[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-341":3,"related-341":77},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":8,"category":10,"cover_url":8,"hotness":11,"is_selected":12,"score":13,"score_detail":14,"sources":22,"tags":24,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":71,"slug":74,"view_count":75,"doi":8,"paper":8,"created_at":76},341,"对地观测驱动的数字(智慧)农业研究前沿与应用案例","https:\u002F\u002Fcastjournals.cast.org.cn\u002Fjoweb\u002Fzhny\u002FCN\u002FY2026\u002FV8\u002FI2\u002F1","吴炳方、马慧、张淼、潘庆成、张翔、陈水森、邱炳文、徐新刚、刘建红、范锦龙、黄健熙、江佳乐、何昌垂在《智慧农业(中英文)》2026年第8卷第2期发表文章。数字农业的本质在于通过物联网、大数据、人工智能及遥感等信息技术,实现农业生产全过程的数字化映射与智能化管控。文章从对地观测视角系统剖析数字(智慧)农业研究前沿与典型案例:农情智慧监测(全口径作物制图、精准产量预测和灾害监测评估与预警)、数字孪生农场(全要素数字化建模、动态科学指导精准生产)、农业系统韧性(应对气候变化挑战)。结合农情遥感智能监测系统与农业水管理系统两个典型案例,展示数字(智慧)农业的云端服务能力,并分析影响商业化与业务化进程的多重阻力。",null,"智慧农业(中英文) 2026, 8(2): 1-17","论文",10,false,76,{"impact":15,"substance":16,"depth":17,"authority":18,"freshness":19,"relevant":20,"comment":21},22,20,18,14,2,1,"核心期刊综述，系统梳理对地观测驱动的智慧农业前沿与应用，具有较高学术价值和行业参考意义，但时效性较低。",[23],{"name":9,"url":6},[25,26,27,28,29,30,31],"数字农业","智慧农业","数字孪生","智慧农业8","对地观测","农情监测","农业系统韧性",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":40,"locked":12,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":48},[45,52,58,65],{"code":46,"score":47,"supported":48,"title":49,"abstract":50,"tags":51},"I1",12,true,[],[26,25,27],[25,27,26],{"code":33,"score":53,"supported":48,"title":54,"abstract":55,"tags":57},4,[],[56],"遥感",[30],{"code":59,"score":60,"supported":48,"title":61,"abstract":62,"tags":64},"H7",3,[],[63],"气候变化",[],{"code":66,"score":60,"supported":48,"title":67,"abstract":68,"tags":70},"I5",[],[69],"物联网",[],[72,73],"农业系统韧性 智慧农业8 农情监测 对地观测","农业系统韧性 智慧农业8","农业系统韧性智慧农业8农情监测对地观测-341",5,"2026-08-09T23:54:49.621624Z",{"total":78,"page":20,"page_size":78,"items":79},6,[80,162,268,334,376,478],{"id":81,"title":82,"url":83,"summary":84,"summary_zh":85,"content":8,"source_name":86,"source_url":83,"published_at":87,"category":10,"cover_url":8,"hotness":11,"is_selected":12,"score":88,"score_detail":89,"sources":94,"tags":96,"primary_topic":100,"secondary_topics":101,"classification":102,"search_phrases":135,"slug":138,"view_count":20,"doi":139,"paper":140,"created_at":161},4563,"Seeing Crops in 3D: Remote Sensing for Smarter Precision Farming","https:\u002F\u002Fdoi.org\u002F10.12688\u002Fopenreseurope.25171.1","The growing adoption of digital technologies in agriculture is opening new opportunities for improving productivity, sustainability and resource efficiency through data-driven decision making. This paper presents the integrated precision-agriculture framework developed by RFSAT Limited within the H2020 AgriBIT project and currently being further enhanced through the ENACT project. The framework combines Internet-of-Things (IoT) sensing, centimetre-accuracy Global Navigation Satellite System (GNSS) positioning, high-resolution multispectral remote sensing and Artificial Intelligence (AI) analytics into a single smart-farming solution. A key enabler is a low-cost, centimetre-accurate GNSS receiver developed by RFSAT, which allows precise georeferencing of aerial and ground observations and, in turn, the generation of high-resolution multispectral 3D models of crop canopies with spatial resolution exceeding 1 cm\u002Fpixel. These digital representations support detailed monitoring of crop growth, canopy development, biomass distribution and plant health throughout the season. Fused with an IoT monitoring infrastructure of environmental and microclimatic sensors, the georeferenced remote-sensing products form a comprehensive digital twin of the agricultural environment, over which machine-learning techniques automatically identify and localise crop anomalies including water stress, nutrient deficiencies, disease outbreaks and pest infestations. The technologies have been validated at operational pilot sites across Greece, Portugal and Italy, spanning orchards, vineyards and other high-value crops, where the combination of centimetre positioning, multispectral 3D modelling, IoT monitoring and AI analytics substantially improved monitoring accuracy, reduced input consumption and enhanced management decisions.","数字技术在农业中的日益普及，正通过数据驱动的决策为提升生产力、可持续性和资源效率开辟新的机遇。本文介绍了RFSAT有限公司在H2020 AgriBIT项目中开发的综合精准农业框架，该框架目前正通过ENACT项目进一步完善。该框架将物联网（IoT）传感、厘米级精度的全球导航卫星系统（GNSS）定位、高分辨率多光谱遥感以及人工智能（AI）分析整合为一个智慧农业解决方案。一个关键的使能因素是由RFSAT开发的低成本、厘米级精度GNSS接收器，它能够对航空和地面观测进行精确的地理参照，进而生成空间分辨率超过1厘米\u002F像素的作物冠层高分辨率多光谱三维模型。这些数字表示支持在整个生长季对作物生长、冠层发育、生物量分布和植物健康进行详细监测。通过与由环境和小气候传感器组成的物联网监测基础设施相融合，地理参照的遥感产品形成了农业环境的综合数字孪生，机器学习技术在此基础上自动识别和定位作物异常，包括水分胁迫、养分缺乏、病害暴发和虫害侵扰。这些技术已在希腊、葡萄牙和意大利的运营试点基地进行了验证，涵盖果园、葡萄园和其他高价值作物，其中厘米级定位、多光谱三维建模、物联网监测和AI分析的结合大幅提高了监测精度、降低了投入品消耗并改善了管理决策。","Open Research Europe","2026-10-09T00:00:00Z",80,{"impact":17,"substance":15,"depth":90,"authority":91,"freshness":92,"relevant":20,"comment":93},19,13,8,"欧盟H2020项目论文，融合厘米级GNSS、多光谱3D建模与AI分析，方法新颖且多国验证，对智慧农业技术路线有参考价值。",[95],{"name":86,"url":83},[26,97,98,27,99],"农业人工智能","农业遥感","精准农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":12,"version":41,"method":42,"evidence":103},{"candidates":104,"agricultural_evidence":48},[105,111,116,124,129],{"code":33,"score":17,"supported":48,"title":106,"abstract":108,"tags":110},[107],"remote sensing",[56,107,109],"multispectral",[98],{"code":46,"score":47,"supported":48,"title":112,"abstract":113,"tags":115},[],[26,99,27,114],"digital twin",[27,26,99],{"code":117,"score":118,"supported":48,"title":119,"abstract":120,"tags":123},"I2",7,[],[121,122],"机器学习","artificial intelligence",[97],{"code":66,"score":78,"supported":48,"title":125,"abstract":126,"tags":128},[],[69,127],"IoT",[],{"code":130,"score":60,"supported":48,"title":131,"abstract":132,"tags":134},"B2",[],[133],"葡萄",[],[136,137],"AgriBIT 精准农业 遥感","GNSS 多光谱 3D 作物","AgriBIT精准农业遥感-4563","10.12688\u002Fopenreseurope.25171.1",{"doi":139,"openalex_id":141,"authors":142,"venue":86,"cited_by_count":152,"oa_url":83,"card":153,"direction":159,"ingested_from":160},"W7221178461",[143,146,149],{"name":144,"orcid":145},"Artur Krukowski","https:\u002F\u002Forcid.org\u002F0000-0002-6635-7585",{"name":147,"orcid":148},"Emmanouela Vogiatzaki","https:\u002F\u002Forcid.org\u002F0000-0001-9239-2045",{"name":150,"orcid":151},"İzzet Kale","https:\u002F\u002Forcid.org\u002F0000-0001-5562-6885",0,{"tldr":154,"method":155,"finding":156,"direction":157,"opportunity":158},"提出融合物联网、厘米级GNSS、多光谱遥感和AI的精准农业框架，构建作物数字孪生。","低成本厘米级GNSS、多光谱3D建模、IoT传感器与机器学习，在希腊、葡萄牙、意","厘米定位与多光谱3D建模结合AI可提升监测精度、减少投入并改善管理决策。","农业遥感与作物表型","可探索低成本厘米级GNSS与多光谱3D数字孪生在更多作物和区域的可扩展性及实时决策优化。","智慧农业 \u002F 农业物联网","openalex","2026-10-10T23:30:07.913557Z",{"id":163,"title":164,"url":165,"summary":166,"summary_zh":167,"content":8,"source_name":168,"source_url":165,"published_at":169,"category":10,"cover_url":8,"hotness":11,"is_selected":12,"score":170,"score_detail":171,"sources":174,"tags":176,"primary_topic":178,"secondary_topics":179,"classification":180,"search_phrases":230,"slug":233,"view_count":60,"doi":234,"paper":235,"created_at":267},4091,"Supporting Digital Agriculture Through GeoAI : A Crop Type Dataset of Four Food Basket Crops in Rwanda","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fgdj3.70097","ABSTRACT Agriculture is a major economic activity, contributing approximately 25% of Rwanda's gross domestic product and employing nearly 65% of its population. Recognising its critical role, the Government of Rwanda has prioritised digital agriculture to manage the sector sustainably. Decision support systems based on remote sensing and Geospatial Artificial Intelligence (GeoAI) offer a transformative opportunity to enhance regional food security by providing real‐time data, improving agricultural forecasting and mitigating the impacts of climate variability. However, GeoAI approaches require labelled, ground‐referenced crop type data for model calibration and validation, which is scarce in sub‐Saharan Africa. To address this gap, we collected crop type data across Musanze, Nyabihu, Nyagatare and Ruhango districts of Rwanda between May and June 2025 to support the development of GeoAI crop monitoring approaches. The dataset, collected using KoboToolbox, integrates the Joint Experiment for Crop Assessment and Monitoring guidelines for field data collection and the Food and Agriculture Organisation's indicative crop classification. We targeted four food‐basket crops from Rwanda's Crop Intensification Programme: bean, maize, Irish potato and rice. In total, 2739 GPS‐located points were collected and used to generate 914 crop‐labelled polygons: 112 for beans, 107 for Irish potato, 417 for maize, 34 for rice and 244 for mixed‐crop fields. Polygons were digitised using four cardinal‐direction photographs taken during fieldwork and Planet imagery acquired during the season. Besides crop labels, the point data include detailed agronomic information such as farm management practices, anti‐erosion measures, cropping systems, agroforestry practices (including fruit‐tree presence), administrative units and GPS coordinates. This rich dataset provides robust, nuanced information for crop monitoring, yield estimation and digital agriculture innovation in Rwanda and beyond.","农业是一项主要的经济活动，贡献了卢旺达约25%的国内生产总值，并雇用了近65%的人口。认识到农业的关键作用，卢旺达政府已将数字农业列为优先事项，以实现该部门的可持续管理。基于遥感和地理空间人工智能（GeoAI）的决策支持系统提供了变革性机遇，可通过提供实时数据、改进农业预测和减轻气候变率的影响来增强区域粮食安全。然而，GeoAI方法需要标记的地面参考作物类型数据用于模型校准和验证，而这在撒哈拉以南非洲地区十分稀缺。为填补这一空白，我们于2025年5月至6月期间在卢旺达穆桑泽、尼亚比胡、尼亚加塔雷和鲁汉戈四个地区收集了作物类型数据，以支持GeoAI作物监测方法的开发。该数据集使用KoboToolbox收集，整合了作物评估与监测联合实验的野外数据收集指南以及联合国粮食及农业组织的指示性作物分类。我们针对卢旺达作物集约化计划中的四种粮篮作物：菜豆、玉米、爱尔兰马铃薯和水稻。共收集了2739个GPS定位点，用于生成914个作物标记多边形：112个菜豆、107个爱尔兰马铃薯、417个玉米、34个水稻和244个混合作物田块。多边形利用野外工作中拍摄的四个基本方位照片和当季获取的Planet影像进行数字化。除作物标签外，点位数据还包括详细的农艺信息，如农场管理实践、防侵蚀措施、种植制度、农林业实践（包括果树存在情况）、行政单元和GPS坐标。这一丰富的数据集为卢旺达及其他地区的作物监测、产量估算和数字农业创新提供了稳健而细致的信息。","Geoscience Data Journal","2026-10-01T00:00:00Z",78,{"impact":172,"substance":15,"depth":17,"authority":18,"freshness":92,"relevant":20,"comment":173},16,"基于GeoAI的卢旺达四类主粮作物地面标注数据集，2739个GPS点位、914个作物多边形，数据规模与方法规范性强，对遥感作物监测与数字农业研究有实质参考价值。",[175],{"name":168,"url":165},[25,26,97,98,177],"作物分类",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":12,"version":41,"method":42,"evidence":181},{"candidates":182,"agricultural_evidence":48},[183,188,192,197,204,211,218,225],{"code":46,"score":172,"supported":48,"title":184,"abstract":186,"tags":187},[185],"digital agriculture",[25,185],[25,26],{"code":33,"score":92,"supported":48,"title":189,"abstract":190,"tags":191},[],[56,107],[177,98],{"code":117,"score":118,"supported":48,"title":193,"abstract":194,"tags":196},[],[122,195],"decision support",[97],{"code":198,"score":78,"supported":48,"title":199,"abstract":200,"tags":203},"A1",[],[201,202],"水稻","rice",[],{"code":205,"score":78,"supported":48,"title":206,"abstract":207,"tags":210},"A3",[],[208,209],"玉米","maize",[],{"code":212,"score":78,"supported":48,"title":213,"abstract":214,"tags":217},"A5",[],[215,216],"马铃薯","potato",[],{"code":219,"score":78,"supported":48,"title":220,"abstract":221,"tags":224},"A8",[],[222,223],"粮食安全","food security",[],{"code":130,"score":60,"supported":48,"title":226,"abstract":227,"tags":229},[],[228],"果树",[],[231,232],"卢旺达 作物类型 数据集","GeoAI 作物监测 遥感","卢旺达作物类型数据集-4091","10.1002\u002Fgdj3.70097",{"doi":234,"openalex_id":236,"authors":237,"venue":168,"cited_by_count":152,"oa_url":165,"card":262,"direction":157,"ingested_from":160},"W7215812020",[238,241,243,246,248,250,252,255,257,259],{"name":239,"orcid":240},"Benson Kipkemboi Kenduiywo","https:\u002F\u002Forcid.org\u002F0000-0002-8448-0499",{"name":242,"orcid":8},"Stephen Sande",{"name":244,"orcid":245},"Anastasia Mumbi Wahome","https:\u002F\u002Forcid.org\u002F0000-0002-0471-0697",{"name":247,"orcid":8},"Joseph Chemutt",{"name":249,"orcid":8},"Ange Marie Tesire",{"name":251,"orcid":8},"Samuel Ishimwe",{"name":253,"orcid":254},"Yves Hategekimana","https:\u002F\u002Forcid.org\u002F0000-0002-2430-8072",{"name":256,"orcid":8},"Lilian Ndungu",{"name":258,"orcid":8},"Golo Rademacher",{"name":260,"orcid":261},"Aniruddha Ghosh","https:\u002F\u002Forcid.org\u002F0000-0003-3667-8019",{"tldr":263,"method":264,"finding":265,"direction":157,"opportunity":266},"在卢旺达四区采集四种主粮作物的GPS标注地块数据集，支撑GeoAI作物监测。","KoboToolbox结合JECAM指南与FAO分类，用Planet影像数字化9","获得2739个GPS点与914个作物标注多边形，含农艺管理信息。","可基于该数据集开发小样本\u002F迁移学习作物制图模型，并扩展至更多撒哈拉以南非洲国家。","2026-10-03T23:30:35.255650Z",{"id":269,"title":270,"url":271,"summary":272,"summary_zh":273,"content":8,"source_name":274,"source_url":271,"published_at":275,"category":10,"cover_url":8,"hotness":11,"is_selected":12,"score":276,"score_detail":277,"sources":280,"tags":282,"primary_topic":285,"secondary_topics":291,"classification":293,"search_phrases":310,"slug":313,"view_count":60,"doi":314,"paper":315,"created_at":333},3501,"A Dual-Phenological-Characteristic Weighting Method to Reconcile Time Discrepancies in Soybean Phenology Estimation from MODIS NDVI Time Series","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193300","Accurate large-scale monitoring of crop phenology is essential for optimizing agricultural management. Remote sensing has been widely used for estimating crop phenological stages, yet time discrepancies often exist between remotely sensed phenological metrics and ground-observed growth stages. Moreover, phenological parameters derived from different characterization models exhibit varying degrees of deviation from field observations. The primary goal of this study was to develop a novel method that fully exploits the deviation patterns of diverse phenological parameters to enhance the accuracy of soybean phenology retrieval. To this end, we extracted 11 phenological parameters for six key growth stages—emerged, blooming, pod-setting, turning yellow, dropping leaf, and harvest—of soybean across 16 U.S. states using MODIS NDVI (normalized difference vegetation index) time-series data from 2000 to 2020, employing GU-, curvature-, and derivative-based phenological modeling methods. The study design centered on proposing a dual-phenological-characteristic weighting (DPCW) method that leverages the deviation features of different phenological parameters relative to ground-observed growth stages, generating composite phenological characteristics by pairing two distinct parameters. The key innovation of this paper is the use of dual-feature weighting to improve the correspondence between satellite-derived phenometrics and field observations, offering an alternative to conventional phenological estimation. The results demonstrated that the optimal DPCW-based combinations for the six growth stages were SOS (start of season) and GREEN, SOS and POS (peak of season), MATURITY and POS, EOS (end of season) and SENES (senescence), RD (recession date) and DD (downturn date), and EOS and DORM (dormancy), respectively. The coefficient of determination (R2) between the retrieved transition dates and ground observations exceeded 0.65 for most stages, with the emerged stage improving to 0.47 from 0.052 and 0.357 of the unadjusted and offset-adjusted benchmarks. The average root mean square error (RMSE) was less than 5 days in most cases, representing a reduction of over 40%, with the most substantial improvement at the turning yellow stage, where RMSE dropped from 12.8 days to 2.8 days. A strength of this study lies in its multi-state, multi-decade validation, demonstrating the robustness and temporal consistency of the DPCW method within the major U.S. soybean-growing region. However, a limitation is that the method’s performance may vary with different satellite sensors or crop types, warranting further investigation. The proposed approach is expected to enhance the accuracy of remote sensing-based crop phenology monitoring and offers an effective alternative for calibrating remotely sensed phenological parameters.","准确的大尺度作物物候监测对于优化农业管理至关重要。遥感已被广泛用于估算作物物候阶段，但遥感物候指标与地面观测生育阶段之间常存在时间差异。此外，不同特征化模型衍生的物候参数与田间观测之间存在不同程度的偏差。本研究的主要目标是开发一种新方法，充分利用多种物候参数的偏差模式，以提高大豆物候反演精度。为此，我们利用2000—2020年MODIS NDVI（归一化差异植被指数）时间序列数据，采用基于GU、曲率和导数的方法提取了美国16个州大豆六个关键生育阶段——出苗、开花、结荚、黄化、落叶和收获——的11个物候参数。研究设计的核心是提出一种双物候特征加权（dual-phenological-characteristic weighting，DPCW）方法，该方法利用不同物候参数相对于地面观测生育阶段的偏差特征，通过配对两个不同参数生成复合物候特征。本文的关键创新在于利用双特征加权提高卫星衍生物候指标与田间观测之间的对应关系，为传统物候估算提供了一种替代方案。结果表明，六个生育阶段基于DPCW的最优组合分别为SOS（生长季开始）与GREEN、SOS与POS（生长季峰值）、MATURITY与POS、EOS（生长季结束）与SENES（衰老）、RD（衰退日期）与DD（下降日期）以及EOS与DORM（休眠）。反演得到的转换日期与地面观测之间的决定系数（R²）在大多数阶段超过0.65，其中出苗阶段从基准的0.052和偏移调整后的0.357提高至0.47。大多数情况下平均均方根误差（RMSE）小于5天，降幅超过40%，其中黄化阶段改善最为显著，RMSE从12.8天降至2.8天。本研究的一个优势在于其多州、多年代际验证，证明了DPCW方法在美国主要大豆种植区内的稳健性和时间一致性。","Remote Sensing","2026-09-24T00:00:00Z",81,{"impact":17,"substance":15,"depth":17,"authority":18,"freshness":278,"relevant":20,"comment":279},9,"提出双物候特征加权方法，用MODIS NDVI长时序数据校正大豆物候遥感估算偏差，方法新颖、验证扎实，对农情遥感监测有参考价值。",[281],{"name":274,"url":271},[26,283,30,56,284],"大豆","作物表型",{"code":286,"slug":287,"name":288,"path":289,"parent_code":290},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",[292],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":12,"version":41,"method":42,"evidence":294},{"candidates":295,"agricultural_evidence":48},[296,302,306],{"code":286,"score":297,"supported":48,"title":298,"abstract":300,"tags":301},15,[299],"soybean",[283,299],[283],{"code":33,"score":278,"supported":48,"title":303,"abstract":304,"tags":305},[],[56,107],[284,30,56],{"code":46,"score":20,"supported":12,"title":307,"abstract":308,"tags":309},[],[],[26],[311,312],"MODIS NDVI 大豆 物候","美国大豆 遥感 物候监测","MODISNDVI大豆物候-3501","10.3390\u002Frs18193300",{"doi":314,"openalex_id":316,"authors":317,"venue":274,"cited_by_count":152,"oa_url":271,"card":328,"direction":157,"ingested_from":160},"W7214203102",[318,320,323,326],{"name":319,"orcid":8},"Qiuxiang Yi",{"name":321,"orcid":322},"Siting Chen","https:\u002F\u002Forcid.org\u002F0000-0003-3468-9320",{"name":324,"orcid":325},"Fumin Wang","https:\u002F\u002Forcid.org\u002F0000-0002-5078-358X",{"name":327,"orcid":8},"Qinyan Zhu",{"tldr":329,"method":330,"finding":331,"direction":157,"opportunity":332},"提出双物候特征加权法，校正MODIS NDVI大豆物候估计与地面观测的时间偏差。","用MODIS NDVI 2000-2020数据，结合GU、曲率、导数三类物候模型","多数生育期R²超0.65，RMSE多小于5天，降幅超40%，转黄期RMSE从12.8天降至2.8天。","可将该加权校正思路迁移到其他作物与多源遥感数据，并探索自适应权重与深度学习融合的物候反演。","2026-09-25T23:30:31.075499Z",{"id":335,"title":336,"url":337,"summary":338,"summary_zh":8,"content":339,"source_name":340,"source_url":8,"published_at":341,"category":342,"cover_url":8,"hotness":11,"is_selected":12,"score":343,"score_detail":344,"sources":347,"tags":349,"primary_topic":352,"secondary_topics":353,"classification":354,"search_phrases":371,"slug":374,"view_count":19,"doi":8,"paper":8,"created_at":375},1787,"第四届数字农业学术研讨会在郑州召开——空天院等主办聚焦\"天空地一体化\"农情监测","https:\u002F\u002Faircas.cas.cn\u002Fdtxw\u002Fhzjl\u002F202609\u002Ft20260904_8278690.html","由中国科学院空天信息创新研究院、数字农业专业委员会主办的第四届数字农业学术研讨会在郑州召开。空天院吴炳方研究员强调数字农业需实现\"高分辨率化、平台化、终端化\"三个转化；西北农林科技大学副校长张宏鸣建议全球水蚀评价与FAO\u002FGSP等国际组织协同；北京师范大学潘耀忠教授剖析大尺度遥感作物制图工程化瓶颈。8月7-9日举办首届农情遥感技术培训班以CropWatch云平台开展实战教学。","8月9至11日，第四届数字农业学术研讨会在河南郑州召开。本次研讨会由国际数字地球学会中国国家委员会数字农业专业委员会、中国农学会农业信息分会、中国科学院空天信息创新研究院（空天院）、中国地理信息产业协会农业农村地理信息工作委员会、河南省农业科学院联合主办，河南省农业科学院农业信息技术研究所、遥感与数字地球全国重点实验室承办，以“数字技术驱动农业系统转型”为主题，吸引了来自全国150余家科研院所、高等院校及相关企事业单位的200余名专家学者、行业部门及企业代表参会，是国内数字农业领域极具影响力的一次学术盛会。\n\n河南省农业科学院党委副书记、副院长王强出席会议开幕式并致欢迎辞，他在致辞中表示全球农业正迎来数字化、智能化变革窗口期，生成式AI、空天信息组网、自主无人系统等新质生产力核心技术，正在颠覆传统农业生产模式、重构现代农业发展范式。他指出，河南作为农业大省，亟需数字科创赋能农业提质升级，希望以本次研讨会为纽带，深化全国产学研交流合作，加速数字农业科技创新成果落地转化，助力中原农业现代化建设，赋能全国农业高质量发展。\n\n会议主席、数字农业专业委员会主任委员、空天院研究员吴炳方，以及会议共同主席、中国农学会农业信息分会主任委员、中国农业科学院农业资源与农业区划研究所所长、研究员吴文斌先后致开幕辞，立足行业发展全局，阐述了数字技术在保障粮食安全、优化水土资源配置、推动农食体系升级中的核心价值与重要意义，并展望了“十五五”数字农业领域的发展方向与前景。开幕式由河南省农业科学院农业信息技术研究所所长曾凯主持。\n\n本次研讨会聚焦行业前沿、深耕技术落地，搭建了高水平学术交流平台。会上，8位特邀专家紧扣“数字技术驱动农业系统转型”的核心议题作主旨报告。在宏观战略层面，联合国粮农组织原副总干事、国际欧亚科学院院士何昌垂指出AI赋能食农体系已是战略必答题，需强化顶层设计与“政产学研用”协同创新；空天院研究员吴炳方强调，提出数字农业需实现“高分辨率化、平台化、终端化”三个转化的观点，以便推动数字农业技术从研究走向落地应用；西北农林科技大学副校长张宏鸣建议全球水蚀评价需与FAO\u002FGSP等国际组织协同，共同构建土壤健康风险防控体系建设；北京师范大学教授潘耀忠则直指痛点，明确了大尺度遥感作物制图面临的工程化瓶颈，深度剖析数据覆盖、AI勾绘与模型泛化等核心难题；中国水利水电科学研究院水利研究所研究员吴文勇呈现数字技术在优化水土资源配置、高效灌溉、保障粮食安全中的关键作用；中国科学院东北地理与农业生态研究所研究员刘焕军构建了基于天空地立体监测体系与“地块画像”平台，提出大模型与AI赋能黑土地质量监测与保护的新路径；西南交通大学教授黄健熙立足学术前瞻，展望了遥感数据同化与深度学习耦合、灾害胁迫下产量预测及时空谱农业遥感大模型等未来趋势；中国科学院地理科学与资源研究所研究员董金玮系统剖析了气候变化下洪旱灾害频发及跨境流域水资源统筹对农业气候韧性的关键影响。上述系列报告精准回应了行业痛点、预判了发展态势，为数字农业技术创新和产业应用提供了具有高度实操价值的理论指引与实践路径。\n\n为拓宽交流维度、覆盖多元研究方向，会议设置5个专题分会场与2个学生快闪报告专场，共聚焦遥感技术、地理信息系统、人工智能、大数据等核心数字技术在农业服务中的创新应用，为不同层级、不同领域的科研人员提供思想碰撞、成果展示、经验互通的平台。\n\n会议期间，数字农业专业委员会同步召开2026年度工作会议，系统复盘年度工作成果，审议专项工作报告，深入研讨农情智慧监测、农业大数据AI应用、作物遥感建模、智能农机装备研发、农业系统集成服务等五大核心工作组发展方案，并完成委员会人员增补选举工作，进一步完善专业委员会组织架构，夯实行业科研统筹与服务能力。\n\n本次研讨会配套预热活动成果丰硕，8月7日至9日举办的首届农情遥感技术培训班，以“理论教学+平台实操”的立体化教学模式，依托CropWatch云平台开展实战教学，精准提升农业科研从业者、基层技术人员的遥感数据处理与农情监测实操能力，有效打通了前沿技术落地应用的“最后一公里”。\n\n此次研讨会的圆满召开，有效打通了数字农业领域产学研用沟通壁垒，推动空天信息、智能大数据技术与农业可持续发展深度融合，不仅集中展示了我国数字农业领域的前沿科研成果，更为培育农业新质生产力、推动传统农业转型升级、保障现代农业高质量发展注入了强劲科创动能。\n\n![Image 1](https:\u002F\u002Faircas.cas.cn\u002Fdtxw\u002Fhzjl\u002F202609\u002FW020260904583592815639.png)\n\n大会合影","中国科学院空天信息创新研究院2026年9月4日","2026-09-04T00:00:00Z","报道",84,{"impact":345,"substance":16,"depth":17,"authority":297,"freshness":78,"relevant":20,"comment":346},25,"全国性数字农业学术盛会，汇聚顶尖专家，聚焦天空地一体化农情监测，具有行业引领价值。",[348],{"name":340,"url":337},[25,30,56,350,351],"天空地一体化","学术研讨会",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":12,"version":41,"method":42,"evidence":355},{"candidates":356,"agricultural_evidence":48},[357,361,365],{"code":33,"score":91,"supported":48,"title":358,"abstract":359,"tags":360},[30],[56],[30,56],{"code":46,"score":47,"supported":48,"title":362,"abstract":363,"tags":364},[25],[25],[25],{"code":366,"score":60,"supported":48,"title":367,"abstract":368,"tags":370},"G1",[],[369],"技术培训",[],[372,373],"天空地一体化 学术研讨会 农情监测 数字农业","天空地一体化 学术研讨会","天空地一体化学术研讨会农情监测数字农业-1787","2026-09-07T00:04:37.580467Z",{"id":377,"title":378,"url":379,"summary":380,"summary_zh":381,"content":8,"source_name":382,"source_url":379,"published_at":383,"category":10,"cover_url":8,"hotness":11,"is_selected":12,"score":384,"score_detail":385,"sources":387,"tags":389,"primary_topic":393,"secondary_topics":394,"classification":399,"search_phrases":450,"slug":453,"view_count":152,"doi":454,"paper":455,"created_at":477},5582,"AI-Based RGB Imaging and 14-Channel Multispectral Food Quality Scanner","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85255","Food-quality assessment plays an important role in food safety, agricultural processing, storage, and supply-chain management. Conventional inspection methods depend on visual judgement or laboratory-based testing, which may require considerable time, specialised equipment, and manual effort. Recent developments in computer vision, multispectral sensing, and artificial intelligence (AI) provide opportunities for automated, non-destructive food-quality assessment. This paper reviews the application of RGB imaging, multispectral sensing, machine learning, and deep learning for fruit-ripeness estimation, surfacedefect detection, grain classification, and food-authenticity assessment. The reviewed literature demonstrates that RGB imaging can extract visible features such as colour, shape, and texture, whereas multispectral sensing provides wavelength-dependent information associated with food characteristics. Published studies have reported promising results in tomato-ripeness classification, apple-defect detection, rice-grain analysis, and honey-adulteration screening. Based on these findings, a compact food-quality scanner integrating an RGB camera, a 14-channel multispectral sensor, controlled illumination, and an embedded processing unit is proposed. The review discusses sensor integration, data preprocessing, feature fusion, classification methods, evaluation metrics, and implementation challenges. It also identifies the need for broader datasets, standardised illumination, independent validation, and resource-efficient AI models to improve the reliability of portable food-inspection systems.","食品质量评估在食品安全、农产品加工、储运和供应链管理中发挥着重要作用。传统检测方法依赖人工目视判断或实验室检测，往往需要较长时间、专用设备和人工投入。近年来，计算机视觉、多光谱传感和人工智能（AI）的发展为自动化、无损食品质量评估提供了新的机遇。本文综述了RGB成像、多光谱传感、机器学习和深度学习在果实成熟度估计、表面缺陷检测、谷物分类和食品真实性评估中的应用。所综述的文献表明，RGB成像能够提取颜色、形状和纹理等可见特征，而多光谱传感则可提供与食品特性相关的波长依赖信息。已发表研究在番茄成熟度分类、苹果缺陷检测、稻米籽粒分析和蜂蜜掺假筛查等方面取得了良好结果。基于上述研究结果，本文提出一种集成了RGB相机、14通道多光谱传感器、受控照明和嵌入式处理单元的紧凑型食品质量扫描仪。综述讨论了传感器集成、数据预处理、特征融合、分类方法、评价指标和实现挑战，并指出需要更广泛的数据集、标准化照明、独立验证和资源高效的AI模型，以提高便携式食品检测系统的可靠性。","International Journal for Research in Applied Science and Engineering Technology","2026-10-21T00:00:00Z",65,{"impact":47,"substance":17,"depth":172,"authority":278,"freshness":11,"relevant":20,"comment":386},"综述性论文，提出集成RGB与14通道多光谱的便携食品质量扫描仪方案，方法框架清晰但缺乏自有实验数据，属细分领域技术进展，可作参考但非重大突破。",[388],{"name":382,"url":379},[26,97,390,391,392],"无损检测","多光谱成像","食品质量检测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[395],{"code":117,"slug":396,"name":397,"path":398,"parent_code":37},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":40,"locked":12,"version":41,"method":42,"evidence":400},{"candidates":401,"agricultural_evidence":48},[402,406,415,422,427,434,440,446],{"code":33,"score":47,"supported":48,"title":403,"abstract":404,"tags":405},[109],[109],[391],{"code":117,"score":11,"supported":48,"title":407,"abstract":408,"tags":414},[],[409,121,410,411,412,122,413],"深度学习","计算机视觉","machine learning","deep learning","computer vision",[97],{"code":416,"score":118,"supported":48,"title":417,"abstract":418,"tags":421},"E4",[],[419,420],"食品安全","food safety",[390],{"code":198,"score":78,"supported":48,"title":423,"abstract":424,"tags":426},[],[425,202],"稻米",[],{"code":428,"score":78,"supported":48,"title":429,"abstract":430,"tags":433},"B1",[],[431,432],"番茄","tomato",[],{"code":130,"score":78,"supported":48,"title":435,"abstract":436,"tags":439},[],[437,438],"苹果","apple",[],{"code":441,"score":60,"supported":48,"title":442,"abstract":443,"tags":445},"E1",[],[444],"农产品加工",[],{"code":46,"score":20,"supported":12,"title":447,"abstract":448,"tags":449},[],[],[26],[451,452],"RGB成像 多光谱 食品质量","14通道 多光谱 扫描仪","RGB成像多光谱食品质量-5582","10.22214\u002Fijraset.2026.85255",{"doi":454,"openalex_id":456,"authors":457,"venue":382,"cited_by_count":152,"oa_url":379,"card":470,"direction":476,"ingested_from":160},"W7223139905",[458,460,462,464,466,468],{"name":459,"orcid":8},"Eshwari Take",{"name":461,"orcid":8},"Avanish Raut",{"name":463,"orcid":8},"Vedant Joshi",{"name":465,"orcid":8},"Shravani Akolkar",{"name":467,"orcid":8},"R. M. Gharat",{"name":469,"orcid":8},"Shruti Dive",{"tldr":471,"method":472,"finding":473,"direction":474,"opportunity":475},"综述RGB与多光谱成像结合AI的食品品质无损检测，并提出一款集成14通道多光谱传感器的紧凑型扫描仪方","综述RGB成像、多光谱传感与机器学习\u002F深度学习在食品检测中的应用，并提出传感器集","RGB与多光谱融合在番茄成熟度、苹果缺陷、稻米分类和蜂蜜掺假检测中表现良好。","农业人工智能与决策模型","可研究多源光谱特征融合与轻量化模型，构建标准化数据集和便携设备独立验证体系。","数字乡村与农业信息化","2026-10-22T11:20:48.684398Z",{"id":479,"title":480,"url":481,"summary":482,"summary_zh":483,"content":8,"source_name":484,"source_url":481,"published_at":383,"category":10,"cover_url":8,"hotness":11,"is_selected":12,"score":88,"score_detail":485,"sources":487,"tags":489,"primary_topic":492,"secondary_topics":497,"classification":505,"search_phrases":533,"slug":536,"view_count":152,"doi":537,"paper":538,"created_at":564},5573,"Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128375","Accurate estimation of nitrogen (N) concentration in alfalfa ( Medicago sativa L .) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance ( R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.","准确估算紫花苜蓿（Medicago sativa L.）的氮浓度有助于饲草品质评估和收获期安排。经验性遥感模型常因三维冠层结构的混杂效应而出现过拟合。引入结构参数可以稳定生化反演，但同步开展原位形态学测量难以大规模获取。我们开发了形态感知潜在嵌入网络（Morphology-Aware Latent Embedding Network，MALENet），这是一个将生理与形态机制同高光谱数据相结合的混合框架。农业技术转移决策支持系统（Decision Support System for Agrotechnology Transfer，DSSAT）与PROSAIL辐射传输模型耦合，生成了结构信息丰富的合成高光谱先验数据。在此基础上，预训练了一个带有挤压—激励（Squeeze-and-Excitation，SE）注意力机制的卷积神经网络（convolutional neural network，CNN），将生态生理学知识提炼为稠密潜在表征。随后，将这一机制性导出的嵌入与在不同田间条件下采集的经验性高光谱测量数据相融合。利用河西走廊多年田间样方进行的评估显示，相较于传统机器学习和深度学习模型（LASSO-ANN：R² = 0.61；CNN：R² = 0.66），预测性能有所提升（R² = 0.71，RMSE = 0.30%）。特征归因表明，MALENet利用近红外多次散射（归因占比58.3%）作为结构代理，以校准氮浓度反演，抵御冠层结构的混杂效应。尽管受样本量和PROSAIL一维冠层假设的限制，这些发现表明，物理引导的深度学习有潜力利用结构信息，在精准饲草管理中进行农业性状反演。","European Journal of Agronomy",{"impact":172,"substance":15,"depth":17,"authority":18,"freshness":11,"relevant":20,"comment":486},"该研究将作物模型与辐射传输模型耦合生成结构先验，并结合无人机高光谱数据反演苜蓿氮素，方法新颖、田间验证扎实，对精准饲草管理有参考价值，值得进入每日精选。",[488],{"name":484,"url":481},[26,97,98,490,491],"氮素监测","紫花苜蓿",{"code":493,"slug":494,"name":495,"path":496,"parent_code":290},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping",[498,499],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":500,"slug":501,"name":502,"path":503,"parent_code":504},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",{"status":40,"locked":12,"version":41,"method":42,"evidence":506},{"candidates":507,"agricultural_evidence":48},[508,513,519,525,529],{"code":33,"score":17,"supported":48,"title":509,"abstract":511,"tags":512},[510],"hyperspectral",[56,107,510],[98],{"code":493,"score":297,"supported":48,"title":514,"abstract":516,"tags":518},[515],"alfalfa",[517,515],"苜蓿",[491],{"code":500,"score":11,"supported":48,"title":520,"abstract":521,"tags":524},[],[522,517,523],"饲草","forage",[491],{"code":117,"score":11,"supported":48,"title":526,"abstract":527,"tags":528},[],[409,121,411,412,195],[97],{"code":46,"score":20,"supported":12,"title":530,"abstract":531,"tags":532},[],[],[26],[534,535],"DSSAT PROSAIL 苜蓿 氮素","无人机 高光谱 苜蓿","DSSATPROSAIL苜蓿氮素-5573","10.1016\u002Fj.eja.2026.128375",{"doi":537,"openalex_id":539,"authors":540,"venue":484,"cited_by_count":152,"oa_url":481,"card":559,"direction":157,"ingested_from":160},"W7223154576",[541,543,545,547,549,552,554,557],{"name":542,"orcid":8},"Shuai Fu",{"name":544,"orcid":8},"Yunhao Li",{"name":546,"orcid":8},"Chunli Miao",{"name":548,"orcid":8},"Jie Liu",{"name":550,"orcid":551},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":553,"orcid":8},"Yongjia Hu",{"name":555,"orcid":556},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":558,"orcid":8},"吴彩霞",{"tldr":560,"method":561,"finding":562,"direction":157,"opportunity":563},"构建MALENet融合DSSAT-PROSAIL结构先验与无人机高光谱，提升苜蓿氮浓度估算精度。","DSSAT耦合PROSAIL生成合成高光谱先验，预训练CNN-SE网络，再融合田","MALENet精度优于传统机器学习和深度学习模型，R²达0.71，近红外多次散射作为结构代理贡献58","可探索将作物模型结构先验迁移至其他牧草或作物生化参数反演，并缓解PROSAIL一维冠层假设局限。","2026-10-22T11:20:39.622844Z"]