[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3103":3,"related-3103":101},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":22,"tags":24,"primary_topic":29,"secondary_topics":35,"classification":36,"search_phrases":96,"slug":99,"view_count":56,"doi":8,"paper":8,"created_at":100},3103,"山东省农科院举办人工智能专题舜耕论坛暨培训交流会——浙江大学数字农业农村研究中心主任何勇教授作\"作物表型多源多尺度智能感知技术与装备\"专题报告","http:\u002F\u002Fwww.saas.ac.cn\u002Farticles\u002Fch10717\u002F202609\u002F1c1dbcca-09c4-421f-80dd-09910a603152.shtml","9-16 山东省农业科学院举办人工智能专题舜耕论坛暨培训交流会，落实院党委\"人工智能驱动科技创新智慧引领高质量发展\"专题活动部署，促进人工智能与各学科创新团队重点攻关方向深度耦合。论坛特邀浙江大学数字农业农村研究中心主任何勇教授作专题报告，围绕植物表型采集解析、智慧农业技术装备前沿领域，从细胞\u002F组织器官\u002F表型获取装备三个层级系统阐释作物表型智能感知技术创新实践，介绍该技术在水稻\u002F草莓\u002F茶叶等作物的示范应用。院长李向东要求各创新团队推动人工智能与作物栽培、畜禽育种、病虫害防控、种质资源鉴定、农产品质量安全等领域深度融合。会议设主会场和视频分会场，全院科研人员代表 500 余人参会。",null,"![Image 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\n\n![Image 18](http:\u002F\u002Fwww.saas.ac.cn\u002Ftemplate\u002Fsdnky\u002Fdefault2026\u002Fimages\u002Fnew2026\u002Fny_banner02.jpg)\n\nYour browser does not support the HTML5 canvas tag.Your browser does not support the HTML5 canvas tag.Your browser does not support the HTML5 canvas tag.\n\n新闻中心![Image 19](http:\u002F\u002Fwww.saas.ac.cn\u002Ftemplate\u002Fsdnky\u002Fdefault2026\u002Fimages\u002Fnew2026\u002Flm_icon01png)\n\n*   [图片新闻](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch10910\u002F)\n*   [农科要闻](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch10717\u002F)\n*   [综合新闻](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch10719\u002F)\n*   [媒体聚焦](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch10723\u002F)\n*   [媒体聚焦(图片)](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch17317\u002F)\n*   [公开公示](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch10800\u002F)\n*   [通知公告](http:\u002F\u002Fwww.saas.ac.cn\u002Fchannels\u002Fch10798\u002F)\n\n![Image 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智慧引领高质量发展”专题活动部署，促进人工智能与各学科创新团队重点攻关方向深度耦合，激活科研创新内生动力。论坛特邀浙江大学数字农业农村研究中心主任何勇教授作专题报告，院党委副书记、院长李向东主持会议并讲话。\n\n报告题为《作物表型多源多尺度智能感知技术与装备》，围绕植物表型采集解析、智慧农业技术装备前沿领域，从细胞、组织器官、表型获取装备三个层级，系统阐释作物表型智能感知技术创新实践，介绍该技术在水稻、草莓、茶叶等作物的示范应用，解读无人机、作物智慧管理装备关键技术及应用路径，提出农业科技创新要加速人工智能深度融入，以多技术交叉融合助推智慧农业高质量发展，为我院农业人工智能科研布局提供重要参考。\n\n李向东指出，报告紧扣智慧农业发展前沿，兼具理论深度和实践价值，对我院科研迭代升级具有重要指导意义。他强调，要提高政治站位，把握战略导向。深入学习贯彻习近平总书记关于人工智能创新发展的重要指示精神，把智慧农业摆在全院科技创新突出位置，强化机遇意识，开辟农业科研新赛道。要聚焦主责主业，精准靶向攻坚。各创新团队依托现有科研基础，推动人工智能与作物栽培、畜禽育种、病虫害防控、种质资源鉴定、农产品质量安全等领域深度融合，坚持问题导向，紧扣产业瓶颈凝练攻关方向，推动智能技术赋能科研实践。要压实闭环管理，推动落地见效。细化攻关任务清单，强化项目、平台、人才、经费要素保障，健全调度考核机制，将人工智能攻关及成果产出纳入评价体系，力争产出高水平科研成果、实用技术与智能装备，形成可复制推广的农业人工智能应用模式。全院科研人员要以此次论坛为契机，拓宽科研视野，聚力攻关，推动我院智慧农业科技创新再上新台阶。\n\n会议设主会场和视频分会场，院属各单位主要负责人、科研分管负责人，拟组建创新团队首席、副首席及45岁以下青年科研人员代表500余人参加会议。\n\n（撰写：陈英凯 核稿：张文君）\n\n分享\n\n分享到\n\n[微信](http:\u002F\u002Fwww.saas.ac.cn\u002Farticles\u002Fch10717\u002F202609\u002F1c1dbcca-09c4-421f-80dd-09910a603152.shtml 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25](http:\u002F\u002Fwww.saas.ac.cn\u002Fresource\u002Fsdnky01\u002Fimage\u002F202604\u002F3fcb1043-85f2-4dca-958d-b207876c780b.png)\n\n![Image 26](http:\u002F\u002Fwww.saas.ac.cn\u002Fresource\u002Fsdnky01\u002Fimage\u002F202604\u002F219d5bb3-cb2b-4115-a146-fc799b429998.png)","山东省农业科学院","2026-09-16T00:00:00Z","报道",10,false,55,{"impact":17,"substance":18,"depth":17,"authority":17,"freshness":19,"relevant":20,"comment":21},12,13,6,1,"省级农科院举办的AI专题学术交流活动，内容聚焦作物表型智能感知，有一定专业价值但属会议报道，信息增量有限。",[23],{"name":10,"url":6},[25,26,27,28],"智慧农业","农业人工智能","作物表型","学术交流",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":37,"locked":14,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":45},[42,50,54,61,67,73,79,85,91],{"code":43,"score":44,"supported":45,"title":46,"abstract":48,"tags":49},"I1",15,true,[47],"数字农业",[25,47],[25],{"code":30,"score":17,"supported":45,"title":51,"abstract":52,"tags":53},[27],[27],[27],{"code":55,"score":56,"supported":45,"title":57,"abstract":58,"tags":60},"A1",3,[],[59],"水稻",[],{"code":62,"score":56,"supported":45,"title":63,"abstract":64,"tags":66},"B1",[],[65],"草莓",[],{"code":68,"score":56,"supported":45,"title":69,"abstract":70,"tags":72},"B3",[],[71],"茶叶",[],{"code":74,"score":56,"supported":45,"title":75,"abstract":76,"tags":78},"C",[],[77],"畜禽",[],{"code":80,"score":56,"supported":45,"title":81,"abstract":82,"tags":84},"E4",[],[83],"农产品质量",[],{"code":86,"score":56,"supported":45,"title":87,"abstract":88,"tags":90},"F1",[],[89],"种质资源",[],{"code":92,"score":20,"supported":14,"title":93,"abstract":94,"tags":95},"I2",[],[],[26],[97,98],"山东省农科院 舜耕论坛 人工智能","何勇 作物表型 智能感知","山东省农科院舜耕论坛人工智能-3103","2026-09-22T00:05:35.424987Z",{"total":19,"page":20,"page_size":19,"items":102},[103,206,279,339,423,520],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":8,"source_name":109,"source_url":106,"published_at":110,"category":111,"cover_url":8,"hotness":112,"is_selected":14,"score":113,"score_detail":114,"sources":119,"tags":124,"primary_topic":127,"secondary_topics":128,"classification":129,"search_phrases":175,"slug":178,"view_count":179,"doi":180,"paper":181,"created_at":205},4814,"Wearable Sensors for Smart Agriculture: From Sensing Mechanisms to Crop Phenotyping and Harvesting Applications","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16202210","Global food security faces severe pressures from population growth, climate change and resource shortages. Traditional agricultural monitoring relies on manual sampling and offline laboratory testing, which involve considerable time delays and cannot realize continuous, in situ dynamic tracking of crop physiological status, field environment and agricultural product quality. Among the various sensing platforms for agriculture, flexible wearable sensors represent an important branch, featuring light weight, conformability, low power consumption and real-time response. It should be noted that rigid-substrate and silicon-based sensors remain indispensable in many agricultural applications, and this review focuses specifically on flexible wearable sensors while acknowledging the broader landscape of agricultural sensing technologies. Through conformal contact with plant surfaces or wearable integration, they can convert physical, chemical and biological signals of agroecosystems into readable electrical\u002Foptical signals via specific sensing transduction mechanisms. This review systematically categorizes wearable sensors for smart agriculture into two main types, optical and electrical, and elaborates their intrinsic sensing principles, summarizes typical application scenarios covering crop growth monitoring, post-harvest harvesting and agricultural product safety detection, and introduces the full-flow signal preprocessing, feature extraction and intelligent analysis technologies matched with wearable sensing data. Finally, the bottlenecks restricting large-scale field deployment of current wearable sensors are summarized, and the development directions of multi-modal integrated wearable sensing, biomimetic nanosensors inspired by natural plant sensing systems, and artificial intelligence (AI)-assisted agricultural monitoring platforms are prospected, aiming to provide a theoretical reference for the design optimization and popularization of agricultural wearable sensing equipment.","全球粮食安全面临人口增长、气候变化和资源短缺的严峻压力。传统农业监测依赖人工采样和离线实验室检测，存在较大的时间延迟，无法实现对作物生理状态、田间环境和农产品品质的连续、原位动态追踪。在各类农业传感平台中，柔性可穿戴传感器是一个重要分支，具有轻量、共形、低功耗和实时响应等特点。需要指出的是，刚性基底和硅基传感器在许多农业应用中仍不可或缺，本综述聚焦于柔性可穿戴传感器，同时承认农业传感技术的更广泛格局。通过与植物表面的共形接触或可穿戴集成，柔性可穿戴传感器能够借助特定的传感转导机制，将农业生态系统的物理、化学和生物信号转化为可读的电信号\u002F光信号。本综述将面向智慧农业的可穿戴传感器系统性地分为光学和电学两大类，阐述其内在传感原理，总结涵盖作物生长监测、采后收获和农产品安全检测的典型应用场景，并介绍与可穿戴传感数据匹配的全流程信号预处理、特征提取和智能分析技术。最后，总结了制约当前可穿戴传感器大规模田间部署的瓶颈，并展望了多模态集成可穿戴传感、受自然植物传感系统启发的仿生纳米传感器以及人工智能（AI）辅助农业监测平台的发展方向，旨在为农业可穿戴传感设备的设计优化与推广应用提供理论参考。","Agriculture","2026-10-11T00:00:00Z","论文",25,77,{"impact":115,"substance":116,"depth":115,"authority":18,"freshness":117,"relevant":20,"comment":118},18,20,8,"系统综述柔性可穿戴传感在智慧农业中的机理与应用，方法梳理扎实，对农业信息化领域有参考价值。",[120,121],{"name":109,"url":106},{"name":122,"url":123},"MDPI Agriculture 16(20) 2210 2026-10-11","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F20\u002F2210",[25,26,27,125,126],"农产品安全","可穿戴传感器",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":130},{"candidates":131,"agricultural_evidence":45},[132,137,143,150,157,163,169],{"code":43,"score":44,"supported":45,"title":133,"abstract":135,"tags":136},[134],"smart agriculture",[25,134],[25],{"code":30,"score":138,"supported":45,"title":139,"abstract":141,"tags":142},9,[140],"crop phenotyping",[],[27],{"code":144,"score":19,"supported":45,"title":145,"abstract":146,"tags":149},"A8",[],[147,148],"粮食安全","food security",[],{"code":151,"score":19,"supported":45,"title":152,"abstract":153,"tags":156},"H7",[],[154,155],"气候变化","climate change",[],{"code":92,"score":158,"supported":45,"title":159,"abstract":160,"tags":162},4,[],[161],"artificial intelligence",[26],{"code":164,"score":158,"supported":45,"title":165,"abstract":166,"tags":168},"I5",[],[167],"农业传感",[126],{"code":170,"score":56,"supported":45,"title":171,"abstract":172,"tags":174},"H6",[],[173],"农业生态",[],[176,177],"可穿戴传感器 智慧农业","作物表型 传感器","可穿戴传感器智慧农业-4814",0,"10.3390\u002Fagriculture16202210",{"doi":180,"openalex_id":182,"authors":183,"venue":109,"cited_by_count":179,"oa_url":106,"card":197,"direction":203,"ingested_from":204},"W7221545913",[184,186,189,191,193,195],{"name":185,"orcid":8},"Liuyang Qiu",{"name":187,"orcid":188},"Xindong Wang","https:\u002F\u002Forcid.org\u002F0000-0002-8101-7300",{"name":190,"orcid":8},"Xinxin Wu",{"name":192,"orcid":8},"Jie Yu",{"name":194,"orcid":8},"Hao Li",{"name":196,"orcid":8},"Xinai Zhang",{"tldr":198,"method":199,"finding":200,"direction":201,"opportunity":202},"综述柔性可穿戴传感器在智慧农业中的传感机制、作物表型监测与采收应用。","综述光学与电学两类柔性可穿戴传感器原理、应用及信号智能分析技术。","可穿戴传感器可实现作物生理、环境与品质的连续原位监测，但田间规模化部署仍受限。","农业遥感与作物表型","多模态集成、仿生纳米传感与AI辅助平台是突破田间规模化监测瓶颈的关键方向。","智慧农业 \u002F 农业物联网","openalex","2026-10-13T23:30:12.054798Z",{"id":207,"title":208,"url":209,"summary":210,"summary_zh":211,"content":8,"source_name":109,"source_url":209,"published_at":212,"category":111,"cover_url":8,"hotness":13,"is_selected":14,"score":213,"score_detail":214,"sources":217,"tags":219,"primary_topic":221,"secondary_topics":225,"classification":231,"search_phrases":256,"slug":259,"view_count":260,"doi":261,"paper":262,"created_at":278},3164,"Spatiotemporal Deep Learning for Rice Plant Height Estimation from Multi-Temporal UAV RGB Imagery","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16182034","Accurate plant height estimation is important for monitoring crop growth and supporting precision agricultural management. Manual measurements are labor-intensive, while LiDAR-based methods are expensive and require complex processing. UAV photogrammetry provides a lower-cost alternative but remains challenging in flooded rice paddies because of canopy deformation and difficulties in terrain extraction. This study proposes Rice-STNet, a spatiotemporal deep learning framework for end-to-end rice plant height estimation using multi-temporal UAV RGB imagery. Rice-STNet integrates a convolutional neural network for spatial feature extraction, Time2Vec for temporal encoding, and a gated recurrent unit network for modeling temporal dependencies across observation dates. The framework was evaluated using field data collected from rice paddies over two growing seasons. Rice-STNet achieved an R2 of 0.97, a root mean squared error of 1.97 cm, and a mean absolute error of 1.14 cm. It outperformed random forest, support vector regression, a CNN-only baseline, and a UAV photogrammetry-based point-cloud approach. In addition, the framework generated high-resolution plant height maps for field-scale analysis of spatial growth variability. These results underscore the importance of jointly modeling spatial and temporal characteristics for continuously evolving crop traits. The proposed framework offers an accurate, scalable, and non-destructive solution for large-scale crop phenotyping and precision agriculture.","准确的株高估算对于监测作物生长和支持精准农业管理具有重要意义。人工测量劳动强度大，而基于激光雷达（LiDAR）的方法成本高昂且需要复杂的处理。无人机摄影测量提供了一种成本较低的替代方案，但在淹水稻田中仍面临挑战，原因在于冠层变形和地形提取困难。本研究提出了Rice-STNet，一种时空深度学习框架，用于利用多时相无人机RGB影像进行端到端水稻株高估算。Rice-STNet集成了用于空间特征提取的卷积神经网络、用于时间编码的Time2Vec，以及用于建模观测日期之间时间依赖关系的门控循环单元网络。该框架利用两个生长季从稻田采集的田间数据进行了评估。Rice-STNet取得了R²为0.97、均方根误差为1.97 cm、平均绝对误差为1.14 cm的结果。其性能优于随机森林、支持向量回归、仅使用CNN的基线方法以及基于无人机摄影测量的点云方法。此外，该框架生成了高分辨率株高图，用于田块尺度空间生长变异性分析。这些结果凸显了联合建模空间与时间特征对于持续变化的作物性状的重要性。所提出的框架为大规模作物表型分析和精准农业提供了一种准确、可扩展且非破坏性的解决方案。","2026-09-21T00:00:00Z",80,{"impact":115,"substance":215,"depth":115,"authority":18,"freshness":138,"relevant":20,"comment":216},22,"提出时空深度学习框架Rice-STNet，用多时相无人机RGB影像实现水稻株高高精度估算，方法新颖、数据跨两个生长季，对作物表型与精准农业有实用价值。",[218],{"name":109,"url":209},[25,26,59,220,27],"遥感",{"code":55,"slug":222,"name":59,"path":223,"parent_code":224},"rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[226,230],{"code":92,"slug":227,"name":228,"path":229,"parent_code":34},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":14,"version":38,"method":39,"evidence":232},{"candidates":233,"agricultural_evidence":45},[234,239,245,249],{"code":55,"score":115,"supported":45,"title":235,"abstract":236,"tags":238},[222],[59,237,222],"稻田",[59],{"code":92,"score":44,"supported":45,"title":240,"abstract":242,"tags":244},[241],"deep learning",[243,241],"深度学习",[26],{"code":30,"score":117,"supported":45,"title":246,"abstract":247,"tags":248},[],[27,140],[27,220],{"code":43,"score":250,"supported":45,"title":251,"abstract":252,"tags":255},7,[],[253,254],"精准农业","precision agriculture",[25],[257,258],"无人机 RGB 水稻株高","Rice-STNet 水稻表型","无人机RGB水稻株高-3164",2,"10.3390\u002Fagriculture16182034",{"doi":261,"openalex_id":263,"authors":264,"venue":109,"cited_by_count":179,"oa_url":209,"card":273,"direction":201,"ingested_from":204},"W7213887432",[265,268,270],{"name":266,"orcid":267},"Weiguo Wang","https:\u002F\u002Forcid.org\u002F0009-0003-4028-9363",{"name":269,"orcid":8},"Noboru Noguchi",{"name":271,"orcid":272},"Liangliang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-5055-3987",{"tldr":274,"method":275,"finding":276,"direction":201,"opportunity":277},"提出Rice-STNet时空深度学习框架，用多时相无人机RGB影像估算水稻株高。","CNN提取空间特征，Time2Vec编码时间，GRU建模时序依赖，两季稻田数据验","R²达0.97、RMSE 1.97cm，优于随机森林、SVR、纯CNN及点云方法。","可迁移至其他作物与多源遥感融合，探索轻量化模型及实时田间部署。","2026-09-22T23:30:18.545958Z",{"id":280,"title":281,"url":282,"summary":283,"summary_zh":284,"content":8,"source_name":285,"source_url":286,"published_at":287,"category":111,"cover_url":8,"hotness":13,"is_selected":14,"score":288,"score_detail":289,"sources":293,"tags":295,"primary_topic":297,"secondary_topics":298,"classification":299,"search_phrases":314,"slug":317,"view_count":158,"doi":318,"paper":319,"created_at":338},2781,"Evaluating Mesh Reconstruction Methods for Crop Phenotyping","https:\u002F\u002Fdoi.org\u002F10.48550\u002Farxiv.2609.16926","Phenotyping an agricultural crop is crucial for studying its entire life cycle, as it provides vital insights to improve yield and, ultimately, food production. Doing the same for crops grown on remote sites is a challenge for the specialists who cannot be available on-site. 3D reconstruction techniques offer a promising solution to this problem by enabling crop digitization, allowing specialists to access the resulting 3D crop models from anywhere at any time. In this work, we evaluate recent 3D reconstruction pipelines for crop phenotyping. We focus on 7 mesh reconstruction pipelines and measure the fidelity and consistency of their outputs qualitatively and quantitatively. Our results suggest that the meshes produced by the GGGS, PGSR, and 2DGS are preferable to the other pipelines, owing to their quantitative metrics and visually pleasing outputs. The GGGS pipeline is better than the second-best pipeline (2DGS) by about 27\\% on the radar chart with 5 dimensions, namely, User ratings, Chamfer distance, LPIPS, PSNR, and SSIM.","对农作物进行表型分析对于研究其整个生命周期至关重要，因为它为提高产量并最终提升粮食生产提供了关键见解。对于生长在偏远地区的作物而言，由于专家无法亲临现场，开展同样的表型分析是一项挑战。三维重建技术通过实现作物数字化，使专家能够随时随地访问生成的作物三维模型，从而为这一问题提供了有前景的解决方案。在本研究中，我们评估了近期用于作物表型分析的三维重建流程。我们聚焦于7种网格重建流程，并对其输出的保真度和一致性进行了定性和定量评估。结果表明，GGGS、PGSR和2DGS生成的网格在定量指标和视觉输出方面优于其他流程。在包含5个维度（用户评分、倒角距离、LPIPS、PSNR和SSIM）的雷达图上，GGGS流程比排名第二的2DGS流程高出约27%。","arXiv (Cornell University)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.16926","2026-09-15T00:00:00Z",74,{"impact":290,"substance":116,"depth":291,"authority":18,"freshness":117,"relevant":20,"comment":292},16,17,"系统评测7种网格重建流程用于作物表型数字化，结论明确、指标可量化，对远程作物表型与三维数字化研究有实质参考价值。",[294],{"name":285,"url":286},[25,26,220,27,296],"三维重建",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":300},{"candidates":301,"agricultural_evidence":45},[302,306,310],{"code":30,"score":290,"supported":45,"title":303,"abstract":304,"tags":305},[140],[27,140],[27,220],{"code":92,"score":260,"supported":14,"title":307,"abstract":308,"tags":309},[],[],[296,26],{"code":43,"score":20,"supported":14,"title":311,"abstract":312,"tags":313},[],[],[25],[315,316],"农业人工智能 三维重建 作物表型 智慧农业","农业人工智能 三维重建","农业人工智能三维重建作物表型智慧农业-2781","10.48550\u002Farxiv.2609.16926",{"doi":318,"openalex_id":320,"authors":321,"venue":285,"cited_by_count":179,"oa_url":332,"card":333,"direction":201,"ingested_from":204},"W7213397688",[322,325,327,330],{"name":323,"orcid":324},"Karanvir Singh","https:\u002F\u002Forcid.org\u002F0009-0003-0484-119X",{"name":326,"orcid":8},"Theo Morales",{"name":328,"orcid":329},"Binh‐Son Hua","https:\u002F\u002Forcid.org\u002F0000-0002-5706-8634",{"name":331,"orcid":8},"Mukesh Saini","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2609.16926",{"tldr":334,"method":335,"finding":336,"direction":201,"opportunity":337},"评估7种网格重建流程在作物表型三维数字化中的保真度与一致性。","对比7种3D重建流程，用Chamfer距离、LPIPS、PSNR、SSIM及用户","GGGS、PGSR和2DGS输出更优，GGGS在五维雷达图上比2DGS高约27%。","可探索轻量化、田间实时三维重建，并建立作物表型专用网格质量评价标准。","2026-09-17T23:30:27.096274Z",{"id":340,"title":341,"url":342,"summary":343,"summary_zh":344,"content":8,"source_name":345,"source_url":342,"published_at":346,"category":111,"cover_url":8,"hotness":13,"is_selected":14,"score":347,"score_detail":348,"sources":350,"tags":352,"primary_topic":354,"secondary_topics":355,"classification":356,"search_phrases":372,"slug":375,"view_count":260,"doi":376,"paper":377,"created_at":422},1303,"PhenoStream: A Cyberinfrastructure for Automated and AI-Based Crop Trait Extraction from Aerial Imagery","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.08.26.747008","The integration of digital technologies for high-throughput field phenotyping is critical for accelerating crop improvement in agriculture. However, extracting traits from remote sensing data remains constrained by fragmented workflows, manual intervention, and limited interoperability among existing tools, resulting in delays that hinder timely biological insight and decision-making. To address these challenges, we present PhenoStream (Phenotyping Streaming), a scalable, end-to-end cyberinfrastructure designed to automate the full lifecycle of aerial imagery-based phenotyping, from data acquisition to plot- and genotype-level inference. The framework integrates automated data ingestion from distributed field sites, geospatial processing, and AI-enabled trait extraction within a unified, user-accessible graphical interface. Its modular and extensible architecture supports adaptable trait modeling and seamless integration of new data sources, enabling deployment across diverse crops, environments, and experimental designs. We demonstrate the system across a large multi-location field trial network of bioenergy crops, where it enables high-throughput characterization of spatiotemporal growth dynamics, genotype-by-environment (GxE) interactions, and predictive modeling of key agronomic traits. By significantly reducing processing latency and manual effort, the platform facilitates near-real-time analysis and reproducible workflows. This work establishes a generalizable and scalable pathway for operationalizing very-high-spatial resolution aerial phenotyping in agricultural research. By bridging data acquisition and analytics, the end-to-end cyberinfrastructure provides a foundation for integrating heterogeneous and unstructured data streams--including remote sensing, environmental, and management data--toward data-driven decision making in agriculture.","将数字技术整合用于高通量田间表型分析，对于加速农业作物改良至关重要。然而，从遥感数据中提取性状仍受制于碎片化的工作流程、人工干预以及现有工具间有限的互操作性，导致处理延迟，阻碍了及时的生物学洞察与决策制定。为应对这些挑战，我们提出了PhenoStream（表型流），一种可扩展的端到端网络基础设施，旨在自动化基于航空影像的表型分析全生命周期，从数据采集到小区及基因型水平推断。该框架在统一且用户友好的图形界面中集成了来自分布式田间站点的自动化数据摄取、地理空间处理及人工智能驱动的性状提取。其模块化和可扩展架构支持适应性性状建模及新数据源的无缝整合，使其能够部署于多种作物、环境及实验设计之中。我们在一个大型多地点生物能源作物田间试验网络中展示了该系统，实现了时空生长动态的高通量表征、基因型-环境（GxE）互作分析以及关键农艺性状的预测建模。通过显著降低处理延迟和人工投入，该平台支持近实时分析和可重复的工作流程。本研究为农业研究中超高空间分辨率航空表型分析的操作化提供了一条通用且可扩展的路径。通过连接数据采集与分析，该端到端网络基础设施为整合异构和非结构化数据流——包括遥感、环境及管理数据——奠定了面向农业数据驱动决策的基础。","bioRxiv (Cold Spring Harbor Laboratory)","2026-08-30T00:00:00Z",82,{"impact":215,"substance":215,"depth":115,"authority":17,"freshness":117,"relevant":20,"comment":349},"提出端到端自动化表型提取平台，显著提升效率，具推广价值。",[351],{"name":345,"url":342},[25,26,220,27,353],"田间表型",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":357},{"candidates":358,"agricultural_evidence":45},[359,364,368],{"code":30,"score":117,"supported":45,"title":360,"abstract":361,"tags":363},[],[220,362],"remote sensing",[27,220],{"code":43,"score":20,"supported":14,"title":365,"abstract":366,"tags":367},[],[],[25],{"code":92,"score":20,"supported":14,"title":369,"abstract":370,"tags":371},[],[],[26],[373,374],"农业人工智能 作物表型 智慧农业 田间表型","农业人工智能 作物表型","农业人工智能作物表型智慧农业田间表型-1303","10.64898\u002F2026.08.26.747008",{"doi":376,"openalex_id":378,"authors":379,"venue":345,"cited_by_count":179,"oa_url":415,"card":416,"direction":203,"ingested_from":204},"W7204800500",[380,383,385,388,390,392,394,396,399,401,404,406,408,410,412],{"name":381,"orcid":382},"Sebastián Varela","https:\u002F\u002Forcid.org\u002F0000-0001-8064-3636",{"name":384,"orcid":8},"Jeremy Ruhter",{"name":386,"orcid":387},"Erik J. Sacks","https:\u002F\u002Forcid.org\u002F0000-0001-7537-4558",{"name":389,"orcid":8},"Xuying Zheng",{"name":391,"orcid":8},"Dylan Allen",{"name":393,"orcid":8},"Anna Hale",{"name":395,"orcid":8},"Cory Landry",{"name":397,"orcid":398},"Xianyan Kuang","https:\u002F\u002Forcid.org\u002F0000-0002-5978-647X",{"name":400,"orcid":8},"B. J. Long",{"name":402,"orcid":403},"Yan Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-5804-8926",{"name":405,"orcid":8},"Shatabdi Proma",{"name":407,"orcid":8},"Sehijpreet Kaur",{"name":409,"orcid":8},"Diego Jarquin",{"name":411,"orcid":8},"Jesse Morrison",{"name":413,"orcid":414},"Andrew D. B. Leakey","https:\u002F\u002Forcid.org\u002F0000-0001-6251-024X","https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2026\u002F08\u002F30\u002F2026.08.26.747008.full.pdf",{"tldr":417,"method":418,"finding":419,"direction":420,"opportunity":421},"提出 PhenoStream 网络基础设施，自动化航空影像作物性状提取全流程。","集成数据采集、地理空间处理与 AI 性状提取的模块化架构。","在多地点生物能源作物试验中实现高通量表型分析，显著降低延迟。","农业人工智能与决策模型","可扩展至多源数据融合与实时决策，探索跨作物通用性状模型及边缘计算部署。","2026-09-01T23:30:11.619805Z",{"id":424,"title":425,"url":426,"summary":427,"summary_zh":428,"content":8,"source_name":429,"source_url":426,"published_at":430,"category":111,"cover_url":8,"hotness":13,"is_selected":14,"score":431,"score_detail":432,"sources":434,"tags":436,"primary_topic":440,"secondary_topics":441,"classification":443,"search_phrases":493,"slug":496,"view_count":179,"doi":497,"paper":498,"created_at":519},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":17,"substance":115,"depth":290,"authority":138,"freshness":13,"relevant":20,"comment":433},"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。",[435],{"name":429,"url":426},[25,26,437,438,439],"无损检测","多光谱成像","食品质量检测",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[442],{"code":92,"slug":227,"name":228,"path":229,"parent_code":34},{"status":37,"locked":14,"version":38,"method":39,"evidence":444},{"candidates":445,"agricultural_evidence":45},[446,451,459,465,470,476,483,489],{"code":30,"score":17,"supported":45,"title":447,"abstract":449,"tags":450},[448],"multispectral",[448],[438],{"code":92,"score":13,"supported":45,"title":452,"abstract":453,"tags":458},[],[243,454,455,456,241,161,457],"机器学习","计算机视觉","machine learning","computer vision",[26],{"code":80,"score":250,"supported":45,"title":460,"abstract":461,"tags":464},[],[462,463],"食品安全","food safety",[437],{"code":55,"score":19,"supported":45,"title":466,"abstract":467,"tags":469},[],[468,222],"稻米",[],{"code":62,"score":19,"supported":45,"title":471,"abstract":472,"tags":475},[],[473,474],"番茄","tomato",[],{"code":477,"score":19,"supported":45,"title":478,"abstract":479,"tags":482},"B2",[],[480,481],"苹果","apple",[],{"code":484,"score":56,"supported":45,"title":485,"abstract":486,"tags":488},"E1",[],[487],"农产品加工",[],{"code":43,"score":20,"supported":14,"title":490,"abstract":491,"tags":492},[],[],[25],[494,495],"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":497,"openalex_id":499,"authors":500,"venue":429,"cited_by_count":179,"oa_url":426,"card":513,"direction":518,"ingested_from":204},"W7223139905",[501,503,505,507,509,511],{"name":502,"orcid":8},"Eshwari Take",{"name":504,"orcid":8},"Avanish Raut",{"name":506,"orcid":8},"Vedant Joshi",{"name":508,"orcid":8},"Shravani Akolkar",{"name":510,"orcid":8},"R. M. Gharat",{"name":512,"orcid":8},"Shruti Dive",{"tldr":514,"method":515,"finding":516,"direction":420,"opportunity":517},"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。","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":521,"title":522,"url":523,"summary":524,"summary_zh":525,"content":8,"source_name":526,"source_url":523,"published_at":430,"category":111,"cover_url":8,"hotness":13,"is_selected":14,"score":213,"score_detail":527,"sources":530,"tags":532,"primary_topic":536,"secondary_topics":541,"classification":548,"search_phrases":577,"slug":580,"view_count":179,"doi":581,"paper":582,"created_at":608},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":290,"substance":215,"depth":115,"authority":528,"freshness":13,"relevant":20,"comment":529},14,"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。",[531],{"name":526,"url":523},[25,26,533,534,535],"农业遥感","氮素监测","紫花苜蓿",{"code":537,"slug":538,"name":539,"path":540,"parent_code":224},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping",[542,543],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"code":544,"slug":545,"name":546,"path":547,"parent_code":74},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed",{"status":37,"locked":14,"version":38,"method":39,"evidence":549},{"candidates":550,"agricultural_evidence":45},[551,556,562,568,573],{"code":30,"score":115,"supported":45,"title":552,"abstract":554,"tags":555},[553],"hyperspectral",[220,362,553],[533],{"code":537,"score":44,"supported":45,"title":557,"abstract":559,"tags":561},[558],"alfalfa",[560,558],"苜蓿",[535],{"code":544,"score":13,"supported":45,"title":563,"abstract":564,"tags":567},[],[565,560,566],"饲草","forage",[535],{"code":92,"score":13,"supported":45,"title":569,"abstract":570,"tags":572},[],[243,454,456,241,571],"decision support",[26],{"code":43,"score":20,"supported":14,"title":574,"abstract":575,"tags":576},[],[],[25],[578,579],"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":581,"openalex_id":583,"authors":584,"venue":526,"cited_by_count":179,"oa_url":523,"card":603,"direction":201,"ingested_from":204},"W7223154576",[585,587,589,591,593,596,598,601],{"name":586,"orcid":8},"Shuai Fu",{"name":588,"orcid":8},"Yunhao Li",{"name":590,"orcid":8},"Chunli Miao",{"name":592,"orcid":8},"Jie Liu",{"name":594,"orcid":595},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":597,"orcid":8},"Yongjia Hu",{"name":599,"orcid":600},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":602,"orcid":8},"吴彩霞",{"tldr":604,"method":605,"finding":606,"direction":201,"opportunity":607},"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"]