[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3900":3,"related-3900":86},{"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":9,"secondary_topics":31,"classification":32,"search_phrases":59,"slug":62,"view_count":63,"doi":64,"paper":65,"created_at":85},3900,"ANÁLISE COMPARATIVA DE SENSORES RGB E LiDAR APLICADOS À TOPOGRAFIA COM DRONES EM ÁREAS AGRÍCOLAS","https:\u002F\u002Fdoi.org\u002F10.24980\u002Fucm.v15i17.6682","O sensoriamento remoto tem se consolidado como uma ferramenta fundamental no monitoramento ambiental e no manejo agrícola, sobretudo com o avanço dos Veículos Aéreos Não Tripulados (VANTs). Este trabalho teve como objetivo realizar uma análise comparativa entre os sensores RGB e LiDAR aplicados ao levantamento topográfico em áreas de cultivo de cana-de-açúcar, atividade que demanda alta precisão altimétrica para a sistematização do terreno. O estudo foi conduzido na Fazenda Brasil, localizada em Suzanápolis-SP, utilizando drones equipados com câmera fotogramétrica RGB e sensor LiDAR. Foram avaliados parâmetros como altitude de voo, sobreposição de imagens, densidade de pontos, tempo de processamento, presença de GCPs e capacidade de penetração na vegetação. Os resultados demonstraram que o sensor LiDAR proporciona maior precisão na obtenção do Modelo Digital do Terreno (MDT), especialmente, em áreas com vegetação densa e relevo acidentado, devido à sua capacidade de atravessar a cobertura vegetal e captar informações diretamente do solo e o sensor RGB apresentou melhor desempenho em áreas abertas, com menor custo operacional, porém com limitações na definição altimétrica em regiões vegetadas. Conclui-se que, embora ambas as tecnologias apresentem potencial para aplicação na agricultura de precisão, o LiDAR mostra-se mais eficiente para levantamentos que exigem maior acurácia em ambientes complexos, enquanto o RGB pode ser uma alternativa viável em áreas menos exigentes em termos de detalhamento topográfico. COMPARATIVE ANALYSIS OF RGB AND LiDAR SENSORS APPLIED TO UAV-BASED TOPOGRAPHIC SURVEYS IN AGRICULTURAL AREAS ABSTRACT Remote sensing has become an essential tool for environmental monitoring and agricultural management, particularly with the advancement of Unmanned Aerial Vehicles (UAVs). This study aimed to conduct a comparative analysis of RGB and LiDAR sensors applied to topographic surveys in sugarcane cultivation areas, an activity that requires high altimetric accuracy for land leveling. The study was conducted at Fazenda Brasil, located in Suzanápolis, São Paulo, Brazil, using UAVs equipped with an RGB photogrammetric camera and a LiDAR sensor. Parameters such as flight altitude, image overlap, point density, processing time, presence of Ground Control Points (GCPs), and vegetation penetration capability were evaluated. The results demonstrated that the LiDAR sensor provides greater accuracy in generating the Digital Terrain Model (DTM), particularly in areas with dense vegetation and rugged terrain, due to its ability to penetrate vegetation cover and acquire information directly from the ground. The RGB sensor performed better in open areas and had lower operational costs, but showed limitations in altimetric definition in vegetated areas. It is concluded that, although both technologies have potential applications in precision agriculture, LiDAR is more efficient for surveys requiring greater accuracy in complex environments, whereas RGB may be a viable alternative in areas with lower topographic detail requirements. Keywords: precision agriculture; remote sensing; RGB; LiDAR.","遥感技术已巩固为环境监测和农业管理中的基本工具，尤其是随着无人机（VANTs）的发展。本研究旨在对应用于甘蔗种植区地形测量的RGB和LiDAR传感器进行比较分析，该活动需要高海拔精度以进行土地平整。研究在位于圣保罗州苏扎纳波利斯的巴西农场进行，使用配备RGB摄影测量相机和LiDAR传感器的无人机。评估了飞行高度、图像重叠度、点密度、处理时间、地面控制点（GCPs）的存在以及植被穿透能力等参数。结果表明，LiDAR传感器在获取数字地形模型（MDT）方面提供了更高的精度，特别是在植被密集和地形崎岖的区域，因为它能够穿透植被覆盖并直接从地面捕获信息，而RGB传感器在开阔区域表现更好，运营成本较低，但在植被覆盖区域的altimetric定义上存在局限性。结论是，尽管两种技术在精准农业中都具有应用潜力，但LiDAR在需要更高精度的复杂环境中显示出更高的效率，而RGB在地形细节要求较低的区域可以成为一种可行的替代方案。RGB和LiDAR传感器应用于农业区域无人机地形测量的比较分析 摘要 遥感已成为环境监测和农业管理的重要工具，特别是随着无人机（UAVs）的进步。本研究旨在对应用于甘蔗种植区地形测量的RGB和LiDAR传感器进行比较分析，该活动需要高海拔精度以进行土地平整。研究在位于巴西圣保罗州苏扎纳波利斯的巴西农场进行，使用配备RGB摄影测量相机和LiDAR传感器的无人机。评估了飞行高度、图像重叠度、点密度、处理时间、地面控制点（GCPs）的存在以及植被穿透能力等参数。结果表明，LiDAR传感器在生成数字地形模型（DTM）方面提供了更高的精度，特别",null,"Unifunec Científica Multidisciplinar","2026-09-30T00:00:00Z","论文",10,false,55,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,18,15,6,1,"巴西期刊对RGB与LiDAR无人机地形测绘的对比研究，方法扎实、结论明确，对精准农业地形平整有参考价值，但属细分技术论文，公共影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","无人机","甘蔗","精准农业","遥感",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":40,"reason":58},[38,45,52],{"code":39,"score":17,"supported":40,"title":41,"abstract":42,"tags":44},"I1",true,[],[29,43],"precision agriculture",[26,29],{"code":46,"score":47,"supported":40,"title":48,"abstract":49,"tags":51},"A6",7,[],[28,50],"sugarcane",[28],{"code":53,"score":47,"supported":40,"title":54,"abstract":55,"tags":57},"I3",[],[30,56],"remote sensing",[30],"多个产业证据相当，需要确认主归属",[60,61],"RGB LiDAR 无人机 地形测量","甘蔗 精准农业 无人机","RGBLiDAR无人机地形测量-3900",2,"10.24980\u002Fucm.v15i17.6682",{"doi":64,"openalex_id":66,"authors":67,"venue":10,"cited_by_count":77,"oa_url":6,"card":78,"direction":82,"ingested_from":84},"W7214933645",[68,70,72,74],{"name":69,"orcid":9},"Alexandre Gabriel LUCHETE",{"name":71,"orcid":9},"Eduardo Sanches CARDOSO",{"name":73,"orcid":9},"Fernando Alves TROMBETA",{"name":75,"orcid":76},"Camila Fernandes Ferreira APARECIDO","https:\u002F\u002Forcid.org\u002F0000-0002-8429-950X",0,{"tldr":79,"method":80,"finding":81,"direction":82,"opportunity":83},"对比无人机RGB与LiDAR传感器在甘蔗田地形测绘中的精度与适用性。","在巴西甘蔗田用无人机搭载RGB相机与LiDAR，评估飞行参数、点密度与植被穿透性","LiDAR在植被密集和崎岖地形中生成DTM精度更高，RGB在开阔区成本更低但高程受限。","农业遥感与作物表型","可探索RGB与LiDAR点云融合，在低成本下提升复杂农田地形建模精度。","openalex","2026-10-01T23:30:29.092979Z",{"total":20,"page":21,"page_size":20,"items":87},[88,192,282,374,432,508],{"id":89,"title":90,"url":91,"summary":92,"summary_zh":93,"content":9,"source_name":94,"source_url":91,"published_at":95,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":96,"score_detail":97,"sources":102,"tags":104,"primary_topic":107,"secondary_topics":113,"classification":119,"search_phrases":144,"slug":147,"view_count":77,"doi":148,"paper":149,"created_at":191},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":18,"substance":98,"depth":18,"authority":99,"freshness":100,"relevant":21,"comment":101},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。",[103],{"name":94,"url":91},[26,27,105,30,106],"育种","紫花苜蓿",{"code":108,"slug":109,"name":110,"path":111,"parent_code":112},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[114],{"code":53,"slug":115,"name":116,"path":117,"parent_code":118},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":120,"locked":14,"version":34,"method":35,"evidence":121},"classified",{"candidates":122,"agricultural_evidence":40},[123,129,134,138],{"code":108,"score":19,"supported":40,"title":124,"abstract":126,"tags":128},[125],"alfalfa",[127,125],"苜蓿",[106],{"code":53,"score":99,"supported":40,"title":130,"abstract":132,"tags":133},[131],"multispectral",[131],[30],{"code":39,"score":47,"supported":40,"title":135,"abstract":136,"tags":137},[],[29,43],[26],{"code":139,"score":140,"supported":40,"title":141,"abstract":142,"tags":143},"C4",4,[],[127],[106],[145,146],"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":148,"openalex_id":150,"authors":151,"venue":94,"cited_by_count":77,"oa_url":91,"card":186,"direction":82,"ingested_from":84},"W7223083966",[152,155,158,161,164,166,168,171,174,177,180,183],{"name":153,"orcid":154},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":156,"orcid":157},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":159,"orcid":160},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":162,"orcid":163},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":165,"orcid":9},"Julie Hansen",{"name":167,"orcid":9},"Christopher Pierce",{"name":169,"orcid":170},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":172,"orcid":173},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":175,"orcid":176},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":178,"orcid":179},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":181,"orcid":182},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":184,"orcid":185},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":187,"method":188,"finding":189,"direction":82,"opportunity":190},"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。","2026-10-22T11:20:32.655394Z",{"id":193,"title":194,"url":195,"summary":196,"summary_zh":197,"content":9,"source_name":198,"source_url":195,"published_at":199,"category":12,"cover_url":9,"hotness":13,"is_selected":40,"score":200,"score_detail":201,"sources":206,"tags":208,"primary_topic":211,"secondary_topics":216,"classification":217,"search_phrases":249,"slug":252,"view_count":140,"doi":253,"paper":254,"created_at":281},5382,"Modeling the global potential of smart farming innovations","https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.2530081123","Smart farming innovations can improve agricultural productivity, resilience, and environmental sustainability, yet their real-world impact hinges on their diffusion dynamics, which in turn depend on their economic viability. We present here a global assessment of the profitability of smart farming innovation across croplands at a spatial resolution of ~5 km from the farmers’ and the societal perspectives. We cover application mapping for precision agriculture, automated application of inputs, autonomous weeding machines, GPS steering assistance systems, and autonomous irrigation systems. For this, we compiled a globally comprehensive dataset of smart farming innovation adoption locations (N = 4,422) based on a combination of a survey (N = 2,677) and a meta-analysis (N = 1,745) and combined them with remotely sensed and statistical data to train an ensemble of five geospatial machine learning models (generalized linear models, random forests, support vector machines, maximum entropy, and boosted regression trees), which show the global distribution of the different innovations’ profitability and its overlap with major environmental issues the innovations can help to mitigate. Simulations of future socioeconomic pathways combined with assumptions about future agri-environmental policies reveal that, especially in middle-income countries, the potential of smart farming will grow over time.","智慧农业创新能够提升农业生产力、韧性和环境可持续性，然而其现实影响取决于扩散动态，而扩散动态又取决于其经济可行性。本文从农民和社会两个视角，以约5公里的空间分辨率对全球农田范围内智慧农业创新的盈利能力进行了评估。研究涵盖精准农业的应用测绘、投入品的自动化施用、自主除草机械、GPS转向辅助系统以及自主灌溉系统。为此，我们基于一项调查（N = 2,677）和一项元分析（N = 1,745）相结合，编制了全球范围内较为全面的智慧农业创新采用地点数据集（N = 4,422），并将其与遥感和统计数据相结合，用于训练由五种地理空间机器学习模型（广义线性模型、随机森林、支持向量机、最大熵和提升回归树）组成的集成模型，这些模型揭示了不同创新盈利能力的全球分布及其与这些创新有助于缓解的主要环境问题的重叠情况。对未来社会经济路径的模拟结合对未来农业环境政策的假设表明，智慧农业的潜力将随时间推移而增长，尤其是在中等收入国家。","Proceedings of the National Academy of Sciences","2026-10-19T00:00:00Z",93,{"impact":202,"substance":203,"depth":204,"authority":19,"freshness":100,"relevant":21,"comment":205},26,24,19,"PNAS 全球尺度智慧农业盈利性评估，数据规模与方法新颖，结论对智慧农业推广具政策参考价值，值得进入每日精选。",[207],{"name":198,"url":195},[26,209,29,30,210],"农业人工智能","全球评估",{"code":212,"slug":213,"name":214,"path":215,"parent_code":118},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":120,"locked":14,"version":34,"method":35,"evidence":218},{"candidates":219,"agricultural_evidence":40},[220,225,231,238,242],{"code":39,"score":221,"supported":40,"title":222,"abstract":223,"tags":224},11,[],[26,29,43],[26,29],{"code":212,"score":47,"supported":40,"title":226,"abstract":227,"tags":230},[],[228,229],"机器学习","machine learning",[209],{"code":232,"score":20,"supported":40,"title":233,"abstract":234,"tags":237},"H3",[],[235,236],"灌溉","irrigation",[],{"code":53,"score":140,"supported":40,"title":239,"abstract":240,"tags":241},[],[30],[30],{"code":243,"score":244,"supported":40,"title":245,"abstract":246,"tags":248},"F4",3,[],[247],"除草",[],[250,251],"农业人工智能 全球评估 智慧农业 精准农业","农业人工智能 全球评估","农业人工智能全球评估智慧农业精准农业-5382","10.1073\u002Fpnas.2530081123",{"doi":253,"openalex_id":255,"authors":256,"venue":198,"cited_by_count":77,"oa_url":195,"card":275,"direction":279,"ingested_from":84},"W7222974188",[257,260,263,266,269,272],{"name":258,"orcid":259},"Shahrear Roman","https:\u002F\u002Forcid.org\u002F0009-0006-5659-4292",{"name":261,"orcid":262},"Hadi Hadi","https:\u002F\u002Forcid.org\u002F0000-0002-2410-9751",{"name":264,"orcid":265},"Wyclife Agumba Oluoch","https:\u002F\u002Forcid.org\u002F0000-0003-0803-9279",{"name":267,"orcid":268},"David Zilberman","https:\u002F\u002Forcid.org\u002F0000-0001-5743-5134",{"name":270,"orcid":271},"Robert Finger","https:\u002F\u002Forcid.org\u002F0000-0002-0634-5742",{"name":273,"orcid":274},"David Wuepper","https:\u002F\u002Forcid.org\u002F0000-0002-1344-6023",{"tldr":276,"method":277,"finding":278,"direction":279,"opportunity":280},"全球5公里尺度评估五类智慧农业创新在农田的盈利性与扩散潜力。","整合4422个采用点调查与元分析数据，用五种地理空间机器学习集成建模。","智慧农业盈利性空间差异显著，中等收入国家未来潜力将随社会经济路径增长。","智慧农业 \u002F 农业物联网","可结合区域政策与农户异质性，细化盈利性预测并评估环境协同效益。","2026-10-20T23:30:08.754594Z",{"id":283,"title":284,"url":285,"summary":286,"summary_zh":287,"content":9,"source_name":288,"source_url":285,"published_at":289,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":290,"score_detail":291,"sources":297,"tags":299,"primary_topic":302,"secondary_topics":303,"classification":315,"search_phrases":359,"slug":362,"view_count":63,"doi":9,"paper":363,"created_at":373},5013,"REMOTE SENSING AND GIS-BASED ASSESSMENT OF SOIL SALINITY IN IRRIGATED LANDS: METHODS, CHALLENGES AND PROSPECTS FOR PRECISION AGRICULTURE","http:\u002F\u002Fwww.arabianjournalofscience.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F1223","Soil salinization is a major land degradation process affecting the sustainability of irrigated agriculture, particularly in arid and semi-arid regions. Conventional soil sampling provides essential information about salinity levels but may be insufficient for continuous monitoring of large agricultural territories because of spatial variability, sampling costs, and temporal limitations. Remote sensing (RS) and geographic information systems (GIS) offer complementary approaches for identifying spatial patterns of salinity, monitoring land conditions, and supporting agricultural management. This article examines the scientific and methodological foundations of integrating multispectral satellite imagery, spectral indices, geospatial analysis, and machine learning for soil salinity assessment in irrigated lands. Particular attention is given to Landsat and Sentinel-2 imagery, the interpretation of vegetation and soil reflectance indicators, the role of electrical conductivity measurements in model calibration, and the limitations associated with vegetation cover, soil moisture, and seasonal variability. The analysis indicates that reliable salinity assessment requires the integration of satellite observations with field measurements and environmental variables rather than the isolated application of spectral indices. The article proposes a methodological framework for developing spatially explicit salinity assessment systems applicable to irrigated agricultural landscapes, including those of Central Asia. The framework provides a basis for improving monitoring strategies and supporting evidence-based land management without assuming unverified local measurement results.","土壤盐渍化是影响灌溉农业可持续性的主要土地退化过程，在干旱和半干旱地区尤为突出。传统土壤采样可提供盐分水平的基本信息，但由于空间变异性、采样成本和时间限制，可能不足以对大面积农业区域进行持续监测。遥感（RS）和地理信息系统（GIS）为识别盐渍化空间格局、监测土地状况和支持农业管理提供了互补性方法。本文探讨了整合多光谱卫星影像、光谱指数、地理空间分析和机器学习进行灌溉土地土壤盐渍化评估的科学与方法学基础。文章特别关注Landsat和Sentinel-2影像、植被与土壤反射率指标的解译、电导率测量在模型校准中的作用，以及与植被覆盖、土壤湿度和季节变异性相关的局限性。分析表明，可靠的盐渍化评估需要将卫星观测与野外测量和环境变量相结合，而非孤立地应用光谱指数。本文提出了一个方法学框架，用于开发适用于灌溉农业景观（包括中亚地区）的空间显式盐渍化评估系统。该框架为改进监测策略和支持基于证据的土地管理提供了基础，且不以未经核实的当地测量结果为假设前提。","Arabian journal of science.","2026-10-13T00:00:00Z",74,{"impact":292,"substance":293,"depth":294,"authority":295,"freshness":17,"relevant":21,"comment":296},16,20,17,13,"系统综述遥感与GIS在灌溉农田盐渍化评估中的方法框架与挑战，对盐碱地治理和精准农业有参考价值，但属综述性论文，非突破性成果。",[298],{"name":288,"url":285},[26,29,30,300,301],"盐碱地治理","土壤监测",{"code":53,"slug":115,"name":116,"path":117,"parent_code":118},[304,309],{"code":305,"slug":306,"name":307,"path":308,"parent_code":118},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":310,"slug":311,"name":312,"path":313,"parent_code":314},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":120,"locked":14,"version":34,"method":35,"evidence":316},{"candidates":317,"agricultural_evidence":40},[318,322,330,335,339,343,347,353],{"code":53,"score":18,"supported":40,"title":319,"abstract":320,"tags":321},[56],[30,56,131],[30],{"code":305,"score":294,"supported":40,"title":323,"abstract":325,"tags":329},[324],"GIS",[326,327,324,328],"地理信息","空间分析","geographic information",[],{"code":310,"score":19,"supported":40,"title":331,"abstract":332,"tags":334},[311],[333,311],"土壤",[300],{"code":39,"score":13,"supported":40,"title":336,"abstract":337,"tags":338},[43],[],[26,29],{"code":212,"score":20,"supported":40,"title":340,"abstract":341,"tags":342},[],[228,229],[],{"code":232,"score":244,"supported":40,"title":344,"abstract":345,"tags":346},[],[235],[],{"code":348,"score":244,"supported":40,"title":349,"abstract":350,"tags":352},"H7",[],[351],"干旱",[],{"code":354,"score":244,"supported":40,"title":355,"abstract":356,"tags":358},"I7",[],[357],"methodological framework",[],[360,361],"遥感 GIS 土壤盐渍化","Landsat Sentinel-2 盐分监测","遥感GIS土壤盐渍化-5013",{"doi":9,"openalex_id":364,"authors":365,"venue":288,"cited_by_count":77,"oa_url":285,"card":368,"direction":82,"ingested_from":84},"W7221843179",[366],{"name":367,"orcid":9},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":369,"method":370,"finding":371,"direction":82,"opportunity":372},"综述遥感与GIS评估灌溉地土壤盐渍化的方法、挑战与前景，并提出空间显式评估框架。","整合多光谱卫星影像、光谱指数、GIS空间分析与机器学习，结合电导率校准。","可靠盐渍化评估需卫星观测与实地测量及环境变量融合，而非孤立使用光谱指数。","可探索多源遥感与机器学习融合的盐渍化动态监测，并验证于中亚等干旱灌区。","2026-10-15T23:30:32.401626Z",{"id":375,"title":376,"url":377,"summary":378,"summary_zh":9,"content":9,"source_name":379,"source_url":377,"published_at":380,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":381,"score_detail":382,"sources":385,"tags":387,"primary_topic":389,"secondary_topics":393,"classification":394,"search_phrases":414,"slug":417,"view_count":21,"doi":418,"paper":419,"created_at":431},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","2026-10-14T00:00:00Z",75,{"impact":292,"substance":293,"depth":294,"authority":383,"freshness":17,"relevant":21,"comment":384},14,"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。",[386],{"name":379,"url":377},[26,27,209,30,388],"大麦",{"code":39,"slug":390,"name":391,"path":392,"parent_code":118},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":120,"locked":14,"version":34,"method":35,"evidence":395},{"candidates":396,"agricultural_evidence":40},[397,401,406,410],{"code":39,"score":140,"supported":40,"title":398,"abstract":399,"tags":400},[],[43],[26],{"code":402,"score":21,"supported":14,"title":403,"abstract":404,"tags":405},"A5",[],[],[388],{"code":212,"score":21,"supported":14,"title":407,"abstract":408,"tags":409},[],[],[209],{"code":53,"score":21,"supported":14,"title":411,"abstract":412,"tags":413},[],[],[30],[415,416],"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":418,"openalex_id":420,"authors":421,"venue":379,"cited_by_count":77,"oa_url":9,"card":9,"direction":9,"ingested_from":84},"W7221758985",[422,425,428],{"name":423,"orcid":424},"Ameer Tamoor Khan","https:\u002F\u002Forcid.org\u002F0000-0001-6838-992X",{"name":426,"orcid":427},"Saiful Azim","https:\u002F\u002Forcid.org\u002F0000-0002-2686-7150",{"name":429,"orcid":430},"Signe Marie Jensen","https:\u002F\u002Forcid.org\u002F0000-0002-7738-5231","2026-10-15T23:30:02.421798Z",{"id":433,"title":434,"url":435,"summary":436,"summary_zh":437,"content":9,"source_name":438,"source_url":435,"published_at":289,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":439,"score_detail":440,"sources":443,"tags":445,"primary_topic":448,"secondary_topics":449,"classification":451,"search_phrases":490,"slug":493,"view_count":21,"doi":494,"paper":495,"created_at":507},4896,"Site-Specific Precision Cover Cropping for Soil Health and Environmental Sustainability Using Remote Sensing and Multi-Criteria Analysis","https:\u002F\u002Fdoi.org\u002F10.21663\u002Feeg-d-26-00041","ABSTRACT Cover crops, which are plants or mixtures of plants grown primarily to enhance and protect soil health, are a key strategy in sustainable agriculture. Traditionally, they have been uniformly planted in a field typically during off-season when the main crops are not in competition. However, advancements in precision agriculture, particularly the integration of remote-sensing technology, demand more efficient seeding strategies based on field variability. The aims of this study were to assess the specific needs of a field in Aurora County, SD, and determine the type and optimal combinations of the cover crop mix. The cover crop species considered in this study included legumes, grasses, and brassicas. The remote-sensing data were extracted from Sentinel-2 satellite imagery. Soil samples were collected from 0 to 30 cm depth, and soil properties were measured. Additionally, topographic maps such as slope and aspect were extracted from light detection and ranging (LiDAR) data. The results of precision cover crop analyses showed that 6.5 percent (7.8 ha), 20.6 percent (24.7 ha), 38.1 percent (45.7 ha), 29.6 percent (35.5 ha), and 5.3 percent (6.3 ha) of the studied field should planted in the following mixtures of crops: rye, radish, and vetch; triticale, turnip, and vetch; triticale, radish, and clover; triticale, rapeseed, and pea; and barley, flax, and clover, respectively. The mixtures significantly improved soil properties by reducing compaction, increasing residue C\u002FN ratio, and enhancing weed suppression compared with the control. The resulting map should be interpreted as a decision-support recommendation rather than a fully validated field prescription.","摘要 覆盖作物（cover crops）是指主要为了提升和保护土壤健康而种植的植物或植物混合体，是可持续农业的一项关键策略。传统上，覆盖作物通常在非主作物竞争的季节以均匀方式播种于田间。然而，精准农业的进步，尤其是遥感技术的整合，要求基于田块变异性制定更高效的播种策略。本研究旨在评估南达科他州奥罗拉县某田块的具体需求，并确定覆盖作物混合物的类型及最优组合。本研究考虑的覆盖作物种类包括豆科、禾本科和十字花科植物。遥感数据提取自Sentinel-2卫星影像。土壤样品采集深度为0至30 cm，并测定了土壤性质。此外，从激光雷达（LiDAR）数据中提取了坡度和坡向等地形图。精准覆盖作物分析结果表明，研究田块中分别有6.5%（7.8 ha）、20.6%（24.7 ha）、38.1%（45.7 ha）、29.6%（35.5 ha）和5.3%（6.3 ha）的面积应种植以下作物混合物：黑麦、萝卜和野豌豆；小黑麦、芜菁和野豌豆；小黑麦、萝卜和三叶草；小黑麦、油菜和豌豆；以及大麦、亚麻和三叶草。与对照相比，这些混合物通过降低紧实度、提高残体碳氮比和增强杂草抑制效果，显著改善了土壤性质。所得图谱应被解读为决策支持建议，而非经过充分验证的田间处方。","Environmental and Engineering Geoscience",72,{"impact":99,"substance":441,"depth":294,"authority":295,"freshness":100,"relevant":21,"comment":442},21,"该研究将遥感与多准则分析结合用于精准覆盖作物制图，方法新颖、数据具体，对农业信息化与可持续农业有参考价值，但属单点田间案例，影响范围有限。",[444],{"name":438,"url":435},[26,29,30,446,447],"土壤健康","覆盖作物",{"code":310,"slug":311,"name":312,"path":313,"parent_code":314},[450],{"code":53,"slug":115,"name":116,"path":117,"parent_code":118},{"status":120,"locked":14,"version":34,"method":35,"evidence":452},{"candidates":453,"agricultural_evidence":40},[454,458,462,466,473,480,486],{"code":310,"score":19,"supported":40,"title":455,"abstract":456,"tags":457},[311],[333,311],[446],{"code":53,"score":99,"supported":40,"title":459,"abstract":460,"tags":461},[56],[30],[30],{"code":39,"score":17,"supported":40,"title":463,"abstract":464,"tags":465},[],[29,43],[26,29],{"code":467,"score":20,"supported":40,"title":468,"abstract":469,"tags":472},"A4",[],[470,471],"油菜","rapeseed",[],{"code":474,"score":20,"supported":40,"title":475,"abstract":476,"tags":479},"H8",[],[477,478],"可持续农业","sustainable agriculture",[],{"code":481,"score":244,"supported":40,"title":482,"abstract":483,"tags":485},"B1",[],[484],"萝卜",[],{"code":108,"score":21,"supported":14,"title":487,"abstract":488,"tags":489},[],[],[447],[491,492],"Sentinel-2 遥感 覆盖作物","精准覆盖作物 土壤健康","Sentinel-2遥感覆盖作物-4896","10.21663\u002Feeg-d-26-00041",{"doi":494,"openalex_id":496,"authors":497,"venue":438,"cited_by_count":77,"oa_url":9,"card":502,"direction":82,"ingested_from":84},"W7221669942",[498,500],{"name":499,"orcid":9},"Ali Mirzakhani Nafchi",{"name":501,"orcid":9},"Salman Mirzaee",{"tldr":503,"method":504,"finding":505,"direction":82,"opportunity":506},"利用遥感与多准则分析为南达科他州田块制定精准覆盖作物混播方案。","Sentinel-2影像、LiDAR地形与土壤采样，结合多准则分析。","田块被划分为5个管理区，各推荐不同覆盖作物混播，显著改善土壤紧实度、残体C\u002FN比和抑草效果。","可验证该决策支持地图的田间效果，并融合实时物联网数据优化动态推荐。","2026-10-14T23:30:27.077312Z",{"id":509,"title":510,"url":511,"summary":512,"summary_zh":513,"content":9,"source_name":514,"source_url":511,"published_at":515,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":516,"score_detail":517,"sources":519,"tags":521,"primary_topic":523,"secondary_topics":527,"classification":528,"search_phrases":554,"slug":557,"view_count":21,"doi":558,"paper":559,"created_at":575},4825,"Multi-Source Remote Sensing Fusion and Deep Learning for Intelligent Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206434","The fusion of multi-source remote sensing data and deep learning technology has been increasingly applied in precision agriculture. This review focuses on the perception–decision–execution framework and summarizes the current application status, technical routes, and development trends of multi-source remote sensing data fusion and deep learning in precision agriculture. It examines the complementary advantages of multimodal data such as optical, thermal infrared (TIR), Light Detection and Ranging (LiDAR), and synthetic aperture radar (SAR) in characterizing crop physiological traits and structural characteristics. At the algorithmic level, this review analyzes the evolution of deep learning architectures from single convolutional neural networks (CNNs) to spatiotemporal fusion networks (STFNs), Transformer-based architectures, and emerging state-space models, with emphasis on their capabilities for heterogeneous feature extraction and cross-modal information integration. This review discusses the transformation mechanism from intelligent perception to executable decision-making and explores the application effectiveness of prescription maps and variable-rate task technology in improving resource utilization. This review proposes forward-looking views on core bottlenecks such as model generalization, physical interpretability, and edge computing. This review not only provides a theoretical analysis of precision agriculture but also provides academic recommendations for the development of intelligent agricultural equipment in the future.","多源遥感数据与深度学习技术的融合已在精准农业中得到日益广泛的应用。本文以“感知—决策—执行”框架为主线，综述了多源遥感数据融合与深度学习在精准农业中的应用现状、技术路线与发展趋势。文章考察了光学、热红外（TIR）、激光雷达（LiDAR）和合成孔径雷达（SAR）等多模态数据在表征作物生理性状与结构特征方面的互补优势。在算法层面，本文分析了深度学习架构从单一卷积神经网络（CNN）向时空融合网络（STFN）、基于Transformer的架构以及新兴状态空间模型的演进，重点探讨了其在异构特征提取与跨模态信息整合方面的能力。本文讨论了从智能感知到可执行决策的转化机制，并探究了处方图与变量作业技术在提升资源利用率方面的应用效果。针对模型泛化能力、物理可解释性与边缘计算等核心瓶颈，本文提出了前瞻性观点。本文不仅为精准农业提供了理论分析，也为未来智能农业装备的发展提供了学术建议。","Sensors","2026-10-12T00:00:00Z",78,{"impact":18,"substance":293,"depth":18,"authority":383,"freshness":17,"relevant":21,"comment":518},"系统综述多源遥感融合与深度学习在精准农业中的感知—决策—执行框架，方法脉络清晰、前瞻性强，对智慧农业装备研发有参考价值。",[520],{"name":514,"url":511},[26,209,29,30,522],"多源数据融合",{"code":118,"slug":524,"name":525,"path":526,"parent_code":9},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":529,"locked":14,"version":34,"method":35,"evidence":530},"partial",{"candidates":531,"agricultural_evidence":40,"reason":553},[532,536,542,546],{"code":39,"score":292,"supported":40,"title":533,"abstract":534,"tags":535},[43],[29,43],[26,29],{"code":212,"score":19,"supported":40,"title":537,"abstract":539,"tags":541},[538],"deep learning",[540,538],"深度学习",[209],{"code":53,"score":19,"supported":40,"title":543,"abstract":544,"tags":545},[56],[30,56],[30],{"code":547,"score":47,"supported":40,"title":548,"abstract":549,"tags":552},"I5",[],[550,551],"边缘计算","edge computing",[522],"一级领域明确，二级方向需要细分",[555,556],"多源遥感 深度学习 精准农业","Transformer 遥感 作物表型","多源遥感深度学习精准农业-4825","10.3390\u002Fs26206434",{"doi":558,"openalex_id":560,"authors":561,"venue":514,"cited_by_count":77,"oa_url":511,"card":570,"direction":82,"ingested_from":84},"W7221565940",[562,565,568],{"name":563,"orcid":564},"Zhen Ma","https:\u002F\u002Forcid.org\u002F0000-0003-4377-9651",{"name":566,"orcid":567},"Xiyuan Chen","https:\u002F\u002Forcid.org\u002F0000-0002-1770-8529",{"name":569,"orcid":9},"Cundeng Wang",{"tldr":571,"method":572,"finding":573,"direction":82,"opportunity":574},"综述多源遥感融合与深度学习在精准农业感知-决策-执行框架中的应用与趋势。","综述光学、热红外、LiDAR、SAR融合及CNN到Transformer、状态空","多模态融合与先进深度学习架构可提升作物感知与处方图决策，但泛化性、可解释性和边缘计算仍是瓶颈。","可探索轻量化状态空间模型与物理约束融合，提升跨区域泛化及边缘端实时决策能力。","2026-10-13T23:30:24.008972Z"]