[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5297":3,"related-5297":71},{"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":56,"slug":59,"view_count":60,"doi":8,"paper":61,"created_at":70},5297,"高志永团队《基于特征优选与优化算法的干热河谷区柑橘蒸腾模拟》","https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1087305323_122014422","《智慧农业(中英文)》2026年第8卷第4期刊载云南农业大学吴明清、高志永等团队论文,以10年生柑橘树为研究对象收集2020—2022年树干液流与环境因子数据;针对干热河谷加快建设智慧灌区背景下传统机理模型参数需求高、对部分参数高度敏感且误差来源复杂等难题,提出基于特征优选与优化算法的蒸腾模拟方法,可为优化灌区水分调控与配水策略提供决策支撑。",null,"智慧农业(中英文)(2026,8(4))","2026-10-13T00:00:00Z","论文",10,false,72,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,21,17,13,5,1,"核心期刊论文，方法有创新且数据规模扎实，对干热河谷智慧灌区水分调控有决策参考价值，但属细分领域研究，时效性一般。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","云南","柑橘","智慧灌区","蒸腾模拟",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":39,"locked":13,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,50],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},12,true,[28],[28],[28],{"code":51,"score":52,"supported":46,"title":53,"abstract":54,"tags":55},"I1",4,[],[26],[26],[57,58],"云南农业大学 柑橘 蒸腾模拟","干热河谷 智慧灌区","云南农业大学柑橘蒸腾模拟-5297",0,{"doi":8,"openalex_id":8,"authors":62,"venue":8,"cited_by_count":60,"oa_url":8,"card":63,"direction":67,"ingested_from":69},[],{"tldr":64,"method":65,"finding":66,"direction":67,"opportunity":68},"用特征优选与优化算法模拟干热河谷区柑橘蒸腾。","基于2020—2022年树干液流与环境因子数据构建特征优选与优化算法模型。","该方法可降低机理模型参数依赖与敏感性，提升蒸腾模拟精度。","智慧农业 \u002F 农业物联网","可探索将蒸腾模拟嵌入智慧灌区实时配水决策，研究多树种迁移与边缘部署。","agent","2026-10-19T00:07:48.135945Z",{"total":72,"page":21,"page_size":72,"items":73},6,[74,129,200,244,305,399],{"id":75,"title":76,"url":77,"summary":78,"summary_zh":8,"content":8,"source_name":79,"source_url":8,"published_at":80,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":81,"score_detail":82,"sources":87,"tags":89,"primary_topic":93,"secondary_topics":94,"classification":95,"search_phrases":117,"slug":120,"view_count":20,"doi":8,"paper":121,"created_at":128},3962,"华中农业大学柑橘类果实泛化识别与定位研究——YOLOv8n-Light轻量化部署","https:\u002F\u002Fnews.foodmate.net\u002F2026\u002F10\u002Fwap_754150.html","华中农业大学工学院马京奥、刘洁副教授团队研究YOLOv8n-Light模型在柑橘类果实识别中的应用，模型可同时识别橙、橘、柚三种果实，计算量较原始YOLOv8降低58%，工作站CPU推理速度提升45.9%（达15.35FPS），树莓派4B推理速度提升38.2%（达0.76FPS）；多轴机械臂采摘作业控制中，果实中心坐标X、Y、Z方向平均误差均控制在10mm内，半径误差小于6mm。312次果园采摘测试验证高兼容性与实用性。研究得到国家重点研发计划、国家柑橘产业技术体系资助。","食品伙伴网·2026年10月01日","2026-10-01T00:00:00Z",82,{"impact":83,"substance":84,"depth":85,"authority":19,"freshness":12,"relevant":21,"comment":86},18,22,19,"高校团队提出轻量化柑橘识别模型并完成果园实测，方法新颖、数据扎实，对智能采摘装备有参考价值。",[88],{"name":79,"url":77},[26,90,91,28,92],"农业人工智能","采摘机器人","轻量化模型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":96},{"candidates":97,"agricultural_evidence":46},[98,102,108,113],{"code":32,"score":45,"supported":46,"title":99,"abstract":100,"tags":101},[28],[28],[28],{"code":103,"score":104,"supported":13,"title":105,"abstract":106,"tags":107},"I2",2,[],[],[90,92],{"code":109,"score":21,"supported":13,"title":110,"abstract":111,"tags":112},"F6",[],[],[91],{"code":51,"score":21,"supported":13,"title":114,"abstract":115,"tags":116},[],[],[26],[118,119],"华中农业大学 柑橘 采摘机器人","YOLOv8n-Light 柑橘识别","华中农业大学柑橘采摘机器人-3962",{"doi":8,"openalex_id":8,"authors":122,"venue":8,"cited_by_count":60,"oa_url":8,"card":123,"direction":67,"ingested_from":69},[],{"tldr":124,"method":125,"finding":126,"direction":67,"opportunity":127},"提出YOLOv8n-Light轻量化模型，实现橙橘柚三种柑橘果实泛化识别与定位，并部署于树莓派和采摘","改进YOLOv8n轻量化模型，工作站与树莓派4B部署，多轴机械臂采摘测试312次","计算量降58%，工作站CPU推理15.35FPS，果实中心坐标误差\u003C10mm，半径误差\u003C6mm。","可探索多品种泛化模型在边缘设备上的精度-速度权衡，及跨季节、遮挡场景下的鲁棒性提升。","2026-10-02T11:53:16.558461Z",{"id":130,"title":131,"url":132,"summary":133,"summary_zh":134,"content":8,"source_name":135,"source_url":132,"published_at":136,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":137,"score_detail":138,"sources":142,"tags":144,"primary_topic":147,"secondary_topics":148,"classification":149,"search_phrases":171,"slug":174,"view_count":104,"doi":175,"paper":176,"created_at":199},2284,"Lightweight nighttime citrus fruit detection in UAV imagery using an enhanced NightNet","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112378","Lightweight nighttime citrus fruit detection in UAV imagery using an enhanced NightNet。Computers and Electronics in Agriculture","基于增强型NightNet的无人机图像轻量化夜间柑橘果实检测。《计算机与电子农业》","Computers and Electronics in Agriculture","2026-09-11T00:00:00Z",77,{"impact":16,"substance":17,"depth":18,"authority":139,"freshness":140,"relevant":21,"comment":141},14,9,"核心期刊论文，提出轻量化夜间柑橘检测方法，方法新颖且面向无人机夜间作业场景，具备细分领域参考价值。",[143],{"name":135,"url":132},[26,145,90,146,28],"无人机","遥感",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":150},{"candidates":151,"agricultural_evidence":46},[152,158,162,166],{"code":32,"score":153,"supported":46,"title":154,"abstract":156,"tags":157},15,[155],"citrus",[28,155],[28],{"code":51,"score":21,"supported":13,"title":159,"abstract":160,"tags":161},[],[],[26],{"code":103,"score":21,"supported":13,"title":163,"abstract":164,"tags":165},[],[],[90],{"code":167,"score":21,"supported":13,"title":168,"abstract":169,"tags":170},"I3",[],[],[146],[172,173],"农业人工智能 智慧农业 无人机 柑橘","农业人工智能 智慧农业","农业人工智能智慧农业无人机柑橘-2284","10.1016\u002Fj.compag.2026.112378",{"doi":175,"openalex_id":177,"authors":178,"venue":135,"cited_by_count":60,"oa_url":8,"card":192,"direction":196,"ingested_from":198},"W7212284586",[179,181,184,186,188,190],{"name":180,"orcid":8},"Zhixian Wu",{"name":182,"orcid":183},"Juntao Xiong","https:\u002F\u002Forcid.org\u002F0000-0002-1095-812X",{"name":185,"orcid":8},"Zhen Liu",{"name":187,"orcid":8},"Sitong Li",{"name":189,"orcid":8},"Jingru Peng",{"name":191,"orcid":8},"Sidi Chen",{"tldr":193,"method":194,"finding":195,"direction":196,"opportunity":197},"提出增强NightNet实现无人机夜间柑橘果实轻量检测。","基于NightNet改进的轻量网络，用于无人机夜间图像。","增强NightNet在夜间无人机图像中实现轻量且准确的柑橘检测。","农业遥感与作物表型","夜间无人机果实检测的轻量化与低照度增强可进一步结合多光谱或时序信息。","openalex","2026-09-13T23:30:02.020275Z",{"id":201,"title":202,"url":203,"summary":204,"summary_zh":8,"content":8,"source_name":205,"source_url":8,"published_at":206,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":81,"score_detail":207,"sources":209,"tags":211,"primary_topic":214,"secondary_topics":215,"classification":216,"search_phrases":231,"slug":234,"view_count":52,"doi":8,"paper":235,"created_at":243},2270,"华中农大实现柑橘黄龙病早期无损诊断 提LLM辅助拉曼光谱相似度融合框架","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7682626369448165922","华中农业大学工学院联合园艺林学学院在Agriculture Communications发表研究,提出LLM辅助的多通道光谱相似度融合框架(LAMSSF),依托少量经qPCR验证的参考样本,实现柑橘HLB早期无损诊断,降低模型对大规模标注数据的依赖。","华中农业大学 Agriculture Communications","2026-09-06T16:00:00Z",{"impact":84,"substance":84,"depth":83,"authority":139,"freshness":72,"relevant":21,"comment":208},"华中农大提出LLM辅助拉曼光谱融合框架，实现柑橘黄龙病早期无损诊断，方法新颖且降低标注依赖，具备行业推广价值。",[210],{"name":205,"url":203},[26,90,212,28,213],"病害检测","光谱遥感",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":217},{"candidates":218,"agricultural_evidence":46},[219,223,227],{"code":32,"score":45,"supported":46,"title":220,"abstract":221,"tags":222},[28],[28],[28],{"code":51,"score":21,"supported":13,"title":224,"abstract":225,"tags":226},[],[],[26],{"code":103,"score":21,"supported":13,"title":228,"abstract":229,"tags":230},[],[],[90],[232,233],"农业人工智能 光谱遥感 智慧农业 病害检测","农业人工智能 光谱遥感","农业人工智能光谱遥感智慧农业病害检测-2270",{"doi":8,"openalex_id":8,"authors":236,"venue":8,"cited_by_count":60,"oa_url":8,"card":237,"direction":241,"ingested_from":69},[],{"tldr":238,"method":239,"finding":240,"direction":241,"opportunity":242},"提出LLM辅助多通道光谱相似度融合框架，实现柑橘黄龙病早期无损诊断。","拉曼光谱多通道相似度融合，LLM辅助，少量qPCR验证参考样本。","少量标注样本即可实现HLB早期无损诊断，降低对大规模标注数据依赖。","农业人工智能与决策模型","可探索LLM辅助光谱融合在更多作物病害早期诊断中的迁移与少样本泛化能力。","2026-09-13T00:04:04.913308Z",{"id":245,"title":246,"url":247,"summary":248,"summary_zh":249,"content":8,"source_name":250,"source_url":247,"published_at":251,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":252,"score_detail":253,"sources":256,"tags":258,"primary_topic":261,"secondary_topics":262,"classification":263,"search_phrases":284,"slug":287,"view_count":288,"doi":289,"paper":290,"created_at":304},1382,"Instance Segmentation and Multi-Object Tracking for Fruit Quality Grading and Dynamic Yield Estimation in Egyptian Citrus Orchards","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102529","Egypt ranks among the world’s leading citrus producers and exporters; however, orchard operations remain constrained by subjective, labor-intensive manual quality grading and time-consuming yield estimation that require specialized expertise. This study presents an AI-driven orchard-intelligence framework for automated fruit quality grading and dynamic yield estimation under the unstructured conditions of Egyptian orange farms. To address the lack of in-field public datasets, a domain-specific instance-segmentation dataset of 618 original pixel-level annotated images, expanded to 1,545 augmented training images, and 15 videos was constructed. This dataset was used to benchmark single-stage segmentation models (YOLOv8, YOLOv11) against a two-stage (Mask R-CNN), alongside a transformer-based bounding-box detection (RT-DETR), under dense canopy occlusion, clustered fruit instances, and variable natural illumination. Each fine-tuned model was evaluated across two stages: static quality grading and dynamic yield estimation. For static grading (Good vs. Defective), YOLOv11l ranked among the top tier of evaluated segmenters, reaching mean Average Precision (mAP) of 0.932 with balanced precision-recall behavior, while maintaining real-time feasibility at 26.1 ms per image. For dynamic yield estimation, a multi-object tracking pipeline was implemented using BoT-SORT with camera-motion compensation and a virtual line-crossing counting mechanism. The evaluation reveals a three-way dissociation: the highest-mAP static segmenter (YOLOv11l), the best dynamic total counter (RT-DETR, WMAPE = 5.66%, despite the lowest box-level static accuracy), and the best quality-resolved dynamic grader (YOLOv11s, WMAPE = 6.97% at 14.2 ms per frame) are three different systems; RT-DETR did not resolve fruit quality reliably under motion. Counting accuracy and grading capability are thus separable properties requiring separate evaluation. Overall, the framework enables objective fruit quality assessment and actionable in-field fruit counting to support harvest logistics and labor planning, providing a practical foundation for smart agriculture in the Egyptian citrus sector.","埃及位居全球领先的柑橘生产国和出口国之列；然而，果园作业仍受制于主观性强、劳动密集的人工质量分级以及耗时且需专业技能的产量估算。本研究提出了一种人工智能驱动的果园智能框架，用于在埃及橙园的非结构化条件下实现自动化果实质量分级和动态产量估算。为解决田间公开数据集缺乏的问题，构建了一个领域特定的实例分割数据集，包含618张原始像素级标注图像，扩充至1545张增强训练图像，以及15段视频。该数据集用于在密集树冠遮挡、果实簇生和自然光照多变条件下，对单阶段分割模型（YOLOv8、YOLOv11）与两阶段模型（Mask R-CNN）进行基准测试，同时对比基于Transformer的边界框检测模型（RT-DETR）。每个微调模型均经过两个阶段的评估：静态质量分级和动态产量估算。在静态分级（优质与缺陷）中，YOLOv11l在评估的分割模型中位居前列，平均精度（mAP）达到0.932，精确率与召回率表现均衡，同时以每张图像26.1毫秒的处理速度保持实时可行性。在动态产量估算方面，采用基于BoT-SORT的多目标跟踪流程，结合相机运动补偿和虚拟线跨越计数机制。评估结果揭示了三重分离现象：静态分割精度最高的模型（YOLOv11l）、动态总数计数最优的模型（RT-DETR，加权平均绝对百分比误差WMAPE = 5.66%，尽管其框级静态精度最低），以及质量分辨动态分级最佳的模型（YOLOv11s，每帧14.2毫秒时WMAPE = 6.97%）是三个不同的系统；RT-DETR在运动条件下无法可靠分辨果实质量。因此，计数精度和分级能力是可分离的属性，需要分别评估。总体而言，该框架实现了客观的果实质量评估和可操作的田间果实计数，以支持采收物流和劳动力规划，为埃及柑橘产业的智慧农业提供了实用基础。","Smart Agricultural Technology","2026-09-01T00:00:00Z",79,{"impact":83,"substance":84,"depth":85,"authority":19,"freshness":254,"relevant":21,"comment":255},7,"针对埃及柑橘园提出AI驱动的品质分级与动态产量估算框架，方法新颖且数据详实，对智慧农业有参考价值。",[257],{"name":250,"url":247},[26,90,259,28,260],"目标检测","产量估算",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":264},{"candidates":265,"agricultural_evidence":46},[266,271,276,280],{"code":32,"score":83,"supported":46,"title":267,"abstract":268,"tags":270},[155],[28,269,155],"orchard",[28],{"code":51,"score":254,"supported":46,"title":272,"abstract":273,"tags":275},[],[26,274],"smart agriculture",[26],{"code":103,"score":104,"supported":13,"title":277,"abstract":278,"tags":279},[],[],[90,259],{"code":167,"score":21,"supported":13,"title":281,"abstract":282,"tags":283},[],[],[260],[285,286],"农业人工智能 产量估算 智慧农业 目标检测","农业人工智能 产量估算","农业人工智能产量估算智慧农业目标检测-1382",3,"10.1016\u002Fj.atech.2026.102529",{"doi":289,"openalex_id":291,"authors":292,"venue":250,"cited_by_count":60,"oa_url":247,"card":299,"direction":67,"ingested_from":198},"W7204959516",[293,295,297],{"name":294,"orcid":8},"Saleh A. Farhoud",{"name":296,"orcid":8},"Yasmine A. Zaghloul",{"name":298,"orcid":8},"Omar M. Shehata",{"tldr":300,"method":301,"finding":302,"direction":67,"opportunity":303},"构建柑橘果园AI框架，实现果实质量分级与动态产量估计，并评估多种分割与跟踪模型。","构建实例分割数据集，微调YOLOv8\u002Fv11、Mask R-CNN、RT-DET","静态分级最优为YOLOv11l，动态计数最优为RT-DETR，质量动态分级最优为YOLOv11s，计","可探索多任务协同优化，如设计统一模型同时满足静态分级与动态计数，或引入时序信息提升运动场景下的质量分级鲁棒性。","2026-09-02T23:30:04.506009Z",{"id":306,"title":307,"url":308,"summary":309,"summary_zh":310,"content":8,"source_name":311,"source_url":308,"published_at":312,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":313,"score_detail":314,"sources":316,"tags":318,"primary_topic":320,"secondary_topics":321,"classification":333,"search_phrases":364,"slug":367,"view_count":21,"doi":368,"paper":369,"created_at":398},1312,"Accurate diagnosis of soil moisture and canopy water content in citrus orchards based on synergistic multi-source remote sensing","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agwat.2026.110725","Accurate prediction of soil moisture is essential for precise agricultural irrigation decision-making and regional water resource optimal scheduling. However, regional soil moisture inversion commonly suffers from three major limitations, including overreliance on single remote sensing data sources, fixed canopy parameters adopted in conventional physical models, and severe cloud contamination in the cloudy mountainous areas of Southwest China. In this study, a synergistic multi-source remote sensing framework integrating Landsat-8\u002F9, Sentinel-2, and Sentinel-1A datasets was established to improve the traditional water cloud model by introducing phenology-driven dynamic canopy parameters. Specifically, canopy coverage and canopy water content of late-maturing citrus across different growth stages were accurately retrieved from Landsat-8\u002F9 and Sentinel-2 imagery to optimize the simulation of canopy backscattering signals. The all-weather C-band radar data effectively mitigate the persistent cloud-cover limitations prevalent in the hilly regions of Southwest China. The results indicated that the vegetation water content in large-scale citrus orchards retrieved by optical remote sensing exhibits significant spatial variability, mainly distributed in the range of 1.65–5.71 kg·m −2 and 1.55–5.78 kg·m −2 , respectively. The soil moisture inversion results using multi-temporal Sentinel-1A data in VV polarization outperformed those in VH polarization, with R² ranges of 0.785–0.854 and 0.515–0.759, RMSE ranges of 6.125–8.351% and 6.942–8.412%, respectively. The two polarizations data have the highest inversion accuracy in citrus fruit maturation stage, with R² ranges of 0.811–0.842 and 0.682–0.711, RMSE ranges of 6.125–6.132% and 6.942–7.025%, respectively. This study proposes a novel methodology for high-resolution and precision inversion of surface soil moisture at the regional farmland scale.","土壤水分的精准预测对于精准农业灌溉决策和区域水资源优化调度至关重要。然而，区域土壤水分反演普遍面临三大局限，包括对单一遥感数据源的过度依赖、传统物理模型中采用固定冠层参数，以及中国西南多云山区严重的云污染问题。本研究构建了一种融合Landsat-8\u002F9、Sentinel-2和Sentinel-1A数据集的协同多源遥感框架，通过引入物候驱动的动态冠层参数对传统水云模型进行改进。具体而言，利用Landsat-8\u002F9和Sentinel-2影像精确反演了不同生长阶段晚熟柑橘的冠层覆盖度和冠层含水量，以优化冠层后向散射信号的模拟。全天候C波段雷达数据有效缓解了西南丘陵地区普遍存在的持续云覆盖限制。结果表明，光学遥感反演的大规模柑橘园植被含水量表现出显著的空间变异性，主要分布在1.65–5.71 kg·m⁻²和1.55–5.78 kg·m⁻²范围内。使用多时相Sentinel-1A VV极化的土壤水分反演结果优于VH极化，R²范围分别为0.785–0.854和0.515–0.759，RMSE范围分别为6.125–8.351%和6.942–8.412%。两种极化数据在柑橘果实成熟期反演精度最高，R²范围分别为0.811–0.842和0.682–0.711，RMSE范围分别为6.125–6.132%和6.942–7.025%。本研究提出了一种在区域农田尺度上实现地表土壤水分高分辨率精准反演的新方法。","Agricultural Water Management","2026-08-31T00:00:00Z",75,{"impact":83,"substance":84,"depth":83,"authority":139,"freshness":288,"relevant":21,"comment":315},"提出多源遥感协同反演土壤水分的新方法，对丘陵山地精准灌溉有参考价值，但时效性一般。",[317],{"name":311,"url":308},[26,146,319,28],"土壤墒情",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[322,328],{"code":323,"slug":324,"name":325,"path":326,"parent_code":327},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":167,"slug":329,"name":330,"path":331,"parent_code":332},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":39,"locked":13,"version":40,"method":41,"evidence":334},{"candidates":335,"agricultural_evidence":46},[336,340,345,350,359],{"code":32,"score":153,"supported":46,"title":337,"abstract":338,"tags":339},[155],[28,155],[28],{"code":323,"score":153,"supported":46,"title":341,"abstract":342,"tags":344},[324],[343,324],"土壤",[319],{"code":167,"score":153,"supported":46,"title":346,"abstract":348,"tags":349},[347],"remote sensing",[146,347],[146],{"code":351,"score":140,"supported":46,"title":352,"abstract":353,"tags":358},"H3",[],[354,355,356,357],"灌溉","水资源","irrigation","water resource",[],{"code":51,"score":52,"supported":46,"title":360,"abstract":361,"tags":363},[],[362],"精准农业",[26],[365,366],"土壤墒情 智慧农业 柑橘 遥感","土壤墒情 智慧农业","土壤墒情智慧农业柑橘遥感-1312","10.1016\u002Fj.agwat.2026.110725",{"doi":368,"openalex_id":370,"authors":371,"venue":311,"cited_by_count":60,"oa_url":308,"card":393,"direction":196,"ingested_from":198},"W7204808715",[372,374,376,378,381,384,387,389,391],{"name":373,"orcid":8},"Zongjun Wu",{"name":375,"orcid":8},"Bin Zhu",{"name":377,"orcid":8},"Ningbo Cui",{"name":379,"orcid":380},"Wei Zhao","https:\u002F\u002Forcid.org\u002F0000-0003-0215-8584",{"name":382,"orcid":383},"Yanjun Wang","https:\u002F\u002Forcid.org\u002F0000-0001-5286-6269",{"name":385,"orcid":386},"Liwen Xing","https:\u002F\u002Forcid.org\u002F0000-0003-0007-3560",{"name":388,"orcid":8},"Quanshan Liu",{"name":390,"orcid":8},"Shenglin Wen",{"name":392,"orcid":8},"Bihang Fan",{"tldr":394,"method":395,"finding":396,"direction":196,"opportunity":397},"融合多源遥感数据改进水云模型，实现柑橘园土壤水分和冠层含水量高精度反演。","集成Landsat-8\u002F9、Sentinel-2和Sentinel-1A数据，引","VV极化反演精度优于VH，果实成熟期精度最高，R²达0.842。","可探索将动态冠层参数方法应用于其他作物或区域，结合更高分辨率数据提升反演精度。","2026-09-01T23:30:20.097045Z",{"id":400,"title":401,"url":402,"summary":403,"summary_zh":8,"content":404,"source_name":405,"source_url":8,"published_at":406,"category":407,"cover_url":8,"hotness":12,"is_selected":13,"score":408,"score_detail":409,"sources":411,"tags":413,"primary_topic":418,"secondary_topics":419,"classification":420,"search_phrases":436,"slug":439,"view_count":60,"doi":8,"paper":8,"created_at":440},5516,"上海农科院葡萄团队赴银川参加第47届世界葡萄与葡萄酒大会","https:\u002F\u002Fwww.saas.sh.cn\u002Fhzjl\u002Fgjhz\u002Fcontent_57128","10月12日至16日第47届世界葡萄与葡萄酒大会在银川举办，智慧农业研究中心葡萄团队负责人奚晓军研究员作专题报告，阐明疏穗与环剥处理对果实糖、花青素及脱落酸积累的源库调控机制。团队还观摩酿酒葡萄AI综合作业机器人成果展示，与研发单位围绕葡萄智慧装备与果园智能化管理深入交流。","2026年10月12-16日，第47届世界葡萄与葡萄酒大会在银川举办，智慧农业研究中心葡萄团队负责人奚晓军研究员一行赴会交流。团队围绕“提升葡萄种植生产韧性与可持续发展能力”主题，向全球同行展示最新科研成果。\n\n会上，奚晓军作“Changes in sugar, anthocyanin and ABA accumulation in response to cluster thinning and girdling in ‘Jumeigui’grape”专题报告。报告围绕葡萄疏穗与环剥处理开展研究，阐明源库比对果实糖、花青素及脱落酸积累的调控机制，为葡萄优质栽培技术提供理论支撑，报告内容引发国内外园艺专家广泛研讨。参会期间，团队前往银川国际会展中心观摩酿酒葡萄 AI 综合作业机器人成果展示，详细了解该装备在葡萄园智能作业方面的技术特点与应用前景，并与研发单位围绕葡萄智慧装备研发、果园智能化管理等方向开展深入交流。\n\n本次参会是葡萄团队持续推进科研国际化、对标国际前沿开展研究的重要实践。","上海市农业科学院 2026-10-19","2026-10-19T00:00:00Z","报道",65,{"impact":16,"substance":18,"depth":19,"authority":19,"freshness":72,"relevant":21,"comment":410},"国际学术会议交流并涉及葡萄AI作业机器人观摩，有实质科研内容与行业参考价值，但属团队参会动态，影响面有限。",[412],{"name":405,"url":402},[26,414,415,416,417],"智能农机","葡萄","栽培技术","国际交流",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":421},{"candidates":422,"agricultural_evidence":46},[423,427,431],{"code":32,"score":45,"supported":46,"title":424,"abstract":425,"tags":426},[415],[415],[415],{"code":51,"score":52,"supported":46,"title":428,"abstract":429,"tags":430},[],[26],[26],{"code":432,"score":21,"supported":13,"title":433,"abstract":434,"tags":435},"F5",[],[],[414],[437,438],"上海农科院 葡萄 银川","世界葡萄与葡萄酒大会 奚晓军","上海农科院葡萄银川-5516","2026-10-22T00:07:04.061911Z"]