions.","土壤是支撑粮食生产和可持续农业系统的基础环境资源。土壤质地是影响土壤持水能力、入渗速率、肥力、侵蚀风险和作物生产力的关键土壤属性。传统上，土壤质地采用比重计法或吸管法测定。这些实验室方法成本高、耗时长，通常需要约两个工作日，限制了其在大规模评估中的实用性。本文提出了一种基于遥感数据和机器学习的跨区域数字土壤质地分类系统，研究区域为马来西亚和巴基斯坦。选择这两个国家是因为它们代表了截然不同的环境条件：马来西亚属于湿润热带条件，巴基斯坦属于干旱和半干旱条件。以OpenLandMap提供的美国农业部（USDA）土壤质地类别作为参考标签，以Landsat光谱波段、光谱指数和航天飞机雷达地形测绘任务（SRTM）高程数据作为预测变量。评估了三种集成模型，包括随机森林（RF）、XGBoost和CatBoost，用于USDA土壤质地的多类分类。基于Landsat的马来西亚—巴基斯坦主数据集取得了最佳结果，RF达到最高精度71.45%，XGBoost为71.31%，CatBoost为67.33%。使用2025年Landsat 8\u002F9和Sentinel-2影像的额外实验分别取得了较低的精度，为63.00%和56.10%，而五折空间块交叉验证产生的平均精度为59.27 ± 1.84%。为增强数据集可靠性，使用PCRWR砂—粉—黏粒比例绘制在USDA土壤质地三角图上，并结合独立的马来西亚土壤资源参考资料，对具有代表性的OpenLandMap衍生土壤质地类别进行了外部验证。特征重要性分析结果表明，高程、裸土指数（BSI）、归一化差异水体指数（NDWI）和归一化差异植被指数（NDVI）是分类OpenLandMap衍生USDA土壤质地类别的重要预测因子。这些结果表明，基于Landsat的光谱和地形特征与集成学习结合使用时，可辅助USDA的跨区域土壤质地分类。然而，类别不平衡、土壤质地类别之间的光谱重叠、时间变异性以及卫星预测因子与s之间的分辨率不匹配","Agriculture","2026-10-05T00:00:00Z",77,{"impact":16,"substance":17,"depth":92,"authority":19,"freshness":95,"relevant":21,"comment":470},"跨区域遥感与集成学习土壤质地分类研究，方法扎实、结论有参考价值，但精度有限，属细分领域进展。",[472],{"name":466,"url":463},[474,26,28,29,475],"数字农业","土壤制图",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":478},{"candidates":479,"agricultural_evidence":41,"reason":507},[480,484,489,493,497,503],{"code":39,"score":119,"supported":41,"title":481,"abstract":482,"tags":483},[210],[43,210],[475],{"code":47,"score":119,"supported":41,"title":485,"abstract":487,"tags":488},[486],"machine learning",[28,486],[28],{"code":58,"score":119,"supported":41,"title":490,"abstract":491,"tags":492},[223],[29,223],[29],{"code":237,"score":238,"supported":41,"title":494,"abstract":495,"tags":496},[],[241],[],{"code":498,"score":238,"supported":41,"title":499,"abstract":500,"tags":502},"H8",[],[501],"可持续农业",[],{"code":53,"score":67,"supported":13,"title":504,"abstract":505,"tags":506},[],[],[474,26],"多个产业证据相当，需要确认主归属",[509,510],"OpenLandMap USDA 土壤质地","Landsat 土壤质地 机器学习","OpenLandMapUSDA土壤质地-4311","10.3390\u002Fagriculture16192147",{"doi":512,"openalex_id":514,"authors":515,"venue":466,"cited_by_count":70,"oa_url":463,"card":529,"direction":75,"ingested_from":189},"W7220411242",[516,518,520,523,526],{"name":517,"orcid":8},"Umair Maqsood",{"name":519,"orcid":8},"Fatima Iftikhar",{"name":521,"orcid":522},"Kashif Sattar","https:\u002F\u002Forcid.org\u002F0000-0001-8620-9419",{"name":524,"orcid":525},"Iftikhar A. Ahmad","https:\u002F\u002Forcid.org\u002F0000-0003-3719-2387",{"name":527,"orcid":528},"Madini Obad Alassafi","https:\u002F\u002Forcid.org\u002F0000-0001-9919-8368",{"tldr":530,"method":531,"finding":532,"direction":75,"opportunity":533},"用Landsat光谱与地形特征结合集成学习，实现马来西亚与巴基斯坦跨区域USDA土壤质地分类。","Landsat光谱波段、光谱指数与SRTM高程，RF\u002FXGBoost\u002FCatBo","RF精度最高71.45%，高程、BSI、NDWI、NDVI为关键预测因子，跨区域可行但精度受限。","可探索多源遥感融合与类别不平衡校正，提升跨气候区土壤质地制图精度与迁移能力。","2026-10-06T23:30:20.282911Z"]