[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3001":3,"related-3001":77},{"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":31,"secondary_topics":37,"classification":38,"search_phrases":61,"slug":64,"view_count":65,"doi":8,"paper":66,"created_at":76},3001,"UAV多光谱不同空间分辨率匹配春小麦多性状监测","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1811","天津师范大学张程程等联合天津市农科院农业资源与环境研究所，从原生0.07 m四波段UAV多光谱影像通过像素聚合重采样生成14种空间分辨率（0.07-3.03 m），耦合PROSAIL辐射传输模型与随机森林评估尺度依赖反演性能。研究揭示了叶面积指数（LAI）、叶绿素含量（Cab）和冠层水分含量（Cw）反演精度对空间分辨率的非单调响应，提出物候阶段自适应分辨率策略并开发Heterogeneity-Scale Game Model（HSGM）刻画最优聚合尺度形成机制。",null,"MDPI Agronomy 16(18):1811","2026-09-15T00:00:00Z","论文",10,false,74,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},15,22,18,13,6,1,"方法新颖、数据扎实的作物遥感反演研究，对精准农业变量施药与无人机监测有参考价值，但属细分领域学术进展，公共影响有限。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","精准农业","遥感","作物表型","春小麦",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[],{"status":39,"locked":13,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,50,56],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},8,true,[34],[],[],{"code":51,"score":52,"supported":13,"title":53,"abstract":54,"tags":55},"I1",2,[],[],[26,27],{"code":57,"score":52,"supported":13,"title":58,"abstract":59,"tags":60},"I3",[],[],[29,28],[62,63],"天津师范大学 春小麦 多光谱","UAV 多光谱 空间分辨率","天津师范大学春小麦多光谱-3001",4,{"doi":8,"openalex_id":8,"authors":67,"venue":8,"cited_by_count":68,"oa_url":8,"card":69,"direction":73,"ingested_from":75},[],0,{"tldr":70,"method":71,"finding":72,"direction":73,"opportunity":74},"用无人机多光谱重采样14种分辨率，结合PROSAIL与随机森林，研究春小麦多性状反演的空间尺度效应。","UAV四波段多光谱像素聚合重采样，耦合PROSAIL模型与随机森林反演LAI、C","反演精度对空间分辨率呈非单调响应，提出物候自适应分辨率策略与HSGM模型。","农业遥感与作物表型","可探索不同作物与物候下最优分辨率普适规律，并将尺度自适应策略嵌入实时无人机监测系统。","agent","2026-09-20T00:03:08.168753Z",{"total":20,"page":21,"page_size":20,"items":78},[79,167,227,278,332,396],{"id":80,"title":81,"url":82,"summary":83,"summary_zh":84,"content":8,"source_name":85,"source_url":82,"published_at":86,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":87,"score_detail":88,"sources":92,"tags":94,"primary_topic":97,"secondary_topics":103,"classification":105,"search_phrases":144,"slug":147,"view_count":135,"doi":148,"paper":149,"created_at":166},4693,"Scalable, cloud-native decision support system for Variable Rate Application (VRA) based on multi-year NDRE time series in arable precision agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23282433","This paper presents an automated, cloud-native geospatial Decision Support System (DSS) that generates variable rate prescription maps directly from Sentinel-2 satellite observations. Designed as an end-to-end arable field VRA architecture, the system operates completely autonomously with minimal input, requiring only the field boundary polygon, the crop phenology class (winter or spring crop), and the multi-year observation window. The computation engine streams Cloud-Optimized GeoTIFF (COG) rasters via asynchronous cloud connectivity through the Microsoft Planetary Computer (MPC) SpatioTemporal Asset Catalog (STAC) Application Programming Interface (API), enforcing data quality through multi-stage Scene Classification Layer (SCL) filtering. Instead of the rapidly saturating Normalized Difference Vegetation Index (NDVI), the model relies on the Normalized Difference Red Edge (NDRE) index, which accurately captures canopy chlorophyll and green biomass dynamics. For each native 10×10 m grid cell, the system extracts the 95th percentile (P95) peak value within a crop-specific reproductive phenological window, eliminating single-day snapshot noise while validating winter or spring growth trajectories via automated phenological ratio calculations. The decision and prescription engine is built upon two core agronomic safety pillars: An exponential empirical Bayesian stability shrinkage that dampens unstable, high-variance zones toward the safe field average, preventing the over-application of inputs on drought-prone areas. A trigonometric Horn slope attenuation derived from digital elevation models that systematically reduces application rates on erosion-prone steep slopes. Cell-level rates are distributed within the target tolerance range using a rank-allocation algorithm coupled with a Hamilton quota (largest remainder method) correction, guaranteeing strict integer-based machine compatibility and zero-sum conservation of the total field input budget. The technology was validated on a 139.03 ha commercial production field across four reference seasons (2022, 2023, 2024, and 2026) for Variable Rate Seeding (VRS) in winter wheat (Triticum aestivum L., 230 kg\u002Fha baseline seed rate) in a rolling hilly landscape in Hungary, benchmarked against conventional uniform application and conservative multi-year VRS planning. The primary quantitative findings are summarized below: · Failure of conventional uniform application: Across the 14,250 cells, the flat 230 kg\u002Fha rate was optimal on only 1.1% of the field area. · Input reallocation: The model reallocated 1,633.7 kg of seed (and 1,167.1 kg compared to conservative VRS) from unproductive areas into high-capacity zones (Figure 1.). Across 98.9% of the field, conventional uniform management generated systematic under- or over-seeding, inducing yield losses or wasted operational expenditure. · Drought mitigation: In temporally unstable zones subject to extreme weather risk (covering 33.6% of the field, 46.69 ha), Bayesian stability shrinkage reduced seed allocation by 495 kg compared to uniform seeding. Establishing lower canopy densities in these vulnerable zones mitigates intra-crop water competition and prevents early-season forced ripening (Lollato et al., 2017). · Erosion control: On steep slopes exceeding 12% inclination (accounting for 9.75% of the field, 13.56 ha), the Horn topographic attenuation engine reduced seed input by 553 kg relative to the fixed 230 kg\u002Fha baseline. This intervention minimizes surface runoff damage and agrochemical leaching. · Economic return: Spatially optimizing plant density based on pedological and topographic constraints delivers substantial yield gains. Reallocating seed across the field—without any expenditure on supplemental fertilizer—produces a projected yield increase of +0.20 to +0.40 t\u002Fha (+28 to +56 tons of grain across the evaluated field) (Kindred et al., 2016). At a baseline wheat price of €192\u002Ft (70,000 HUF\u002Ft), this generates an estimated net value of €5,376 to €10,752 (1.96 to 3.92M HUF) on this single 139 ha field.","本文提出了一种自动化、云原生的地理空间决策支持系统（DSS），可直接从Sentinel-2卫星观测数据生成变量施肥处方图。该系统设计为端到端的耕地变量施肥（VRA）架构，完全自主运行且输入需求极低，仅需田块边界多边形、作物物候类别（冬作或春作）以及多年观测窗口。计算引擎通过Microsoft Planetary Computer（MPC）时空资产目录（STAC）应用程序接口（API）以异步云连接方式流式传输云优化GeoTIFF（COG）栅格数据，并通过多阶段场景分类层（SCL）滤波确保数据质量。该模型不采用易快速饱和的归一化差异植被指数（NDVI），而是依赖归一化差异红边指数（NDRE），后者能够准确捕捉冠层叶绿素和绿色生物量动态。对于每个原生10×10 m网格单元，系统提取作物特定生殖物候窗口内的第95百分位数（P95）峰值，消除单日快照噪声，同时通过自动化物候比率计算验证冬作或春作生长轨迹。决策与处方引擎建立在两个核心农艺安全支柱之上：指数经验贝叶斯稳定性收缩，将不稳定的高方差区域向安全的田块平均值收缩，防止在易旱区域过量施用投入品；基于数字高程模型推导的三角函数Horn坡度衰减，系统性地降低易侵蚀陡坡上的施用量。单元级施用量通过秩分配算法结合Hamilton配额（最大余数法）校正，在目标容差范围内分配，确保严格的基于整数的机器兼容性和田块总投入预算的零和守恒。该技术已在匈牙利起伏丘陵地貌的139.03 ha商业化生产田块上，针对冬小麦（Triticum aestivum L.，基准播种量230 kg\u002Fha）的变量播种（VRS），在四个参考季节（2022、2023、2024和2026年）进行了验证，并以常规均一施用和保守的多年VRS规划为基准进行比较。主要定量结果总结如下：· 常规均一施用的失败：在14,250个单元中，统一的230 kg\u002Fha施","Zenodo (CERN European Organization for Nuclear Research)","2026-10-10T00:00:00Z",83,{"impact":18,"substance":89,"depth":90,"authority":19,"freshness":12,"relevant":21,"comment":91},23,19,"基于多年度NDRE时序的云原生变量施肥决策系统，方法新颖、验证数据扎实，对精准农业信息化有较高参考价值。",[93],{"name":85,"url":82},[95,26,96,27,28],"数字农业","变量施肥",{"code":98,"slug":99,"name":100,"path":101,"parent_code":102},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[104],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":13,"version":40,"method":41,"evidence":106},{"candidates":107,"agricultural_evidence":46},[108,114,119,125,133,140],{"code":51,"score":109,"supported":46,"title":110,"abstract":112,"tags":113},11,[111],"precision agriculture",[],[95,26,27],{"code":98,"score":109,"supported":46,"title":115,"abstract":117,"tags":118},[116],"decision support",[116],[],{"code":32,"score":120,"supported":46,"title":121,"abstract":122,"tags":124},9,[],[34,123,33],"winter wheat",[],{"code":126,"score":127,"supported":46,"title":128,"abstract":129,"tags":132},"F3",7,[],[130,131],"施肥","fertilizer",[96],{"code":134,"score":135,"supported":46,"title":136,"abstract":137,"tags":139},"H7",3,[],[138],"drought",[],{"code":57,"score":21,"supported":13,"title":141,"abstract":142,"tags":143},[],[],[28],[145,146],"Sentinel-2 NDRE 变量施肥","云原生 决策支持系统 精准农业","Sentinel-2NDRE变量施肥-4693","10.5281\u002Fzenodo.23282433",{"doi":148,"openalex_id":150,"authors":151,"venue":85,"cited_by_count":68,"oa_url":82,"card":159,"direction":164,"ingested_from":165},"W7221365319",[152,154,156],{"name":153,"orcid":8},"Zoltán Szenek",{"name":155,"orcid":8},"Donát Szám",{"name":157,"orcid":158},"Gábor Milics","https:\u002F\u002Forcid.org\u002F0000-0002-4499-8322",{"tldr":160,"method":161,"finding":162,"direction":73,"opportunity":163},"构建云原生决策支持系统，用多年NDRE时序自动生成精准农业变量施肥\u002F播种处方图。","Sentinel-2 NDRE、COG\u002FSTAC云流式处理、贝叶斯收缩与坡度衰减","在匈牙利139公顷冬小麦田验证，系统优于传统均一施用与保守多年VRS规划。","可探索将NDRE时序DSS扩展至多作物、多区域及实时物联网数据融合的通用处方框架。","数字乡村与农业信息化","openalex","2026-10-11T23:30:36.790037Z",{"id":168,"title":169,"url":170,"summary":171,"summary_zh":8,"content":8,"source_name":172,"source_url":8,"published_at":173,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":174,"score_detail":175,"sources":178,"tags":180,"primary_topic":184,"secondary_topics":185,"classification":195,"search_phrases":215,"slug":218,"view_count":21,"doi":8,"paper":219,"created_at":226},5535,"基于无人机多光谱图像与VGG21模型的小麦渍害调控效果识别","https:\u002F\u002Ffinance.sina.cn\u002F2026-10-16\u002Fdetail-inivmprm8363597.d.html","江苏省农科院农业信息研究所联合中国农科院环发所与湖北省农科院粮作所，在小麦拔节至抽穗、抽穗至灌浆两个阶段设置对照、渍水胁迫、硅肥与氨基酸调控试验，用大疆精灵4采集冠层多光谱影像。VGG21模型对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","智慧农业（中英文）2026-10-21","2026-10-21T00:00:00Z",82,{"impact":18,"substance":17,"depth":18,"authority":176,"freshness":12,"relevant":21,"comment":177},14,"省级科研机构联合攻关，方法新颖、数据扎实，对渍害遥感识别与调控评估有实质参考价值，值得入选。",[179],{"name":172,"url":170},[26,181,29,182,183],"农业遥感","无人机多光谱","小麦渍害",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[186,190],{"code":57,"slug":187,"name":188,"path":189,"parent_code":102},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"code":134,"slug":191,"name":192,"path":193,"parent_code":194},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":39,"locked":13,"version":40,"method":41,"evidence":196},{"candidates":197,"agricultural_evidence":46},[198,202,206,211],{"code":32,"score":109,"supported":46,"title":199,"abstract":200,"tags":201},[34],[34],[],{"code":57,"score":109,"supported":46,"title":203,"abstract":204,"tags":205},[182],[],[29,181,182],{"code":134,"score":45,"supported":46,"title":207,"abstract":209,"tags":210},[208],"渍害",[],[],{"code":51,"score":21,"supported":13,"title":212,"abstract":213,"tags":214},[],[],[26],[216,217],"江苏省农科院 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硅肥调控","江苏省农科院小麦渍害多光谱-5535",{"doi":8,"openalex_id":8,"authors":220,"venue":8,"cited_by_count":68,"oa_url":8,"card":221,"direction":73,"ingested_from":75},[],{"tldr":222,"method":223,"finding":224,"direction":73,"opportunity":225},"用无人机多光谱图像和VGG21模型识别小麦渍害后硅肥与氨基酸调控效果。","大疆精灵4采集冠层多光谱影像，构建VGG21深度学习分类模型。","VGG21对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","可扩展多生育期、多调控组合与轻量化模型，探索渍害胁迫等级与调控效果的同步识别。","2026-10-22T00:07:10.070916Z",{"id":228,"title":229,"url":230,"summary":231,"summary_zh":8,"content":8,"source_name":232,"source_url":8,"published_at":233,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":234,"score_detail":235,"sources":240,"tags":242,"primary_topic":243,"secondary_topics":244,"classification":247,"search_phrases":266,"slug":269,"view_count":68,"doi":8,"paper":270,"created_at":277},5296,"雷添杰研究员团队《基于无人机多光谱图像和VGG21模型的小麦渍害调控效果识别》","https:\u002F\u002Ffinance.sina.com.cn\u002Froll\u002F2026-10-16\u002Fdoc-inivmprm8363597.shtml","《智慧农业(中英文)》2026年第8卷第4期刊载梁万杰、刘晓君、雷添杰等论文,在小麦拔节—抽穗和抽穗—灌浆两个生长阶段进行对照、渍水胁迫、硅肥调控、氨基酸调控控制试验;利用大疆精灵4多光谱无人机采集小麦冠层多光谱图像建立数据集,基于VGG21模型构建识别模型,发现硅肥调控样本识别性能最好,识别精度最高,具良好鲁棒性和可扩展性,可为小麦渍害综合防控提供理论与技术支持。","智慧农业(中英文)(2026,8(4))","2026-10-16T00:00:00Z",76,{"impact":236,"substance":237,"depth":238,"authority":176,"freshness":45,"relevant":21,"comment":239},16,21,17,"核心期刊论文，方法新颖、结论可靠，对小麦渍害防控有实用价值，值得进入每日精选。",[241],{"name":232,"url":230},[26,181,29,182,183],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[245,246],{"code":134,"slug":191,"name":192,"path":193,"parent_code":194},{"code":57,"slug":187,"name":188,"path":189,"parent_code":102},{"status":39,"locked":13,"version":40,"method":41,"evidence":248},{"candidates":249,"agricultural_evidence":46},[250,254,258,262],{"code":32,"score":109,"supported":46,"title":251,"abstract":252,"tags":253},[34],[34],[],{"code":134,"score":109,"supported":46,"title":255,"abstract":256,"tags":257},[208],[208],[],{"code":57,"score":109,"supported":46,"title":259,"abstract":260,"tags":261},[182],[],[29,181,182],{"code":51,"score":65,"supported":46,"title":263,"abstract":264,"tags":265},[],[26],[26],[267,268],"雷添杰 小麦渍害 无人机多光谱","VGG21 小麦渍害 识别模型","雷添杰小麦渍害无人机多光谱-5296",{"doi":8,"openalex_id":8,"authors":271,"venue":8,"cited_by_count":68,"oa_url":8,"card":272,"direction":73,"ingested_from":75},[],{"tldr":273,"method":274,"finding":275,"direction":73,"opportunity":276},"用无人机多光谱图像和VGG21模型识别小麦渍害调控效果。","大疆精灵4采集冠层多光谱图像，构建VGG21识别模型。","硅肥调控样本识别性能最好、精度最高，模型鲁棒可扩展。","可探索多生育期多光谱特征与不同调控措施的耦合机制，并扩展到其他作物渍害监测。","2026-10-19T00:07:48.071055Z",{"id":279,"title":280,"url":281,"summary":282,"summary_zh":283,"content":8,"source_name":284,"source_url":281,"published_at":285,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":286,"score_detail":287,"sources":289,"tags":291,"primary_topic":293,"secondary_topics":294,"classification":295,"search_phrases":310,"slug":313,"view_count":135,"doi":314,"paper":315,"created_at":331},4020,"Robust QR Code Detection in Oblique Aerial Images for Agricultural Field Experiments","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193361","Field experiments in precision agriculture require reliable linkage of plant variants to experimental plots, which is difficult in oblique aerial imagery because the drone position does not identify the imaged ground location. We present and evaluate a reproducible workflow for detecting QR reference markers in high-resolution UAV images acquired over a wheat field before crop growth at a 45° viewing angle. The workflow combines manually defined regions of interest, documented preprocessing, a rotation sweep, and independent evaluation of OpenCV and pyzbar decoding backends. QR codes were selected for practical field deployment because their payload can directly encode a plot identifier and can be checked with an ordinary smartphone; the study does not benchmark QR codes against other fiducial-marker families. In 500 field-collected QR-code crops from 20 images, 491 codes (98.2%) were decoded; all nine failures had incomplete code visibility. The observed result is therefore a system-level outcome of both image acquisition and decoding under the tested configuration, rather than a growing-season operational accuracy. The mean processing time was approximately 0.41 s per region of interest on a standard PC. The work demonstrates QR-marker detection under the stated pre-growth conditions and provides a basis for future plot-label reference, calibration, and downstream Fusarium Head Blight phenotyping.","精准农业中的田间试验需要将植物变异可靠地关联到试验小区，而这在倾斜航空影像中较为困难，因为无人机位置并不能标识所成像的地面位置。我们提出并评估了一套可重复的工作流程，用于在作物生长前以45°视角获取的小麦田高分辨率无人机影像中检测QR参考标记。该工作流程结合了人工定义的感兴趣区域、有记录的前处理步骤、旋转扫描，以及对OpenCV和pyzbar解码后端的独立评估。选择QR码用于实际田间部署，是因为其有效载荷可直接编码小区标识符，并可用普通智能手机进行核验；本研究并未将QR码与其他基准标记类型进行基准比较。在来自20幅影像的500个田间采集QR码裁剪区域中，491个码（98.2%）被成功解码；全部9个失败案例均存在码可见性不完整的问题。因此，所观察到的结果是测试配置下图像采集与解码共同作用的系统级结果，而非整个生长季的运行精度。在标准PC上，每个感兴趣区域的平均处理时间约为0.41 s。该工作证明了在上述生长前条件下QR标记检测的可行性，并为未来的小区标签参考、校准以及下游赤霉病（Fusarium Head Blight）表型分析提供了基础。","Remote Sensing","2026-10-01T00:00:00Z",65,{"impact":45,"substance":18,"depth":236,"authority":176,"freshness":120,"relevant":21,"comment":288},"该论文提出斜视无人机影像中QR码检测的可复现流程，在500个样本中解码成功率达98.2%，为精准农业田间试验的地块标识提供了实用方案，方法新颖且数据可靠，但属细分技术进展，影响范围有限。",[290],{"name":284,"url":281},[26,292,181,34,27],"无人机",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":296},{"candidates":297,"agricultural_evidence":46},[298,302,306],{"code":51,"score":45,"supported":46,"title":299,"abstract":300,"tags":301},[],[27,111],[26,27],{"code":32,"score":127,"supported":46,"title":303,"abstract":304,"tags":305},[],[34,33],[34],{"code":57,"score":21,"supported":13,"title":307,"abstract":308,"tags":309},[],[],[181],[311,312],"无人机 斜视影像 QR码 检测","小麦 田间试验 地块标识","无人机斜视影像QR码检测-4020","10.3390\u002Frs18193361",{"doi":314,"openalex_id":316,"authors":317,"venue":284,"cited_by_count":68,"oa_url":281,"card":326,"direction":73,"ingested_from":165},"W7215065180",[318,320,323],{"name":319,"orcid":8},"Gudrun Kinz",{"name":321,"orcid":322},"Hermann Buerstmayr","https:\u002F\u002Forcid.org\u002F0000-0002-0748-2351",{"name":324,"orcid":325},"Peter M. Roth","https:\u002F\u002Forcid.org\u002F0000-0001-9566-1298",{"tldr":327,"method":328,"finding":329,"direction":73,"opportunity":330},"提出并评估了在45°斜视无人机图像中检测田间二维码标记的可复现流程，解码成功率达98.2%。","结合手动ROI、预处理、旋转扫描及OpenCV与pyzbar双后端解码，使用50","491\u002F500个二维码成功解码，9个失败均因码可见性不完整，平均处理时间约0.41秒\u002FROI。","可探索斜视图像下多类基准标记的鲁棒检测与自动ROI定位，并接入生长季表型分析流程。","2026-10-02T23:30:19.815764Z",{"id":333,"title":334,"url":335,"summary":336,"summary_zh":8,"content":8,"source_name":337,"source_url":335,"published_at":285,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":338,"score_detail":339,"sources":341,"tags":343,"primary_topic":345,"secondary_topics":346,"classification":347,"search_phrases":362,"slug":365,"view_count":52,"doi":366,"paper":367,"created_at":395},3865,"Multisensor data fusion from uav platforms: a robust approach for estimating photosynthetic capacity across vertical leaf layers in winter wheat","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10452-4","Multisensor data fusion from uav platforms: a robust approach for estimating photosynthetic capacity across vertical leaf layers in winter wheat。Precision Agriculture","Precision Agriculture",80,{"impact":18,"substance":237,"depth":238,"authority":176,"freshness":12,"relevant":21,"comment":340},"该论文提出无人机多传感器数据融合方法估算冬小麦垂直叶层光合能力，方法新颖、数据可靠，对智慧农业遥感监测有参考价值，值得进入每日精选。",[342],{"name":337,"url":335},[26,292,34,28,344],"光合能力",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":348},{"candidates":349,"agricultural_evidence":46},[350,354,358],{"code":32,"score":89,"supported":46,"title":351,"abstract":352,"tags":353},[123,33],[123,33],[34],{"code":51,"score":65,"supported":46,"title":355,"abstract":356,"tags":357},[],[111],[26],{"code":57,"score":21,"supported":13,"title":359,"abstract":360,"tags":361},[],[],[28],[363,364],"无人机 多传感器 数据融合 冬小麦","光合能力 智慧农业 无人机 小麦","无人机多传感器数据融合冬小麦-3865","10.1007\u002Fs11119-026-10452-4",{"doi":366,"openalex_id":368,"authors":369,"venue":337,"cited_by_count":68,"oa_url":8,"card":8,"direction":73,"ingested_from":165},"W7214905644",[370,372,375,378,381,383,385,388,391,393],{"name":371,"orcid":8},"Dongwei Han",{"name":373,"orcid":374},"Shaolong Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-2525-0095",{"name":376,"orcid":377},"Muhammad Zain","https:\u002F\u002Forcid.org\u002F0000-0002-8073-6282",{"name":379,"orcid":380},"Weijun Zhang","https:\u002F\u002Forcid.org\u002F0009-0006-1540-7382",{"name":382,"orcid":8},"Zhaokai Ma",{"name":384,"orcid":8},"Xintong Lu",{"name":386,"orcid":387},"Jianliang Wang","https:\u002F\u002Forcid.org\u002F0000-0001-5733-4202",{"name":389,"orcid":390},"Yuanyuan Zhao","https:\u002F\u002Forcid.org\u002F0000-0003-4202-6383",{"name":392,"orcid":8},"Tao Liu",{"name":394,"orcid":8},"Chengming Sun","2026-10-01T23:30:02.493554Z",{"id":397,"title":398,"url":399,"summary":400,"summary_zh":401,"content":8,"source_name":402,"source_url":399,"published_at":403,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":404,"score_detail":405,"sources":409,"tags":411,"primary_topic":414,"secondary_topics":415,"classification":418,"search_phrases":450,"slug":453,"view_count":21,"doi":454,"paper":455,"created_at":482},3863,"Species identification of crops and weeds in wheat fields using hyperspectral data and machine learning algorithms","https:\u002F\u002Fdoi.org\u002F10.31489\u002F2026feb3\u002F137-151","Based on establishing the relationship between the morphological and anatomical properties of plant tissues and the recognition efficiency of the machine learning algorithm, a comprehensive assessment of the limitations of ML algorithms (using Random Forest as an example) in the multiclass identification of crops and weeds in the agroecosystems of northeastern Kazakhstan was carried out for the first time. The study substantiates the principal biological causes manifested in the optical overlap of classes in the hyperspectral feature space. Data collection was performed with a hyperspectral sensor in the VIS-NIR range. The analysis of spectral profiles was carried out based on variance distribution assessment using principal component analysis and the construction of reflectance curves. Classification of the extracted data was performed using the Random Forest algorithm. Spring wheat (Triticum aestivum) crops at different phenological phases and five dominant weed species (Euphorbia virgata, Descurainia sophia, Vicia hirsuta, Fallopia convolvulus, Echinochloa crus-galli) were selected as research objects. The algorithm demonstrated \"crop-weed\" segregation efficiency with an overall classification accuracy on test data of 72.94%. High identification accuracy was revealed for wheat (MCC=0.994) due to its spectral stability and pronounced red edge features (680-730 nm). Reduced accuracy in intra-class weed separation is driven by species-specific anatomy: thick cuticles in Euphorbia virgata increase reflectance, complex structure in Vicia hirsuta causes spectral dispersion, and mesophyll similarity between Echinochloa crus-galli and grasses leads to optical overlap with crop features. Thus, the model enables reliable crop-weed differentiation, a key requirement for precision agriculture and targeted agrochemical application.","在建立植物组织形态解剖特性与机器学习算法识别效率之间关系的基础上，首次对机器学习算法（以随机森林为例）在哈萨克斯坦东北部农业生态系统中作物与杂草多类识别方面的局限性进行了综合评估。研究证实了在高光谱特征空间中表现为类别光谱重叠的主要生物学成因。数据采集使用VIS-NIR波段的高光谱传感器完成。光谱剖面分析基于主成分分析的方差分布评估及反射率曲线构建进行。提取数据的分类采用随机森林算法完成。选取不同物候期的春小麦（Triticum aestivum）作物及五种优势杂草物种（Euphorbia virgata、Descurainia sophia、Vicia hirsuta、Fallopia convolvulus、Echinochloa crus-galli）作为研究对象。该算法在测试数据上实现了“作物-杂草”分离，总体分类精度为72.94%。小麦因其光谱稳定性及显著的红边特征（680-730 nm）而表现出较高的识别精度（MCC=0.994）。杂草类内分离精度降低由物种特异性解剖结构所致：Euphorbia virgata的厚角质层增加了反射率，Vicia hirsuta的复杂结构导致光谱离散，而Echinochloa crus-galli与禾本科植物之间的叶肉相似性导致其与作物特征发生光谱重叠。因此，该模型能够实现可靠的作物-杂草区分，这是精准农业和靶向农化施用的关键要求。","Fundamental and Experimental Biology","2026-09-29T00:00:00Z",70,{"impact":406,"substance":407,"depth":238,"authority":406,"freshness":120,"relevant":21,"comment":408},12,20,"高光谱结合机器学习实现麦田作物与杂草识别，方法新颖、结论具体，对精准农业有参考价值，但属细分领域研究，影响范围有限。",[410],{"name":402,"url":399},[26,412,34,413,28],"机器学习","杂草识别",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[416,417],{"code":98,"slug":99,"name":100,"path":101,"parent_code":102},{"code":57,"slug":187,"name":188,"path":189,"parent_code":102},{"status":39,"locked":13,"version":40,"method":41,"evidence":419},{"candidates":420,"agricultural_evidence":46},[421,425,430,435,439,445],{"code":32,"score":16,"supported":46,"title":422,"abstract":423,"tags":424},[33],[34,33],[34],{"code":98,"score":16,"supported":46,"title":426,"abstract":428,"tags":429},[427],"machine learning",[412,427],[412],{"code":57,"score":406,"supported":46,"title":431,"abstract":433,"tags":434},[432],"hyperspectral",[432],[28],{"code":51,"score":127,"supported":46,"title":436,"abstract":437,"tags":438},[],[27,111],[26],{"code":440,"score":135,"supported":46,"title":441,"abstract":442,"tags":444},"H6",[],[443],"农业生态",[],{"code":446,"score":21,"supported":13,"title":447,"abstract":448,"tags":449},"F4",[],[],[413],[451,452],"高光谱 小麦 杂草 识别","随机森林 作物 杂草 分类","高光谱小麦杂草识别-3863","10.31489\u002F2026feb3\u002F137-151",{"doi":454,"openalex_id":456,"authors":457,"venue":402,"cited_by_count":68,"oa_url":8,"card":476,"direction":481,"ingested_from":165},"W7214771122",[458,461,464,467,470,473],{"name":459,"orcid":460},"Rimma Meyramovna Ualiyeva","https:\u002F\u002Forcid.org\u002F0000-0003-3551-5007",{"name":462,"orcid":463},"Mariya M. Kaverina","https:\u002F\u002Forcid.org\u002F0000-0002-3838-7656",{"name":465,"orcid":466},"Александра Викторовна Осипова","https:\u002F\u002Forcid.org\u002F0000-0002-3947-5252",{"name":468,"orcid":469},"E. Koppayev","https:\u002F\u002Forcid.org\u002F0009-0004-0476-9017",{"name":471,"orcid":472},"М. А. Чайзабекова","https:\u002F\u002Forcid.org\u002F0009-0004-0468-956X",{"name":474,"orcid":475},"Sayan Berikovich Zhangazin","https:\u002F\u002Forcid.org\u002F0000-0002-5813-8331",{"tldr":477,"method":478,"finding":479,"direction":73,"opportunity":480},"利用高光谱数据与随机森林识别哈萨克斯坦东北部麦田作物与杂草，并分析误判的生物学原因。","VIS-NIR高光谱传感器采集数据，PCA分析光谱，随机森林分类春小麦与五种杂草","作物-杂草总体分类精度72.94%，小麦识别精度高（MCC=0.994），杂草种间分离受解剖结构影响","可探索融合形态解剖特征与深度学习，提升杂草种间分类精度，服务精准施药。","农业人工智能与决策模型","2026-09-30T23:30:54.952093Z"]