[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2049":3,"related-2049":95},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":61,"slug":64,"view_count":65,"doi":66,"paper":67,"created_at":94},2049,"“Space-sky-ground-Lake” four-dimensional monitoring technology for water quality in Baiyangdian - a new model for future environmental monitoring","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.wsee.2026.08.002","The “Space-Sky-Ground-Lake” four-dimensional integrated water quality monitoring system in Xiong'an New Area, represents a significant innovation in environmental monitoring. By combining satellite remote sensing, unmanned aerial vehicles (UAVs), unmanned ships, and automatic water quality monitoring stations, the system achieves comprehensive, three-dimensional oversight of Baiyangdian's water quality. Satellite data enables high-resolution spatial analysis and trend assessment, while UAVs and unmanned ships provide real-time data from inaccessible regions. Automated stations continuously track key indicators, enhancing efficiency and accuracy. This intelligent and unmanned monitoring system markedly enhances both monitoring efficiency and data accuracy, offering new directions for the advancement of environmental monitoring, particularly in water quality assessment. Its successful implementation demonstrates that cutting-edge technology can effectively tackle the challenges posed by water quality monitoring in complex environments, thereby providing robust support for ecological protection and governance. As technology evolves, the SSGL framework is poised to drive the environmental monitoring sector toward greater intelligence and efficiency.","雄安新区“天地水”一体化水质监测系统是环境监测领域的一项重大创新。该系统融合了卫星遥感、无人机、无人船及水质自动监测站，实现了对白洋淀水质的全面立体监管。卫星数据支持高分辨率空间分析与趋势评估，无人机和无人船则能从未能到达的区域实时采集数据。自动监测站持续追踪关键指标，提升了效率与准确性。这一智能化、无人化的监测系统显著提高了监测效率和数据精度，为环境监测尤其是水质评估的发展提供了新方向。其成功实施证明，前沿技术能有效应对复杂环境下的水质监测挑战，为生态保护与治理提供了有力支撑。随着技术的进步，“天地水”框架有望推动环境监测行业向更智能、更高效的方向发展。",null,"Watershed Ecology and the Environment","2026-09-08T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,13,8,1,"空天地湖一体化水质监测系统为复杂环境下的水环境治理提供了可复制的智能化范式，方法新颖、数据体系完整，对农业面源污染与乡村生态监测具有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字乡村","无人机","遥感","水质监测","智慧环保",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":60},[39,47,55],{"code":40,"score":41,"supported":42,"title":43,"abstract":44,"tags":46},"I3",7,true,[],[29,45],"remote sensing",[29],{"code":48,"score":49,"supported":42,"title":50,"abstract":51,"tags":54},"I4",6,[],[52,53],"空间分析","spatial analysis",[],{"code":56,"score":22,"supported":14,"title":57,"abstract":58,"tags":59},"I6",[],[],[27],"农业相关性需要复核",[62,63],"数字乡村 智慧环保 水质监测 无人机","数字乡村 智慧环保","数字乡村智慧环保水质监测无人机-2049",0,"10.1016\u002Fj.wsee.2026.08.002",{"doi":66,"openalex_id":68,"authors":69,"venue":10,"cited_by_count":65,"oa_url":86,"card":87,"direction":91,"ingested_from":93},"W7211976305",[70,72,74,76,78,81,83],{"name":71,"orcid":9},"Yajun Qiao",{"name":73,"orcid":9},"Chunhui Zhang",{"name":75,"orcid":9},"Tingyue Liu",{"name":77,"orcid":9},"Zhiyu Zhang",{"name":79,"orcid":80},"Rong Chang","https:\u002F\u002Forcid.org\u002F0000-0002-9914-1449",{"name":82,"orcid":9},"Tianyu Kang",{"name":84,"orcid":85},"Peidong Su","https:\u002F\u002Forcid.org\u002F0000-0002-1658-697X","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2589471426000136\u002Fpdf",{"tldr":88,"method":89,"finding":90,"direction":91,"opportunity":92},"构建“天-空-地-湖”四维水质监测系统，实现白洋淀水质立体化智能监控。","融合卫星遥感、无人机、无人船与自动监测站，多源数据协同监测。","该系统显著提升监测效率与数据精度，为复杂环境水质监测提供新范式。","智慧农业 \u002F 农业物联网","可探索多源异构数据融合算法与实时预警模型，并迁移至农业面源污染监测场景。","openalex","2026-09-10T23:30:15.733502Z",{"total":49,"page":22,"page_size":49,"items":96},[97,207,286,358,418,478],{"id":98,"title":99,"url":100,"summary":101,"summary_zh":102,"content":9,"source_name":103,"source_url":100,"published_at":104,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":105,"score_detail":106,"sources":111,"tags":113,"primary_topic":117,"secondary_topics":123,"classification":129,"search_phrases":158,"slug":161,"view_count":65,"doi":162,"paper":163,"created_at":206},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":17,"substance":107,"depth":17,"authority":108,"freshness":109,"relevant":22,"comment":110},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。",[112],{"name":103,"url":100},[114,28,115,29,116],"智慧农业","育种","紫花苜蓿",{"code":118,"slug":119,"name":120,"path":121,"parent_code":122},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[124],{"code":40,"slug":125,"name":126,"path":127,"parent_code":128},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":130,"locked":14,"version":35,"method":36,"evidence":131},"classified",{"candidates":132,"agricultural_evidence":42},[133,140,145,152],{"code":118,"score":134,"supported":42,"title":135,"abstract":137,"tags":139},15,[136],"alfalfa",[138,136],"苜蓿",[116],{"code":40,"score":108,"supported":42,"title":141,"abstract":143,"tags":144},[142],"multispectral",[142],[29],{"code":146,"score":41,"supported":42,"title":147,"abstract":148,"tags":151},"I1",[],[149,150],"精准农业","precision agriculture",[114],{"code":153,"score":154,"supported":42,"title":155,"abstract":156,"tags":157},"C4",4,[],[138],[116],[159,160],"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":162,"openalex_id":164,"authors":165,"venue":103,"cited_by_count":65,"oa_url":100,"card":200,"direction":204,"ingested_from":93},"W7223083966",[166,169,172,175,178,180,182,185,188,191,194,197],{"name":167,"orcid":168},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":170,"orcid":171},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":173,"orcid":174},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":176,"orcid":177},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":179,"orcid":9},"Julie Hansen",{"name":181,"orcid":9},"Christopher Pierce",{"name":183,"orcid":184},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":186,"orcid":187},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":189,"orcid":190},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":192,"orcid":193},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":195,"orcid":196},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":198,"orcid":199},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":201,"method":202,"finding":203,"direction":204,"opportunity":205},"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。","农业遥感与作物表型","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":208,"title":209,"url":210,"summary":211,"summary_zh":212,"content":9,"source_name":213,"source_url":210,"published_at":214,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":215,"score_detail":216,"sources":218,"tags":220,"primary_topic":224,"secondary_topics":225,"classification":226,"search_phrases":264,"slug":267,"view_count":65,"doi":268,"paper":269,"created_at":285},5395,"Strengthening Flood Resilience in Data-Sparse Agricultural Regions: A Planning Approach for Coastal Guyana","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23451582","Abstract. Coastal Guyana concentrates most of the country’s population and agricultural production in low-lying polder landscapes that are increasingly exposed to flooding from intense rainfall, sea-level rise, and deterioration of drainage and irrigation infrastructure. Engineering design and resilience planning in these settings are often constrained by limited hydrometric monitoring (discharge, water levels), incomplete documented drainage network, and restricted access to institutional datasets. This paper presents the value of using a low-information framework for flood resilience planning and preliminary hydraulic design in data-sparse agricultural regions, using the 364 km² part of Guyana’s Mahaica–Mahaicony catchment as a case study. The framework integrates (i) rainfall frequency analysis from long-term gauge records, (ii) remote sensing and global datasets (DEM and land-use\u002Fland-cover) to delineate sub-basins and screen flood impact, (iii) simplified runoff and volume-based drainage calculations consistent with rice-field storage and saturated soils, and (iv) structured stakeholder engagement to identify flood-prone areas, operational constraints, and feasible interventions. Six-sub-basins are used for developing a routing strategy to distribute runoff volumes and to explicitly incorporate upstream detention in the wetlands, thereby reducing peak flows conveyed northward through constrained canal corridors. High-level drainage canals, dedicated conveyance paths for critical sub-basins, and dual-purpose drains supporting both drainage and irrigation storage are used to determine preliminary cross-section designs, which are then assessed with 1D hydraulic modelling under tidal boundary conditions. The approach supports phased investment planning that improves flood safety, protects livelihoods, and increases agricultural reliability while aligning with national adaptation priorities.","摘要：圭亚那沿海地区集中了全国大部分人口和农业生产，地处低洼圩田景观，日益面临强降雨、海平面上升以及排水灌溉基础设施老化所带来的洪涝风险。在此类地区，工程设计与韧性规划往往受限于水文监测（流量、水位）资料不足、排水管网记录不完整以及机构数据集获取受限。本文以圭亚那马海卡—马海科尼（Mahaica–Mahaicony）流域364 km²的区域为案例，展示了在数据稀疏的农业地区采用低信息框架进行洪涝韧性规划与初步水力设计的价值。该框架整合了：(i) 基于长期测站记录的降雨频率分析；(ii) 利用遥感与全球数据集（DEM及土地利用\u002F土地覆盖）划分子流域并筛查洪涝影响；(iii) 与稻田蓄水和饱和土壤条件相适应的简化径流与基于水量的排水计算；(iv) 结构化利益相关方参与，以识别易涝区域、运行约束和可行干预措施。研究将流域划分为六个子流域，用于制定汇流路径策略，以分配径流量并明确纳入上游湿地滞蓄，从而削减通过受限渠廊向北输送的峰值流量。基于高位排水渠、关键子流域专用输水通道以及兼顾排水与灌溉蓄水的两用沟渠，确定初步断面设计，并在潮汐边界条件下采用一维水动力模型进行评估。该方法支持分阶段投资规划，在提升防洪安全、保护生计和提高农业可靠性的同时，与国家适应优先事项相衔接。","Geo-Eco-Marina","2026-10-18T00:00:00Z",70,{"impact":108,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":217},"该论文提出一种低信息条件下的防洪韧性规划框架，方法新颖，但案例位于圭亚那，对我国三农信息化的直接参考价值有限，因此不宜进入每日精选。",[219],{"name":213,"url":210},[27,29,221,222,223],"气候适应","防洪韧性","灌排设施",{"code":40,"slug":125,"name":126,"path":127,"parent_code":128},[],{"status":130,"locked":14,"version":35,"method":36,"evidence":227},{"candidates":228,"agricultural_evidence":42},[229,233,240,247,253,260],{"code":40,"score":41,"supported":42,"title":230,"abstract":231,"tags":232},[],[29,45],[29],{"code":234,"score":49,"supported":42,"title":235,"abstract":236,"tags":239},"A1",[],[237,238],"稻田","rice",[],{"code":241,"score":49,"supported":42,"title":242,"abstract":243,"tags":246},"H3",[],[244,245],"灌溉","irrigation",[],{"code":248,"score":154,"supported":42,"title":249,"abstract":250,"tags":252},"H7",[],[251],"洪涝",[221],{"code":254,"score":255,"supported":42,"title":256,"abstract":257,"tags":259},"H2",3,[],[258],"土壤",[],{"code":56,"score":22,"supported":14,"title":261,"abstract":262,"tags":263},[],[],[27],[265,266],"Guyana Mahaica Mahaicony 洪水","沿海稻田 排水规划","GuyanaMahaicaMahaicony洪水-5395","10.5281\u002Fzenodo.23451582",{"doi":268,"openalex_id":270,"authors":271,"venue":213,"cited_by_count":65,"oa_url":210,"card":279,"direction":204,"ingested_from":93},"W7222952224",[272,274,276],{"name":273,"orcid":9},"Omesh Persaud",{"name":275,"orcid":9},"Colis Allen",{"name":277,"orcid":278},"Ioana Popescu","https:\u002F\u002Forcid.org\u002F0000-0002-1009-5424",{"tldr":280,"method":281,"finding":282,"direction":283,"opportunity":284},"提出低信息框架，为数据稀缺农业区规划洪水韧性并做初步水力设计。","降雨频率分析、遥感DEM与土地利用、简化径流计算、利益相关者参与及一维水力模型。","上游湿地滞蓄与分区排水可降低峰值流量，支撑分阶段投资规划。","农业绿色发展与碳","可探索低监测区遥感与参与式数据融合的洪水韧性规划方法及作物淹水风险量化。","2026-10-20T23:30:19.345049Z",{"id":287,"title":288,"url":289,"summary":290,"summary_zh":291,"content":9,"source_name":292,"source_url":289,"published_at":293,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":294,"score_detail":295,"sources":298,"tags":300,"primary_topic":9,"secondary_topics":304,"classification":305,"search_phrases":317,"slug":320,"view_count":154,"doi":321,"paper":322,"created_at":357},5174,"Remote sensing of aquatic nutrients from proxy estimation to integration with process models","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs43247-026-04152-0","Leveraging its capability for large-scale, repeated monitoring, remote sensing has increasingly supported nitrogen and phosphorus estimation in surface waters. However, the evolution of this field from proxy-based nutrient retrieval to integration with process-based models remains insufficiently synthesized. Here we review remote sensing approaches for estimating aquatic nutrients, with emphasis on their roles in supporting process-based modeling, and compare their methodologies, applications, and limitations. At the watershed scale, remote sensing supports land use mapping and pollution load estimation, identifying nutrient sources and constraining transport simulations. At the lake scale, it provides continuous observations and water quality products that inform hydrodynamic and water quality models. At the field and interface scales, remote sensing helps identify hotspots of nutrient loss by capturing the heterogeneity of management practices. Despite these advances, nutrient estimation remains constrained by proxy relationships and spatial scale. Future research should integrate remote sensing with hydrodynamic and biogeochemical models to constrain nutrient fluxes and improve mechanistic understanding of aquatic nutrient cycling. At the watershed scale, remote sensing maps land use and estimates pollution loads; at the lake scale, it provides water quality data for models; at field and interface scales, it identifies nutrient-loss hotspots, based on a review of remote sensing to approaches aquatic nutrients.","凭借大规模、重复监测的能力，遥感日益支撑地表水中氮磷的估算。然而，该领域从基于代理指标的养分反演向与过程模型融合的演进仍缺乏充分综合。本文综述了用于估算水生养分的遥感方法，重点阐述其在支撑过程模型方面的作用，并比较其方法、应用与局限。在流域尺度，遥感支撑土地利用制图与污染负荷估算，识别养分来源并约束输移模拟。在湖泊尺度，遥感提供连续观测与水质产品，为水动力与水质模型提供输入。在田块与界面尺度，遥感通过捕捉管理措施的异质性，帮助识别养分流失热点。尽管取得上述进展，养分估算仍受代理关系与空间尺度的制约。未来研究应将遥感与水动力及生物地球化学模型相结合，以约束养分通量并提升对水生养分循环机理的认识。在流域尺度，遥感可制图土地利用并估算污染负荷；在湖泊尺度，遥感为模型提供水质数据；在田块与界面尺度，遥感可识别养分流失热点——上述结论基于对水生养分遥感方法的综述。","Communications Earth & Environment","2026-10-15T00:00:00Z",80,{"impact":17,"substance":107,"depth":17,"authority":296,"freshness":21,"relevant":22,"comment":297},14,"系统综述遥感估算水体氮磷及其与过程模型耦合的进展与局限，方法学价值明确，但属综述类论文，公共影响有限。",[299],{"name":292,"url":289},[29,30,301,302,303],"水环境","面源污染","氮磷养分",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":306},{"candidates":307,"agricultural_evidence":14,"reason":60},[308,312],{"code":40,"score":134,"supported":42,"title":309,"abstract":310,"tags":311},[45],[29,45],[29],{"code":313,"score":22,"supported":14,"title":314,"abstract":315,"tags":316},"H5",[],[],[302],[318,319],"遥感 水体营养盐 估算","氮磷 遥感 过程模型","遥感水体营养盐估算-5174","10.1038\u002Fs43247-026-04152-0",{"doi":321,"openalex_id":323,"authors":324,"venue":292,"cited_by_count":65,"oa_url":351,"card":352,"direction":204,"ingested_from":93},"W7221994887",[325,327,330,333,335,337,339,341,343,345,347,349],{"name":326,"orcid":9},"Chenyi Shi",{"name":328,"orcid":329},"Xihua Wang","https:\u002F\u002Forcid.org\u002F0000-0002-4795-304X",{"name":331,"orcid":332},"Y. Jun Xu","https:\u002F\u002Forcid.org\u002F0000-0003-3646-626X",{"name":334,"orcid":9},"Xingyu Zhu",{"name":336,"orcid":9},"Shunqing Jia",{"name":338,"orcid":9},"Zejun Liu",{"name":340,"orcid":9},"Boyang Mao",{"name":342,"orcid":9},"Yan Dai",{"name":344,"orcid":9},"Yanxin Rong",{"name":346,"orcid":9},"Qinya Lv",{"name":348,"orcid":9},"Xuming Ji",{"name":350,"orcid":9},"Chengming Luo","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs43247-026-04152-0.pdf",{"tldr":353,"method":354,"finding":355,"direction":204,"opportunity":356},"综述遥感估算水体氮磷的方法，及其与过程模型在多尺度上的集成。","文献综述，按流域、湖泊、田块界面三个尺度比较遥感方法与局限。","遥感仍受代理关系与空间尺度制约，需与水文和生物地球化学模型集成。","可研究遥感与过程模型耦合以约束营养盐通量，填补多尺度集成与机理理解的空白。","2026-10-17T23:30:29.898801Z",{"id":359,"title":360,"url":361,"summary":362,"summary_zh":9,"content":9,"source_name":363,"source_url":361,"published_at":364,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":365,"score_detail":366,"sources":369,"tags":371,"primary_topic":374,"secondary_topics":378,"classification":379,"search_phrases":400,"slug":403,"view_count":22,"doi":404,"paper":405,"created_at":417},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":367,"substance":18,"depth":19,"authority":296,"freshness":21,"relevant":22,"comment":368},16,"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。",[370],{"name":363,"url":361},[114,28,372,29,373],"农业人工智能","大麦",{"code":146,"slug":375,"name":376,"path":377,"parent_code":128},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":130,"locked":14,"version":35,"method":36,"evidence":380},{"candidates":381,"agricultural_evidence":42},[382,386,391,396],{"code":146,"score":154,"supported":42,"title":383,"abstract":384,"tags":385},[],[150],[114],{"code":387,"score":22,"supported":14,"title":388,"abstract":389,"tags":390},"A5",[],[],[373],{"code":392,"score":22,"supported":14,"title":393,"abstract":394,"tags":395},"I2",[],[],[372],{"code":40,"score":22,"supported":14,"title":397,"abstract":398,"tags":399},[],[],[29],[401,402],"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":404,"openalex_id":406,"authors":407,"venue":363,"cited_by_count":65,"oa_url":9,"card":9,"direction":9,"ingested_from":93},"W7221758985",[408,411,414],{"name":409,"orcid":410},"Ameer Tamoor Khan","https:\u002F\u002Forcid.org\u002F0000-0001-6838-992X",{"name":412,"orcid":413},"Saiful Azim","https:\u002F\u002Forcid.org\u002F0000-0002-2686-7150",{"name":415,"orcid":416},"Signe Marie Jensen","https:\u002F\u002Forcid.org\u002F0000-0002-7738-5231","2026-10-15T23:30:02.421798Z",{"id":419,"title":420,"url":421,"summary":422,"summary_zh":423,"content":9,"source_name":424,"source_url":421,"published_at":425,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":294,"score_detail":426,"sources":428,"tags":430,"primary_topic":9,"secondary_topics":434,"classification":435,"search_phrases":452,"slug":455,"view_count":65,"doi":456,"paper":457,"created_at":477},4610,"UAV-based estimation of aboveground biomass carbon in the Sundarbans mangrove forest of Bangladesh","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2515-7620\u002Faeb18f","Abstract Reliable biomass and carbon assessments are essential for understanding mangrove forest dynamics and their role in global carbon cycling. However, field-based biomass estimation is time-consuming, labour-intensive, and costly. Remote sensing provides a practical and cost-effective alternative, particularly with unmanned aerial vehicles (UAVs) that can capture high-resolution imagery. The assessment of biomass carbon in the Sundarbans Mangrove Forest (SMF) using high-resolution remote sensing remains understudied. This study develops an integrated carbon assessment framework for estimating aboveground biomass carbon in the SMF using UAV-derived canopy metrics and field measurements. Error propagation and feature importance analyses were performed to quantify uncertainty and assess predictor influence, providing a robust and uncertainty-aware approach for mangrove biomass carbon assessment. CPA was used to estimate proxy DBH using linear, polynomial, logarithmic, power, and exponential regression models. All CPA–DBH models showed comparable performance (R2 = 0.73-0.75). The linear and polynomial models achieved the lowest RMSE (1.77 cm), while the polynomial model produced the lowest MAE (1.22 cm) and was therefore selected for estimating Proxy DBH. Field-based AGBC was calculated from measured tree height and DBH using species-specific and generic allometric equations applicable to the Sundarbans, whereas UAV-based AGBC was estimated using UAV-derived Mean DSM Height and CPA-derived Proxy DBH. Simple linear regression (SLR), multiple linear regression (MLR), and machine-learning (ML) models (Random Forest, Support Vector Machine, Artificial Neural Network, and Extreme Gradient Boosting) were evaluated for AGBC prediction. The MLR model showed the best performance on the validation\u002Fmodel-selection subset (R² = 0.86, RMSE = 14.70 Mg C ha⁻¹, MedAE = 4.58 Mg C ha⁻¹). UAV-derived AGBC estimates closely matched field measurements and previously reported values for the Sundarbans, demonstrating the potential of high-resolution UAV metrics for reliable mangrove AGBC estimation and supporting carbon monitoring, REDD+ initiatives, and sustainable mangrove management in Bangladesh.","可靠的生物量和碳评估对于理解红树林动态及其在全球碳循环中的作用至关重要。然而，基于实地调查的生物量估算耗时、费力且成本高昂。遥感提供了一种实用且具有成本效益的替代方案，尤其是利用能够获取高分辨率影像的无人机（UAV）。利用高分辨率遥感对孙德尔本斯红树林（SMF）进行生物量碳评估的研究仍较为缺乏。本研究开发了一个综合碳评估框架，利用无人机获取的冠层指标和实地测量数据估算SMF的地上生物量碳。通过误差传播和特征重要性分析量化不确定性并评估预测因子的影响，为红树林生物量碳评估提供了一种稳健且考虑不确定性的方法。采用CPA（冠层投影面积）通过线性、多项式、对数、幂函数和指数回归模型估算代理DBH（胸径）。所有CPA–DBH模型均表现出相近的性能（R² = 0.73–0.75）。线性模型和多项式模型取得了最低的RMSE（1.77 cm），而多项式模型产生了最低的MAE（1.22 cm），因此被选用于估算代理DBH。基于实地的AGBC（地上生物量碳）通过实测树高和DBH，采用适用于孙德尔本斯的物种特异性异速生长方程和通用异速生长方程计算，而基于无人机的AGBC则利用无人机获取的平均DSM高度和CPA衍生的代理DBH进行估算。采用简单线性回归（SLR）、多元线性回归（MLR）和机器学习（ML）模型（随机森林、支持向量机、人工神经网络和极端梯度提升）评估AGBC预测性能。MLR模型在验证\u002F模型选择子集上表现最佳（R² = 0.86，RMSE = 14.70 Mg C ha⁻¹，MedAE = 4.58 Mg C ha⁻¹）。无人机获取的AGBC估算值与实地测量结果及先前报道的孙德尔本斯数值高度吻合，表明高分辨率无人机指标在可靠估算红树林AGBC方面具有潜力，可支持孟加拉国的碳监测、REDD+倡议和可持续红树林管理。","Environmental Research Communications","2026-10-07T00:00:00Z",{"impact":17,"substance":107,"depth":17,"authority":296,"freshness":21,"relevant":22,"comment":427},"该研究利用无人机高分辨率影像与机器学习模型估算孟加拉国孙德尔本斯红树林地上生物量碳，方法新颖、数据可靠，对林业碳汇监测与REDD+具有参考价值，但属境外案例、应用范围有限，可作为专业资讯收录。",[429],{"name":424,"url":421},[28,29,431,432,433],"智慧林业","红树林","碳汇计量",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":436},{"candidates":437,"agricultural_evidence":14,"reason":60},[438,442,447],{"code":40,"score":41,"supported":42,"title":439,"abstract":440,"tags":441},[],[29,45],[29],{"code":392,"score":255,"supported":42,"title":443,"abstract":444,"tags":446},[],[445],"机器学习",[],{"code":448,"score":22,"supported":14,"title":449,"abstract":450,"tags":451},"H6",[],[],[432],[453,454],"孟加拉国 孙德尔本斯 红树林 无人机","无人机 生物量碳 遥感估算","孟加拉国孙德尔本斯红树林无人机-4610","10.1088\u002F2515-7620\u002Faeb18f",{"doi":456,"openalex_id":458,"authors":459,"venue":424,"cited_by_count":65,"oa_url":471,"card":472,"direction":283,"ingested_from":93},"W7220935167",[460,463,466,468],{"name":461,"orcid":462},"Md Abul Hashem","https:\u002F\u002Forcid.org\u002F0009-0006-2712-3168",{"name":464,"orcid":465},"Md Sarwar Hossain","https:\u002F\u002Forcid.org\u002F0000-0001-6101-659X",{"name":467,"orcid":9},"Md. Tauhidor Rahaman",{"name":469,"orcid":470},"Brian Barrett","https:\u002F\u002Forcid.org\u002F0000-0002-4380-4020","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2515-7620\u002Faeb18f\u002Fpdf",{"tldr":473,"method":474,"finding":475,"direction":204,"opportunity":476},"利用无人机冠层指标与地面数据估算孟加拉孙德尔本斯红树林地上生物量碳。","无人机高分辨率影像提取冠层指标，结合CPA估算DBH，用MLR与机器学习模型预测","多元线性回归表现最佳（R²=0.86），无人机估算值与实测及历史值高度吻合。","可探索多时相无人机与卫星融合，提升红树林碳储量动态监测及不确定性量化能力。","2026-10-10T23:31:07.399747Z",{"id":479,"title":480,"url":481,"summary":482,"summary_zh":483,"content":9,"source_name":484,"source_url":481,"published_at":485,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":486,"score_detail":487,"sources":489,"tags":491,"primary_topic":493,"secondary_topics":494,"classification":499,"search_phrases":528,"slug":531,"view_count":65,"doi":532,"paper":533,"created_at":554},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":108,"substance":17,"depth":367,"authority":20,"freshness":109,"relevant":22,"comment":488},"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。",[490],{"name":484,"url":481},[114,28,372,29,492],"葡萄园管理",{"code":40,"slug":125,"name":126,"path":127,"parent_code":128},[495],{"code":392,"slug":496,"name":497,"path":498,"parent_code":128},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":130,"locked":14,"version":35,"method":36,"evidence":500},{"candidates":501,"agricultural_evidence":42},[502,506,512,516,523],{"code":40,"score":17,"supported":42,"title":503,"abstract":504,"tags":505},[142],[29,45,142],[29],{"code":392,"score":134,"supported":42,"title":507,"abstract":509,"tags":511},[508],"deep learning",[510,508],"深度学习",[372],{"code":146,"score":41,"supported":42,"title":513,"abstract":514,"tags":515},[],[149,150],[114],{"code":517,"score":49,"supported":42,"title":518,"abstract":519,"tags":522},"B2",[],[520,521],"葡萄","grape",[],{"code":48,"score":49,"supported":42,"title":524,"abstract":525,"tags":527},[],[52,526],"GIS",[],[529,530],"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":532,"openalex_id":534,"authors":535,"venue":484,"cited_by_count":65,"oa_url":481,"card":549,"direction":204,"ingested_from":93},"W7220993218",[536,538,541,543,546],{"name":537,"orcid":9},"Dimitra Passa",{"name":539,"orcid":540},"Spyridon E. Detsikas","https:\u002F\u002Forcid.org\u002F0000-0002-3960-9361",{"name":542,"orcid":9},"Georgios Nektarios Tselos",{"name":544,"orcid":545},"George P. Petropoulos","https:\u002F\u002Forcid.org\u002F0000-0003-1442-1423",{"name":547,"orcid":548},"Antonis Litke","https:\u002F\u002Forcid.org\u002F0000-0002-7658-2559",{"tldr":550,"method":551,"finding":552,"direction":204,"opportunity":553},"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"]