[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4020":3,"related-4020":87},{"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":32,"secondary_topics":37,"classification":38,"search_phrases":61,"slug":64,"view_count":65,"doi":66,"paper":67,"created_at":86},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。",null,"Remote Sensing","2026-10-01T00:00:00Z","论文",10,false,65,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,18,16,14,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","无人机","农业遥感","小麦","精准农业",{"code":33,"slug":34,"name":30,"path":35,"parent_code":36},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,56],{"code":45,"score":17,"supported":46,"title":47,"abstract":48,"tags":50},"I1",true,[],[31,49],"precision agriculture",[27,31],{"code":33,"score":52,"supported":46,"title":53,"abstract":54,"tags":55},7,[],[30,34],[30],{"code":57,"score":22,"supported":14,"title":58,"abstract":59,"tags":60},"I3",[],[],[29],[62,63],"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",3,"10.3390\u002Frs18193361",{"doi":66,"openalex_id":68,"authors":69,"venue":10,"cited_by_count":78,"oa_url":6,"card":79,"direction":83,"ingested_from":85},"W7215065180",[70,72,75],{"name":71,"orcid":9},"Gudrun Kinz",{"name":73,"orcid":74},"Hermann Buerstmayr","https:\u002F\u002Forcid.org\u002F0000-0002-0748-2351",{"name":76,"orcid":77},"Peter M. Roth","https:\u002F\u002Forcid.org\u002F0000-0001-9566-1298",0,{"tldr":80,"method":81,"finding":82,"direction":83,"opportunity":84},"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。","农业遥感与作物表型","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。","openalex","2026-10-02T23:30:19.815764Z",{"total":88,"page":22,"page_size":88,"items":89},6,[90,161,268,323,402,472],{"id":91,"title":92,"url":93,"summary":94,"summary_zh":9,"content":9,"source_name":95,"source_url":93,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":96,"score_detail":97,"sources":101,"tags":103,"primary_topic":106,"secondary_topics":107,"classification":108,"search_phrases":126,"slug":129,"view_count":130,"doi":131,"paper":132,"created_at":160},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":98,"depth":99,"authority":20,"freshness":13,"relevant":22,"comment":100},21,17,"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。",[102],{"name":95,"url":93},[27,28,30,104,105],"遥感","光合能力",{"code":33,"slug":34,"name":30,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":109},{"candidates":110,"agricultural_evidence":46},[111,117,122],{"code":33,"score":112,"supported":46,"title":113,"abstract":115,"tags":116},23,[114,34],"winter wheat",[114,34],[30],{"code":45,"score":118,"supported":46,"title":119,"abstract":120,"tags":121},4,[],[49],[27],{"code":57,"score":22,"supported":14,"title":123,"abstract":124,"tags":125},[],[],[104],[127,128],"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",2,"10.1007\u002Fs11119-026-10452-4",{"doi":131,"openalex_id":133,"authors":134,"venue":95,"cited_by_count":78,"oa_url":9,"card":9,"direction":83,"ingested_from":85},"W7214905644",[135,137,140,143,146,148,150,153,156,158],{"name":136,"orcid":9},"Dongwei Han",{"name":138,"orcid":139},"Shaolong Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-2525-0095",{"name":141,"orcid":142},"Muhammad Zain","https:\u002F\u002Forcid.org\u002F0000-0002-8073-6282",{"name":144,"orcid":145},"Weijun Zhang","https:\u002F\u002Forcid.org\u002F0009-0006-1540-7382",{"name":147,"orcid":9},"Zhaokai Ma",{"name":149,"orcid":9},"Xintong Lu",{"name":151,"orcid":152},"Jianliang Wang","https:\u002F\u002Forcid.org\u002F0000-0001-5733-4202",{"name":154,"orcid":155},"Yuanyuan Zhao","https:\u002F\u002Forcid.org\u002F0000-0003-4202-6383",{"name":157,"orcid":9},"Tao Liu",{"name":159,"orcid":9},"Chengming Sun","2026-10-01T23:30:02.493554Z",{"id":162,"title":163,"url":164,"summary":165,"summary_zh":166,"content":9,"source_name":167,"source_url":164,"published_at":168,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":169,"score_detail":170,"sources":174,"tags":176,"primary_topic":178,"secondary_topics":179,"classification":185,"search_phrases":215,"slug":218,"view_count":65,"doi":219,"paper":220,"created_at":267},3354,"Optimizing canopy nitrogen retrieval in wheat from high-resolution UAV hyperspectral data using RTM-based Gaussian process regression","https:\u002F\u002Fdoi.org\u002F10.1080\u002F01904167.2026.2736627","The canopy nitrogen content (CNC) is one of the vital crop health indicators directly influencing the crop growth, yield, and nutritional status. Unmanned aerial vehicles (UAVs) that acquired hyperspectral imagery opened a new path for nondestructive, accurate, and timely estimation of CNC, facilitating the adoption of smart agro practices in precision agriculture for site-specific nutrient management. With the purpose of processing such hyperspectral data into CNC maps, two hybrid retrieval approaches, protein-based (CNCprot) and chlorophyll-based (CNCchl) models, were built by combining a radiative transfer model with a machine learning Gaussian process regression (GPR) algorithm. The GPR retrieval models were trained using synthetic spectral data from the PROSAIL-PRO model, and the sampling was optimized using the Euclidean distance-based diversity (EBD) active learning (AL) technique. The CNC models were applied to a UAV-borne hyperspectral image in the spectral range of 400-1000 nm with an ultrahigh spatial resolution of 4 cm, acquired over the experimental wheat field of Indian Council of Agricultural Research (ICAR) -Indian Agricultural Research Institute (IARI), New Delhi, India. The GPR models yielded good prediction accuracies with R2 values of 0.74 and 0.56 and NRMSE of 13.62 and 23.82% for CNCchl and CNCprot, respectively. The consistent prediction accuracies and low associated uncertainties revealed that CNC was more accurately mapped using chlorophyll-based CNC retrieval applied to a Vis-NIR-based hyperspectral data. These promising results demonstrated the possibility of applying hybrid GPR models at ultrahigh resolution and the operational viability of UAV datasets for regular CNC monitoring.","冠层氮含量（CNC）是直接影响作物生长、产量和营养状况的重要作物健康指标之一。无人机（UAV）获取的高光谱影像为无损、精准、及时地估算CNC开辟了新途径，有助于在精准农业中采用智能农业实践进行定点养分管理。为了将此类高光谱数据处理为CNC分布图，本研究通过将辐射传输模型与机器学习高斯过程回归（GPR）算法相结合，构建了两种混合反演方法：基于蛋白质的模型（CNCprot）和基于叶绿素的模型（CNCchl）。GPR反演模型使用PROSAIL-PRO模型生成的合成光谱数据进行训练，并采用基于欧氏距离的多样性（EBD）主动学习（AL）技术优化采样。CNC模型被应用于一幅无人机载高光谱影像，光谱范围为400-1000 nm，空间分辨率高达4 cm，该影像获取自印度农业研究理事会（ICAR）-印度农业研究所（IARI）位于印度新德里的实验麦田。GPR模型取得了良好的预测精度，CNCchl和CNCprot的R²值分别为0.74和0.56，NRMSE分别为13.62%和23.82%。一致的预测精度和较低的相关不确定性表明，将基于叶绿素的CNC反演应用于基于可见光-近红外（Vis-NIR）的高光谱数据能够更准确地制图CNC。这些有前景的结果证明了在超高分辨率下应用混合GPR模型的可能性，以及无人机数据集用于常规CNC监测的可行性。","Journal of Plant Nutrition","2026-09-23T00:00:00Z",79,{"impact":18,"substance":171,"depth":18,"authority":172,"freshness":17,"relevant":22,"comment":173},22,13,"将辐射传输模型与高斯过程回归结合，用4厘米超高分辨率无人机高光谱实现小麦冠层氮素精准反演，方法新颖、结论可靠，对精准农业养分管理有实操参考价值。",[175],{"name":167,"url":164},[27,28,30,104,177],"氮素监测",{"code":33,"slug":34,"name":30,"path":35,"parent_code":36},[180],{"code":57,"slug":181,"name":182,"path":183,"parent_code":184},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":186},{"candidates":187,"agricultural_evidence":46},[188,193,198,202,209],{"code":33,"score":189,"supported":46,"title":190,"abstract":191,"tags":192},12,[34],[34],[30],{"code":57,"score":189,"supported":46,"title":194,"abstract":196,"tags":197},[195],"hyperspectral",[195],[104],{"code":45,"score":52,"supported":46,"title":199,"abstract":200,"tags":201},[],[31,49],[27],{"code":203,"score":88,"supported":46,"title":204,"abstract":205,"tags":208},"I2",[],[206,207],"机器学习","machine learning",[],{"code":210,"score":65,"supported":46,"title":211,"abstract":212,"tags":214},"F3",[],[213],"养分管理",[],[216,217],"无人机 高光谱 小麦 氮素","PROSAIL 高斯过程回归 小麦","无人机高光谱小麦氮素-3354","10.1080\u002F01904167.2026.2736627",{"doi":219,"openalex_id":221,"authors":222,"venue":167,"cited_by_count":78,"oa_url":9,"card":261,"direction":266,"ingested_from":85},"W7214104831",[223,225,227,230,233,235,237,240,243,246,248,251,253,255,258],{"name":224,"orcid":9},"R. G. Rejith",{"name":226,"orcid":9},"Rabi N. Sahoo",{"name":228,"orcid":229},"Shalini Gakhar","https:\u002F\u002Forcid.org\u002F0000-0001-5717-1714",{"name":231,"orcid":232},"Jochem Verrelst","https:\u002F\u002Forcid.org\u002F0000-0002-6313-2081",{"name":234,"orcid":9},"Amrita Bhandari",{"name":236,"orcid":9},"Tarun Kondraju",{"name":238,"orcid":239},"Rajeev Ranjan","https:\u002F\u002Forcid.org\u002F0000-0003-2233-9147",{"name":241,"orcid":242},"Mahesh C. Meena","https:\u002F\u002Forcid.org\u002F0000-0001-5386-883X",{"name":244,"orcid":245},"Abir Dey","https:\u002F\u002Forcid.org\u002F0000-0002-5009-9518",{"name":247,"orcid":9},"Joydeep Mukherjee",{"name":249,"orcid":250},"Sudhir Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-1089-7435",{"name":252,"orcid":9},"Mahesh Kumar",{"name":254,"orcid":9},"Raju Dhandapani",{"name":256,"orcid":257},"Anchal Dass","https:\u002F\u002Forcid.org\u002F0000-0003-3909-1803",{"name":259,"orcid":260},"Viswanathan Chinnusamy","https:\u002F\u002Forcid.org\u002F0000-0003-2174-9064",{"tldr":262,"method":263,"finding":264,"direction":83,"opportunity":265},"用PROSAIL-PRO与高斯过程回归从无人机高光谱数据反演小麦冠层氮含量。","PROSAIL-PRO合成光谱训练GPR，EBD主动学习采样，4cm无人机高光谱","叶绿素基模型精度更高，R²达0.74，NRMSE为13.62%，优于蛋白基模型。","可探索多生育期、多品种迁移能力及主动学习采样策略对反演精度的提升空间。","智慧农业 \u002F 农业物联网","2026-09-24T23:30:10.311254Z",{"id":269,"title":270,"url":271,"summary":272,"summary_zh":9,"content":9,"source_name":273,"source_url":9,"published_at":274,"category":275,"cover_url":9,"hotness":13,"is_selected":46,"score":276,"score_detail":277,"sources":280,"tags":282,"primary_topic":285,"secondary_topics":286,"classification":287,"search_phrases":318,"slug":321,"view_count":65,"doi":9,"paper":9,"created_at":322},2841,"中国农科院资划所多因子监测实现小麦晚霜冻害大范围动态预警登European Journal of Agronomy","https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fmtxw\u002Fbe939456a5354c4da317420bf21577db.htm","近日,中国农业科学院农业资源与农业区划研究所农业遥感团队提出了多因子综合霜冻监测指数,提升小麦晚霜冻害监测精度。黄淮海平原是我国冬小麦核心产区,每年3至4月小麦拔节至抽穗期间频发的晚霜冻害严重威胁粮食安全。该指数综合高精度气象预报和冬小麦生长进程信息,兼顾低温强度、持续时间及不同生育期敏感性,并利用遥感土壤水分数据构建非线性调节系数,精准刻画湿润土壤对低温的缓冲效应,突破了气象站点的空间局限。","中国农业科学院 \u002F 中国科学报","2026-09-17T00:00:00Z","报道",87,{"impact":171,"substance":171,"depth":18,"authority":278,"freshness":13,"relevant":22,"comment":279},15,"国家级科研团队在核心期刊提出多因子霜冻监测指数，方法新颖、结论可靠，对黄淮海冬小麦防灾减灾有实质价值，值得进入每日精选。",[281],{"name":273,"url":271},[27,29,283,30,284],"粮食安全","农业气象",{"code":33,"slug":34,"name":30,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":288},{"candidates":289,"agricultural_evidence":46},[290,294,299,303,309,314],{"code":33,"score":189,"supported":46,"title":291,"abstract":292,"tags":293},[30],[30],[30],{"code":295,"score":118,"supported":46,"title":296,"abstract":297,"tags":298},"A8",[],[283],[283],{"code":57,"score":118,"supported":46,"title":300,"abstract":301,"tags":302},[],[104],[29],{"code":304,"score":65,"supported":46,"title":305,"abstract":306,"tags":308},"H2",[],[307],"土壤",[],{"code":310,"score":22,"supported":14,"title":311,"abstract":312,"tags":313},"H7",[],[],[284],{"code":45,"score":22,"supported":14,"title":315,"abstract":316,"tags":317},[],[],[27],[319,320],"农业气象 农业遥感 智慧农业 粮食安全","农业气象 农业遥感","农业气象农业遥感智慧农业粮食安全-2841","2026-09-18T00:03:28.607700Z",{"id":324,"title":325,"url":326,"summary":327,"summary_zh":328,"content":9,"source_name":329,"source_url":326,"published_at":330,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":169,"score_detail":331,"sources":334,"tags":336,"primary_topic":339,"secondary_topics":340,"classification":341,"search_phrases":360,"slug":363,"view_count":364,"doi":365,"paper":366,"created_at":401},2615,"A low-cost RGB image and point cloud integrated scanning system for wheat phenotyping","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112441","A low-cost RGB image and point cloud integrated scanning system for wheat phenotyping。Computers and Electronics in Agriculture","一种用于小麦表型分析的低成本RGB图像与点云集成扫描系统。","Computers and Electronics in Agriculture","2026-09-16T00:00:00Z",{"impact":18,"substance":332,"depth":99,"authority":20,"freshness":13,"relevant":22,"comment":333},20,"低成本RGB与点云融合的小麦表型扫描系统，方法新颖且面向育种实践，具备细分领域参考价值。",[335],{"name":329,"url":326},[27,29,30,337,338],"表型组学","三维点云",{"code":33,"slug":34,"name":30,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":342},{"candidates":343,"agricultural_evidence":46},[344,348,352,356],{"code":33,"score":278,"supported":46,"title":345,"abstract":346,"tags":347},[34],[30,34],[30],{"code":45,"score":22,"supported":14,"title":349,"abstract":350,"tags":351},[],[],[27],{"code":203,"score":22,"supported":14,"title":353,"abstract":354,"tags":355},[],[],[338],{"code":57,"score":22,"supported":14,"title":357,"abstract":358,"tags":359},[],[],[29],[361,362],"三维点云 农业遥感 智慧农业 表型组学","三维点云 农业遥感","三维点云农业遥感智慧农业表型组学-2615",5,"10.1016\u002Fj.compag.2026.112441",{"doi":365,"openalex_id":367,"authors":368,"venue":329,"cited_by_count":78,"oa_url":9,"card":396,"direction":83,"ingested_from":85},"W7213304555",[369,371,373,375,377,379,381,383,385,387,389,391,393],{"name":370,"orcid":9},"Yan Li",{"name":372,"orcid":9},"Suyi Liu",{"name":374,"orcid":9},"Xuerui Qi",{"name":376,"orcid":9},"Chuanjing Jin",{"name":378,"orcid":9},"Xiangxin Zhuang",{"name":380,"orcid":9},"Zetao Wang",{"name":382,"orcid":9},"Jinyong Li",{"name":384,"orcid":9},"Chengming Zhao",{"name":386,"orcid":9},"Xia Yao",{"name":388,"orcid":9},"Yongchao Tian",{"name":390,"orcid":9},"Yan Zhu",{"name":392,"orcid":9},"Weixing Cao",{"name":394,"orcid":395},"Xiaohu Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-5654-1445",{"tldr":397,"method":398,"finding":399,"direction":83,"opportunity":400},"开发低成本RGB与点云融合扫描系统，用于小麦表型测量。","低成本RGB相机与点云融合扫描系统，采集小麦三维表型数据。","该系统能低成本获取小麦RGB与点云数据，实现表型分析。","可探索低成本多模态表型系统在田间高通量育种和抗逆监测中的应用。","2026-09-16T23:30:01.907231Z",{"id":403,"title":404,"url":405,"summary":406,"summary_zh":407,"content":9,"source_name":329,"source_url":405,"published_at":408,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":409,"score_detail":410,"sources":412,"tags":414,"primary_topic":417,"secondary_topics":418,"classification":419,"search_phrases":434,"slug":437,"view_count":65,"doi":438,"paper":439,"created_at":471},2114,"Estimating wheat lodging fraction from Sentinel-1\u002F2 imagery using UAV-derived reference labels","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112410","Estimating wheat lodging fraction from Sentinel-1\u002F2 imagery using UAV-derived reference labels。Computers and Electronics in Agriculture","利用无人机获取的参考标签，从Sentinel-1\u002F2影像估算小麦倒伏比例。《计算机与电子农业》","2026-09-11T00:00:00Z",81,{"impact":18,"substance":98,"depth":18,"authority":20,"freshness":13,"relevant":22,"comment":411},"核心期刊论文，提出用Sentinel-1\u002F2结合无人机参考标签估算小麦倒伏比例，方法新颖、数据可靠，对农业遥感监测有实质参考价值。",[413],{"name":329,"url":405},[27,28,30,415,416],"遥感监测","作物倒伏",{"code":33,"slug":34,"name":30,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":420},{"candidates":421,"agricultural_evidence":46},[422,426,430],{"code":33,"score":278,"supported":46,"title":423,"abstract":424,"tags":425},[34],[30,34],[30],{"code":45,"score":22,"supported":14,"title":427,"abstract":428,"tags":429},[],[],[27],{"code":57,"score":22,"supported":14,"title":431,"abstract":432,"tags":433},[],[],[415],[435,436],"作物倒伏 智慧农业 遥感监测 无人机","作物倒伏 智慧农业","作物倒伏智慧农业遥感监测无人机-2114","10.1016\u002Fj.compag.2026.112410",{"doi":438,"openalex_id":440,"authors":441,"venue":329,"cited_by_count":78,"oa_url":405,"card":466,"direction":83,"ingested_from":85},"W7212131491",[442,444,447,449,452,454,456,459,460,463],{"name":443,"orcid":9},"Baoyuan Zhang",{"name":445,"orcid":446},"Tianxiang Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-0996-2586",{"name":448,"orcid":9},"Xingyu Liu",{"name":450,"orcid":451},"Chao Song","https:\u002F\u002Forcid.org\u002F0000-0002-0328-9239",{"name":453,"orcid":9},"Xiaoyuan Bao",{"name":455,"orcid":9},"Qian Sun",{"name":457,"orcid":458},"Yuchun Pan","https:\u002F\u002Forcid.org\u002F0000-0002-1163-5963",{"name":386,"orcid":9},{"name":461,"orcid":462},"Meiyan Shu","https:\u002F\u002Forcid.org\u002F0000-0002-1519-5520",{"name":464,"orcid":465},"Xiaohe Gu","https:\u002F\u002Forcid.org\u002F0000-0002-7102-1939",{"tldr":467,"method":468,"finding":469,"direction":83,"opportunity":470},"利用无人机标签结合Sentinel-1\u002F2影像估算小麦倒伏比例。","Sentinel-1\u002F2卫星影像与无人机参考标签，机器学习回归。","无人机标签可有效训练卫星影像模型，实现区域小麦倒伏比例估算。","可探索多时相卫星与无人机协同的倒伏早期预警及不同品种泛化能力。","2026-09-11T23:30:01.591874Z",{"id":473,"title":474,"url":475,"summary":476,"summary_zh":477,"content":9,"source_name":478,"source_url":475,"published_at":479,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":480,"score_detail":481,"sources":483,"tags":485,"primary_topic":488,"secondary_topics":489,"classification":495,"search_phrases":538,"slug":541,"view_count":78,"doi":542,"paper":543,"created_at":570},1725,"Sustainable Wheat Production Through Precision Agriculture: An LCA Case Study in Italy","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15091646","The growing demand for food is placing growing pressure on agricultural systems, raising concerns about their environmental sustainability. In this context, precision agriculture represents a promising approach for improving resource-use efficiency while maintaining high yields. This study aimed to assess the environmental performance of soft wheat production during the transition from conventional to smart and precision farming under real farm conditions. A Life Cycle Assessment (LCA) was conducted following ISO 14040 and 14044 standards using primary data collected from a farm located in Umbria (Italy). Two consecutive three-year production periods were compared: conventional farming (2019–2021) and smart and precision farming (2022–2024). Environmental impacts were assessed using the Environmental Footprint 3.1 method. The transition was associated with a 20.8% reduction in the overall environmental impact. This improvement reflects the combined effects of adopting smart and precision agriculture technologies together with the renewal of farm machinery, which results in lower fuel consumption and more efficient fertilizer and pesticide management. Most impact categories showed improvements, particularly climate change, ecotoxicity, and particulate matter. However, freshwater eutrophication increased due to changes in fertilization strategy, including reduced nitrogen fertilizer use and increased phosphorus fertilizer application. Sensitivity analyses confirmed the consistency of the results; despite some influence on yield variation, the overall comparison remained consistent under the tested scenarios. These findings highlight the potential of smart and precision agriculture to improve the environmental sustainability of soft wheat production under real farm conditions.","日益增长的粮食需求给农业系统带来了越来越大的压力，引发了对农业环境可持续性的担忧。在此背景下，精准农业（precision agriculture）代表了一种在保持高产的同时提高资源利用效率的有前景的方法。本研究旨在评估在真实农场条件下，软质小麦（soft wheat）生产从传统农业向智能与精准农业转型过程中的环境绩效。依据ISO 14040和ISO 14044标准，利用从意大利翁布里亚（Umbria）地区一个农场收集的一手数据，进行了生命周期评价（Life Cycle Assessment, LCA）。研究比较了两个连续的三生产周期：传统农业（2019—2021年）和智能与精准农业（2022—2024年）。采用环境足迹3.1方法（Environmental Footprint 3.1）评估环境影响。转型与总体环境影响降低20.8%相关联。这一改善反映了采用智能与精准农业技术以及农机更新的综合效应，从而降低了燃料消耗，并实现了肥料和农药的更高效管理。大多数影响类别均有所改善，尤其是气候变化、生态毒性和颗粒物。然而，淡水富营养化（freshwater eutrophication）有所增加，原因是施肥策略的变化，包括减少氮肥使用和增加磷肥施用。敏感性分析证实了结果的一致性；尽管产量变化对结果有一定影响，但在所测试情景下，总体比较结果保持一致。这些发现凸显了智能与精准农业在真实农场条件下改善软质小麦生产环境可持续性的潜力。","Land","2026-09-04T00:00:00Z",75,{"impact":18,"substance":171,"depth":18,"authority":172,"freshness":118,"relevant":22,"comment":482},"基于真实农场数据的LCA研究，量化了精准农业转型的环境效益，对农业可持续发展具有参考价值。",[484],{"name":478,"url":475},[27,30,31,486,487],"生命周期评价","意大利",{"code":33,"slug":34,"name":30,"path":35,"parent_code":36},[490],{"code":210,"slug":491,"name":492,"path":493,"parent_code":494},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"status":39,"locked":14,"version":40,"method":41,"evidence":496},{"candidates":497,"agricultural_evidence":46},[498,502,506,514,521,527,533],{"code":45,"score":19,"supported":46,"title":499,"abstract":500,"tags":501},[49],[31,49],[27,31],{"code":33,"score":278,"supported":46,"title":503,"abstract":504,"tags":505},[34],[30,34],[30],{"code":210,"score":21,"supported":46,"title":507,"abstract":508,"tags":513},[],[509,510,511,512],"施肥","肥料","fertilizer","fertilization",[],{"code":515,"score":88,"supported":46,"title":516,"abstract":517,"tags":520},"F4",[],[518,519],"农药","pesticide",[],{"code":310,"score":88,"supported":46,"title":522,"abstract":523,"tags":526},[],[524,525],"气候变化","climate change",[],{"code":528,"score":65,"supported":46,"title":529,"abstract":530,"tags":532},"F5",[],[531],"农机",[],{"code":534,"score":22,"supported":14,"title":535,"abstract":536,"tags":537},"H4",[],[],[486],[539,540],"生命周期评价 智慧农业 精准农业 意大利","生命周期评价 智慧农业","生命周期评价智慧农业精准农业意大利-1725","10.3390\u002Fland15091646",{"doi":542,"openalex_id":544,"authors":545,"venue":478,"cited_by_count":78,"oa_url":475,"card":564,"direction":266,"ingested_from":85},"W7208758947",[546,548,551,554,556,558,561],{"name":547,"orcid":9},"Claudia Camplone",{"name":549,"orcid":550},"Luisa Paolotti","https:\u002F\u002Forcid.org\u002F0000-0002-8264-1674",{"name":552,"orcid":553},"Francesco Romagnoli","https:\u002F\u002Forcid.org\u002F0000-0003-1814-7310",{"name":555,"orcid":9},"Sara Allahverdiveva",{"name":557,"orcid":9},"Renzo Santi",{"name":559,"orcid":560},"Lorenzo Covarelli","https:\u002F\u002Forcid.org\u002F0000-0002-4568-6160",{"name":562,"orcid":563},"Lucia Rocchi","https:\u002F\u002Forcid.org\u002F0000-0002-3386-2390",{"tldr":565,"method":566,"finding":567,"direction":568,"opportunity":569},"通过LCA评估意大利软小麦从传统向精准农业转型的环境影响，发现总影响降低20.8%。","基于农场实地数据，采用LCA（ISO标准）和Environmental Foot","精准农业结合机械更新使环境影响降低20.8%，但磷肥增加导致淡水富营养化上升。","农业绿色发展与碳","可研究精准农业下施肥策略优化，平衡产量与富营养化风险，或扩展至其他作物和地区。","2026-09-05T23:30:09.493129Z"]