[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5516":3,"related-5516":67},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":61,"slug":64,"view_count":65,"doi":8,"paper":8,"created_at":66},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。",null,"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",10,false,65,{"impact":17,"substance":18,"depth":19,"authority":19,"freshness":20,"relevant":21,"comment":22},16,17,13,6,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","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},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,50,56],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},12,true,[28],[28],[28],{"code":51,"score":52,"supported":46,"title":53,"abstract":54,"tags":55},"I1",4,[],[26],[26],{"code":57,"score":21,"supported":14,"title":58,"abstract":59,"tags":60},"F5",[],[],[27],[62,63],"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",0,"2026-10-22T00:07:04.061911Z",{"total":20,"page":21,"page_size":20,"items":68},[69,124,190,268,341,414],{"id":70,"title":71,"url":72,"summary":73,"summary_zh":8,"content":74,"source_name":75,"source_url":8,"published_at":76,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":77,"score_detail":78,"sources":84,"tags":86,"primary_topic":90,"secondary_topics":91,"classification":92,"search_phrases":119,"slug":122,"view_count":82,"doi":8,"paper":8,"created_at":123},3387,"从会种粮到'慧'种粮——中联重科岳麓智慧农场、常州艾宝机器人、温氏华系种猪、牧原智能猪舍AI兽医集体亮相","https:\u002F\u002Fnews.cyol.com\u002Fgb\u002Fkeji\u002Farticles\u002F2026-09\u002F23\u002Fcontent_v6vKznCP2x.html","中联重科岳麓智慧农场'天空地人机'协同智慧作业模式搭载多光谱相机巡田无人机最高飞120米、20分钟完成整片地块巡检，长势图精度达3米×3米网格；常州艾宝机器人四臂采摘猕猴桃机器人经第三方检测适合山坡丘陵地形，一台机器能节约7至8个人工；温氏股份、德康农牧'华系种猪育种技术与核心种源创制及应用'项目获国家科学技术进步奖一等奖；牧原股份智能化猪舍AI兽医全天候值守。","◎记者 刘立 王乔琪\n\n又是一年丰收季。田野之间，沉甸甸的果实背后，农业生产逻辑正在发生深刻变化。\n\n今年5月，国务院印发的《加快农业农村现代化“十五五”规划》明确提出：“推进人工智能运用和智慧农业发展。加快农业人工智能技术创新、应用场景拓展和产业生态营造。”\n\n近期，上海证券报记者在对部分农业种养殖龙头企业和农业展会的调研中发现，从种子实验室到果园大田，智能化工具正在把农业从经验驱动推向数据驱动。AI不再只是实验室概念，它正从育种、田间种植延伸到畜牧水产全链条，改写着传统农业依靠经验、看天吃饭的旧模式。\n\n**以AI打磨农业“芯片”**\n\n种业被称作农业的“芯片”。记者在采访中发现，快速发展的AI技术，正在缩短育种探索的漫长周期。\n\n在近日举办的全国种子双交会上，中种集团展台上一台ELISA蛋白全自动分析检测工作站设备吸引不少参会者驻足。据工作人员介绍，传统育种蛋白检测多采用人工、半自动化操作，通量低、稳定性差，而这台设备针对生物育种性状鉴定、蛋白定量筛查场景，可实现实验全流程无人自动化运行，日检测通量比人工高5倍以上，成本降低50%，且完全国产化。\n\n记者在采访中发现，AI正在把复杂的农业科研数据转化成通俗直观的结论。育种不再完全依靠科研人员肉眼观察记录，而是由仪器采集数据，AI完成解析筛选。\n\n上海泽泉科技股份有限公司研发的花粉活力仪可以应用于玉米、小麦、果树、大豆杂交育种项目，帮助科研人员精准把握授粉窗口，稳定后代品质。公司副总经理张凯峰对记者说：“过去做大豆、玉米、小麦等杂交育种，收集花粉之后，不知道花粉活性。我们的设备可以对每一粒花粉活性做量化测定，活性不达标的花粉直接淘汰。随着科技的普及，我相信，未来这些智能设备都会逐步下沉到普通农户。”\n\n一位农业专家对记者表示，AI并非颠覆传统育种，而是对育种体系的升级赋能。智能育种的核心，是依靠高通量设备获取标准化的基因组、表型组数据，依托算法挖掘基因与性状之间的关联，推动育种从过去依赖经验的“试错筛选”，转向数据驱动的“精准设计”，助力我国种业向育种4.0阶段迈进。\n\n**“空天地一体化”重构农业生产模式**\n\n在位于湖南长沙的中联重科岳麓智慧农场，中联智慧农业打造的“天空地人机”协同智慧作业模式，正在改写大田管护方式。\n\n以往人工巡查四五百亩稻田，要耗费一至两小时。中联智慧农业这套搭载多光谱相机的巡田无人机最高可飞120米，20分钟就能完成整片地块巡检，变焦可达100多倍，捕捉到虫害、长势异常等疑点便低空补拍核验。影像实时回传云端后，AI算法输出水稻长势图，精度可达3米乘以3米网格，明确标注地块补水、补肥位置与用量。\n\n天上完成巡查，地上设备随即落地执行。中联智慧农业部署的物联网灌排设备、微型气象站同步采集风、雨、光照数据，农户通过手机软件就能查看农事日历、操控田间设备。这种“按数据决策”的模式，改变了传统农业“凭经验跑田”的粗放管理方式。\n\n除了天上飞的无人机，地上走的机器人也开始应用到更多农业生产场景。\n\n在北京平谷的2026世界农业科技创新大会上，常州艾宝机器人有限公司研发的猕猴桃采摘机器人吸引了不少观众。公司工程师赵炳宇向记者介绍，这款四臂采摘机器人，经第三方机构检测，适合山坡丘陵地形，一台机器能够节约7至8个人工。现阶段设备尚处在定制阶段，已有3家企业完成定制采购。\n\n“我们主要从事AI智能水果采摘机器人研发，已突破多臂协同、视觉感知、自主导航等关键技术，已经推出苹果、葡萄、猕猴桃采摘等系列产品，目的就是推动采摘机器人从技术示范走向生产落地。”赵炳宇说。\n\n**农牧业智能化驶入快车道**\n\n中国是全球最大的猪肉消费国，年消费猪肉超过5000万吨。庞大的产业规模背后，是管理规模大、场景复杂的生猪养殖体系。如今，人工智能正加速渗透进这个最传统的行业。\n\n畜牧种业是畜牧业的“芯片”，在很长一段时间，国内要从海外进口种猪，还要高价采购海外生猪基因芯片，支付高昂的知识产权费。通常一头外引种猪的综合成本高达3万元至5万元。\n\n这一局面如今已被打破。日前，温氏股份、德康农牧等作为主要完成单位参与的“华系种猪育种技术与核心种源创制及应用”项目，荣获国家科学技术进步奖一等奖。该项目通过基因组选择、表型精准测定等技术创新，构建了华系种猪自主育种体系，创制了具有自主知识产权的核心种猪群体，标志着我国生猪核心种源实现高标准自主可控，彻底打破国外长期垄断格局。\n\n在河南，畜牧业龙头企业牧原股份的智能化猪舍里，AI兽医已投入全天候值守。\n\n“过去发现猪不吃食，饲养员找兽医、翻手册、凭经验判断，平均响应时间需要个把小时；现在从发现异常到推荐用药的闭环，仅需数秒。”牧原股份技术负责人介绍，名为“小牧助手”的AI应用，融合企业30余年养殖知识与全产业链330万套智能装备每日产生的20亿条数据，被封装为可嵌入日常流程的AI智能体。目前，牧原与阿里云合作共建的养猪大模型“猪小牧”已向全行业开放共享，助力行业高质量发展。\n\n此外，类似变革正在更多养殖场景中铺开。比如在水产养殖领域，广西防城港东兴市红树林农业有限公司的养殖基地引入AI“厂长”，集成AI算法、高清水下摄像头与传感器网络，24小时实时采集虾群数据，自动调控水阀与投料，每日精准投喂300余次。目前，该基地一年可养对虾5茬，年产量35万至40万公斤。\n\n【责任编辑：母建鑫】","中青在线 2026-09-23","2026-09-23T00:00:00Z",85,{"impact":79,"substance":80,"depth":81,"authority":45,"freshness":82,"relevant":21,"comment":83},26,22,18,7,"紧扣十五五规划AI+农业主线，覆盖育种、种植、养殖全链条，案例与数据扎实，具行业风向标价值。",[85],{"name":75,"url":72},[26,87,27,88,89],"农业人工智能","种业振兴","智慧养殖",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":93},{"candidates":94,"agricultural_evidence":46},[95,101,106,110,114],{"code":32,"score":96,"supported":46,"title":97,"abstract":98,"tags":100},3,[],[99],"猕猴桃",[],{"code":102,"score":21,"supported":14,"title":103,"abstract":104,"tags":105},"F1",[],[],[88],{"code":57,"score":21,"supported":14,"title":107,"abstract":108,"tags":109},[],[],[27],{"code":51,"score":21,"supported":14,"title":111,"abstract":112,"tags":113},[],[],[26],{"code":115,"score":21,"supported":14,"title":116,"abstract":117,"tags":118},"I2",[],[],[87],[120,121],"中联重科 岳麓智慧农场","温氏 华系种猪","中联重科岳麓智慧农场-3387","2026-09-25T00:09:27.645047Z",{"id":125,"title":126,"url":127,"summary":128,"summary_zh":129,"content":8,"source_name":130,"source_url":127,"published_at":76,"category":131,"cover_url":8,"hotness":13,"is_selected":14,"score":132,"score_detail":133,"sources":138,"tags":140,"primary_topic":144,"secondary_topics":145,"classification":146,"search_phrases":165,"slug":168,"view_count":96,"doi":169,"paper":170,"created_at":189},3255,"Grape yield estimation using on-the-fly MIMO millimeter-wave radar at operational field speed: A variety-dependent proof of concept","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112395","Accurate pre-harvest grape yield estimation is a critical challenge in precision viticulture, yet current optical approaches (RGB cameras, LiDAR) are fundamentally limited by foliage occlusion and sensitivity to ambient lighting conditions. This paper presents a ground-based grape yield estimation system using a dual 77 GHz Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous-Wave (FM-CW) radar mounted on a rover operating at an average speed of 1.0 m\u002Fs (3.7 km\u002Fh), matching practical vineyard tractor speeds. Unlike prior radar-based studies conducted in static or slow-moving setups, the proposed pipeline achieves continuous 3D reconstruction of vinerows through digital beamforming in the elevation plane, combined with an IMU-fused Kalman filter for point cloud stabilization on irregular terrain. An experimental campaign was conducted on 136 km of scanned vinerows across seven grape varieties and three phenological growth stages (BBCH 75–89) in a commercial vineyard in Gascony, France. Statistical features extracted from 3D radar echo level images are used to train a Support Vector Regressor (SVR) evaluated in cross-validation. The primary out-of-sample result is a cross-validation Mean Absolute Error (CV MAE) of 31.2% on the full multi-variety dataset without any defoliation, achieved against certified wine-vat bulk weights as the ground truth reference. This figure should be contextualized against the ∼ 24–25% discrepancy observed between portable field weighing plates and vat-certified weights across the same dataset, and against manual pre-harvest estimation errors routinely exceeding 30% in viticultural practice. A sensitivity analysis over physical parameters reveals that grape variety is the dominant −and limiting- source of performance heterogeneity: CV MAE ranges from 7% for Tannat to 69% for Baco, demonstrating that a single global model is insufficient for multi-variety deployment and that variety-specific calibration is a necessary condition for operational use. These results are best understood as an architectural proof of concept: the primary contribution is the demonstration that MIMO digital beamforming removes the speed bottleneck of prior mechanically-scanned radar systems, enabling gapless 3D vinerow reconstruction at practical field speeds. The yield estimation results provide a first quantitative characterization of the system’s sensing capability and its variety-dependent limitations and establish a baseline for future variety-specific model development.","准确的采前葡萄产量估测是精准葡萄栽培中的一项关键挑战，然而当前的光学方法（RGB相机、LiDAR）从根本上受到冠层遮挡和对环境光照条件敏感性的限制。本文提出了一种地基葡萄产量估测系统，采用双77 GHz多输入多输出（MIMO）调频连续波（FM-CW）雷达，搭载于以平均速度1.0 m\u002Fs（3.7 km\u002Fh）行驶的移动平台上，与实际葡萄园拖拉机作业速度相匹配。与先前在静态或慢速移动装置中进行的雷达研究不同，所提出的处理流程通过仰角平面上的数字波束成形实现葡萄行连续三维重建，并结合IMU融合卡尔曼滤波器在不规则地形上实现点云稳定。在法国加斯科尼的一个商业葡萄园中，对七个葡萄品种和三个物候生长期（BBCH 75–89）的136 km扫描葡萄行进行了实验。从三维雷达回波强度图像中提取的统计特征用于训练支持向量回归器（SVR），并通过交叉验证进行评估。主要样本外结果为：在完整多品种数据集上、未进行任何去叶处理的情况下，交叉验证平均绝对误差（CV MAE）为31.2%，以认证酒罐散装重量作为地面真值参考。该数值应结合以下背景加以理解：在同一数据集上，便携式田间称重板与酒罐认证重量之间观察到约24–25%的差异，而葡萄栽培实践中人工采前估测误差通常超过30%。对物理参数的敏感性分析表明，葡萄品种是性能异质性的主要——也是限制性——来源：CV MAE从Tannat的7%到Baco的69%不等，表明单一全局模型不足以支持多品种部署，品种特异性校准是实际应用的必要条件。这些结果最好被理解为一种架构层面的概念验证：主要贡献在于证明了MIMO数字波束成形消除了先前机械扫描雷达系统的速度瓶颈，从而能够在实际田间速度下实现无间隙三维葡萄行重建。产量估测结果首次定量表征了该系统的感知能力及其品种依赖性局限，并为未来","Computers and Electronics in Agriculture","论文",84,{"impact":81,"substance":134,"depth":135,"authority":136,"freshness":13,"relevant":21,"comment":137},23,19,14,"首次在实用田间车速下用MIMO毫米波雷达实现葡萄产量预估，136公里商业园数据与品种依赖性结论具参考价值。",[139],{"name":130,"url":127},[26,141,28,142,143],"精准农业","产量预估","毫米波雷达",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":147},{"candidates":148,"agricultural_evidence":46},[149,155,160],{"code":32,"score":150,"supported":46,"title":151,"abstract":153,"tags":154},15,[152],"grape",[28,152],[28],{"code":57,"score":96,"supported":46,"title":156,"abstract":157,"tags":159},[],[158],"tractor",[],{"code":51,"score":161,"supported":14,"title":162,"abstract":163,"tags":164},2,[],[],[26,141],[166,167],"MIMO 毫米波雷达 葡萄 产量预估","葡萄园 雷达 三维重建","MIMO毫米波雷达葡萄产量预估-3255","10.1016\u002Fj.compag.2026.112395",{"doi":169,"openalex_id":171,"authors":172,"venue":130,"cited_by_count":65,"oa_url":127,"card":182,"direction":186,"ingested_from":188},"W7214043753",[173,176,179],{"name":174,"orcid":175},"Etienne Dedic","https:\u002F\u002Forcid.org\u002F0000-0002-1414-019X",{"name":177,"orcid":178},"Dominique Henry","https:\u002F\u002Forcid.org\u002F0000-0002-6016-140X",{"name":180,"orcid":181},"H. Aubert","https:\u002F\u002Forcid.org\u002F0000-0002-6113-0648",{"tldr":183,"method":184,"finding":185,"direction":186,"opportunity":187},"用双77GHz MIMO雷达在1m\u002Fs车速下重建葡萄藤行并估算产量，验证品种依赖性。","双77GHz MIMO FMCW雷达、数字波束成形、IMU卡尔曼滤波、SVR，1","全品种交叉验证MAE为31.2%，但品种间差异大（Tannat 7%至Baco 69%），需按品种校","农业遥感与作物表型","可研究品种自适应建模与迁移学习，解决多品种部署时单一模型失效问题，并融合光学与雷达数据。","openalex","2026-09-23T23:30:01.516727Z",{"id":191,"title":192,"url":193,"summary":194,"summary_zh":195,"content":8,"source_name":196,"source_url":193,"published_at":197,"category":131,"cover_url":8,"hotness":13,"is_selected":14,"score":198,"score_detail":199,"sources":203,"tags":205,"primary_topic":208,"secondary_topics":209,"classification":216,"search_phrases":246,"slug":249,"view_count":161,"doi":250,"paper":251,"created_at":267},2874,"Laboratory Evaluation of Multispectral Grape Quality Grade Prediction with Compensation for Controlled Illumination Variation","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2631-8695\u002Faea952","Abstract Accurate fruit quality assessment is important for selective harvesting in agricultural robotics, where multispectral sensing provides a non-destructive approach but remains sen- sitive to variability in illumination conditions. This study presents a controlled laboratory evaluation conducted in a dark box under halogen illumination with fixed sensor and sam- ple geometry, in which illumination variation was simulated by adjusting lamp intensity. Prediction models for visually assigned grape quality grades were first established under fixed illumination using an AS7265x multispectral sensor with 18 channels from 410 to 940 nm, covering the visible and part of the near-infrared range. Spectral data were prepro- cessed using standard normal variate transformation, standardization, variable importance in projection feature selection, and mixup interpolation. Three machine learning meth- ods, partial least squares regression, support vector regression, and random forest, were evaluated with nested cross-validation, out-of-fold prediction, and 2000-iteration bootstrap resampling. Support vector regression achieved the best performance, with R² = 0.9084 [95% CI: 0.851–0.945], RMSE = 0.3352, and MAE = 0.2374. To address illumination vari- ability, three compensation methods, direct standardization, extended multiplicative scatter correction, and a polynomial per-band fitting method, were implemented and compared across five lamp intensity levels from 20% to 100% of rated power. The three methods and the uncompensated condition reached similar mean accuracy across the five levels, with the exception of extended multiplicative scatter correction, which was lower at every level. Di- rect standardization reduced the spread of the five predictions obtained for the same berry by about 31% relative to no compensation, and gave the highest grade-level accuracy of the four conditions. These results provide laboratory feasibility evidence that direct standard- ization can improve the consistency of grade prediction across controlled illumination levels, and establish a basis for further validation under natural field illumination toward future robotic harvesting applications.","摘要 准确的水果品质评估对于农业机器人中的选择性采收至关重要，其中多光谱传感提供了一种非破坏性方法，但仍对光照条件的变化较为敏感。本研究提出了一种在暗箱中、卤素灯照明下、传感器与样品几何构型固定的受控实验室评估方法，通过调节灯强度来模拟光照变化。首先在固定光照条件下，使用AS7265x多光谱传感器（具有410至940 nm范围内的18个通道，覆盖可见光及部分近红外波段）建立了视觉 assigned 葡萄品质等级的预测模型。光谱数据采用标准正态变量变换、标准化、投影变量重要性特征选择以及mixup插值进行预处理。采用嵌套交叉验证、折外预测和2000次迭代自助重采样，对偏最小二乘回归、支持向量回归和随机森林三种机器学习方法进行了评估。支持向量回归取得了最佳性能，R² = 0.9084 [95% CI: 0.851–0.945]，RMSE = 0.3352，MAE = 0.2374。为解决光照变化问题，实施了直接标准化、扩展多元散射校正和逐波段多项式拟合三种补偿方法，并在额定功率20%至100%的五个灯强度水平下进行了比较。三种方法及未补偿条件在五个水平下达到了相似的平均准确率，但扩展多元散射校正除外，其在每个水平下均较低。与未补偿相比，直接标准化使同一浆果五次预测结果的离散度降低了约31%，并在四种条件中给出了最高的等级准确率。这些结果为直接标准化能够提高受控光照水平下等级预测的一致性提供了实验室可行性证据，并为未来机器人采收应用中在自然田间光照下进一步验证奠定了基础。","Engineering Research Express","2026-09-17T00:00:00Z",66,{"impact":200,"substance":201,"depth":18,"authority":19,"freshness":200,"relevant":21,"comment":202},8,20,"实验室条件下多光谱葡萄品质分级与光照补偿研究，方法扎实、结论可靠，但属实验室可行性验证，产业影响有限。",[204],{"name":196,"url":193},[26,87,28,206,207],"多光谱检测","水果品质分级",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[210],{"code":211,"slug":212,"name":213,"path":214,"parent_code":215},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":217},{"candidates":218,"agricultural_evidence":46},[219,223,229,235,242],{"code":32,"score":150,"supported":46,"title":220,"abstract":221,"tags":222},[152],[28,152],[28],{"code":211,"score":224,"supported":46,"title":225,"abstract":227,"tags":228},11,[226],"multispectral",[226],[],{"code":115,"score":82,"supported":46,"title":230,"abstract":231,"tags":234},[],[232,233],"机器学习","machine learning",[87],{"code":236,"score":20,"supported":46,"title":237,"abstract":238,"tags":241},"F6",[],[239,240],"农业机器人","robotic harvesting",[],{"code":51,"score":21,"supported":14,"title":243,"abstract":244,"tags":245},[],[],[26],[247,248],"多光谱 葡萄 品质分级","AS7265x 多光谱传感器","多光谱葡萄品质分级-2874","10.1088\u002F2631-8695\u002Faea952",{"doi":250,"openalex_id":252,"authors":253,"venue":196,"cited_by_count":65,"oa_url":193,"card":261,"direction":266,"ingested_from":188},"W7213509882",[254,256,258],{"name":255,"orcid":8},"Guodong Xiu",{"name":257,"orcid":8},"Sára Strakošová",{"name":259,"orcid":260},"Ikuo Mizuuchi","https:\u002F\u002Forcid.org\u002F0000-0003-4657-2613",{"tldr":262,"method":263,"finding":264,"direction":186,"opportunity":265},"在暗箱中评估多光谱葡萄品质分级预测，并比较三种光照补偿方法。","AS7265x多光谱传感器、SNV与VIP预处理、SVR\u002FRF建模、直接标准化等","SVR预测最佳（R²=0.9084）；直接标准化使同果预测离散度降低约31%。","可延伸至自然田间光照下的验证，并融合机器人采摘的实时多光谱分级系统。","农业人工智能与决策模型","2026-09-18T23:30:52.790038Z",{"id":269,"title":270,"url":271,"summary":272,"summary_zh":273,"content":8,"source_name":130,"source_url":271,"published_at":274,"category":131,"cover_url":8,"hotness":13,"is_selected":14,"score":275,"score_detail":276,"sources":278,"tags":280,"primary_topic":282,"secondary_topics":283,"classification":289,"search_phrases":310,"slug":313,"view_count":96,"doi":314,"paper":315,"created_at":340},1278,"ERG-Mask: An edge–region collaborative instance segmentation model for greenhouse table-grape clusters","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112369","ERG-Mask: An edge–region collaborative instance segmentation model for greenhouse table-grape clusters。Computers and Electronics in Agriculture","ERG-Mask：面向温室鲜食葡萄果串的边缘-区域协同实例分割模型。《Computers and Electronics in Agriculture》","2026-08-31T00:00:00Z",62,{"impact":45,"substance":81,"depth":17,"authority":136,"freshness":161,"relevant":21,"comment":277},"针对温室葡萄簇的实例分割新方法，发表于核心期刊，技术有创新性，但影响范围限于细分领域，时效性较低。",[279],{"name":130,"url":271},[26,87,281,28],"设施农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[284],{"code":285,"slug":286,"name":287,"path":288,"parent_code":36},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture",{"status":39,"locked":14,"version":40,"method":41,"evidence":290},{"candidates":291,"agricultural_evidence":46},[292,296,302,306],{"code":32,"score":150,"supported":46,"title":293,"abstract":294,"tags":295},[152],[28,152],[28],{"code":285,"score":136,"supported":46,"title":297,"abstract":299,"tags":301},[298],"greenhouse",[300,298],"温室",[],{"code":51,"score":21,"supported":14,"title":303,"abstract":304,"tags":305},[],[],[26],{"code":115,"score":21,"supported":14,"title":307,"abstract":308,"tags":309},[],[],[87],[311,312],"农业人工智能 智慧农业 设施农业 葡萄","农业人工智能 智慧农业","农业人工智能智慧农业设施农业葡萄-1278","10.1016\u002Fj.compag.2026.112369",{"doi":314,"openalex_id":316,"authors":317,"venue":130,"cited_by_count":65,"oa_url":8,"card":335,"direction":266,"ingested_from":188},"W7204777248",[318,321,324,327,329,332],{"name":319,"orcid":320},"Zhengtong Ning","https:\u002F\u002Forcid.org\u002F0000-0002-9218-6425",{"name":322,"orcid":323},"Jian Li","https:\u002F\u002Forcid.org\u002F0000-0002-4187-317X",{"name":325,"orcid":326},"Haidong Li","https:\u002F\u002Forcid.org\u002F0000-0002-6634-9593",{"name":328,"orcid":8},"Liangkuan Zhu",{"name":330,"orcid":331},"Qingjie Wang","https:\u002F\u002Forcid.org\u002F0009-0001-9038-7821",{"name":333,"orcid":334},"Yi Wang","https:\u002F\u002Forcid.org\u002F0000-0002-0192-3195",{"tldr":336,"method":337,"finding":338,"direction":266,"opportunity":339},"提出ERG-Mask模型，用于温室鲜食葡萄串的实例分割，提升边缘与区域协同精度。","结合边缘与区域信息的协同实例分割模型，基于深度学习。","ERG-Mask在葡萄串分割上优于现有方法，精度更高。","可探索该模型在复杂遮挡、不同光照下的鲁棒性，或扩展至其他簇状果实（如番茄、蓝莓）的检测。","2026-09-01T23:30:01.626379Z",{"id":342,"title":343,"url":344,"summary":345,"summary_zh":346,"content":8,"source_name":347,"source_url":344,"published_at":348,"category":131,"cover_url":8,"hotness":13,"is_selected":14,"score":349,"score_detail":350,"sources":353,"tags":355,"primary_topic":359,"secondary_topics":360,"classification":361,"search_phrases":387,"slug":390,"view_count":65,"doi":391,"paper":392,"created_at":413},5401,"A HIERARCHICAL DISTRIBUTED EDGE AI FRAMEWORK FOR REAL-TIME APPLE LEAF DISEASE CLASSIFICATION","https:\u002F\u002Fdoi.org\u002F10.22190\u002Ffuacr260506002g","The application of artificial intelligence (AI) in agriculture has transformed crop management, particularly in the detection of plant diseases. Apple trees, a key agricultural commodity, are vulnerable to various diseases, which can severely impact crop yields. Traditional disease detection methods, often reliant on manual inspections, are time-consuming and prone to error. In response, this study proposes a novel two-stage edge AI framework for apple leaf disease classification that integrates RGB imagery with distributed edge computing. The first stage performs binary classification to distinguish between healthy and diseased leaves, achieving an F1 score of 95.50%. This optimization is achieved by utilizing lightweight models on edge devices, thereby enhancing computational efficiency. The second stage employs a consensus-based multiclass classification approach, achieving a 96.44% F1 score in identifying specific diseases. This dual-stage system, supported by an edge AI architecture, ensures real-time disease detection while minimizing resource consumption and enhancing scalability in large-scale agricultural environments. The full system achieved a 95.50% accuracy and F1 score on the special dataset. The results demonstrate the system’s efficacy in controlled laboratory conditions, offering a promising solution for precise, real-time agricultural monitoring. This study underscores the potential of distributed edge AI systems for scalable and efficient disease detection in precision agriculture.","人工智能（AI）在农业中的应用已深刻改变了作物管理方式，尤其是在植物病害检测方面。苹果树作为重要的农业经济作物，易受多种病害侵袭，严重影响作物产量。传统的病害检测方法通常依赖人工检查，耗时且易出错。为此，本研究提出了一种新颖的两阶段边缘AI框架，用于苹果叶片病害分类，该框架将RGB图像与分布式边缘计算相结合。第一阶段执行二分类以区分健康叶片与患病叶片，F1分数达到95.50%。该优化通过在边缘设备上采用轻量级模型实现，从而提升了计算效率。第二阶段采用基于共识的多类分类方法，在识别具体病害方面F1分数达到96.44%。该双阶段系统在边缘AI架构的支持下，实现了实时病害检测，同时最大限度地降低了资源消耗，并增强了在大规模农业环境中的可扩展性。完整系统在专用数据集上达到了95.50%的准确率和F1分数。结果表明，该系统在受控实验室条件下具有有效性，为精准、实时的农业监测提供了一种有前景的解决方案。本研究凸显了分布式边缘AI系统在精准农业中实现可扩展且高效病害检测的潜力。","Facta Universitatis Series Automatic Control and Robotics","2026-10-18T00:00:00Z",76,{"impact":81,"substance":351,"depth":18,"authority":45,"freshness":200,"relevant":21,"comment":352},21,"该研究提出两级边缘AI框架，在苹果叶片病害识别上取得95%以上F1值，方法新颖、数据可靠，对精准农业实时监测有参考价值，但尚处实验室验证阶段，产业影响有限。",[354],{"name":347,"url":344},[26,87,356,357,358],"边缘计算","作物病害识别","苹果病害",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":362},{"candidates":363,"agricultural_evidence":46},[364,370,375,381],{"code":32,"score":81,"supported":46,"title":365,"abstract":367,"tags":369},[366],"apple",[34,368,366],"苹果",[358],{"code":51,"score":82,"supported":46,"title":371,"abstract":372,"tags":374},[],[141,373],"precision agriculture",[26],{"code":376,"score":82,"supported":46,"title":377,"abstract":378,"tags":380},"I5",[],[356,379],"edge computing",[356],{"code":115,"score":382,"supported":46,"title":383,"abstract":384,"tags":386},5,[],[385],"artificial intelligence",[357,87],[388,389],"苹果叶片病害 边缘AI","分布式边缘计算 病害分类","苹果叶片病害边缘AI-5401","10.22190\u002Ffuacr260506002g",{"doi":391,"openalex_id":393,"authors":394,"venue":347,"cited_by_count":65,"oa_url":344,"card":407,"direction":266,"ingested_from":188},"W7222964752",[395,398,401,404],{"name":396,"orcid":397},"Milan Grujev","https:\u002F\u002Forcid.org\u002F0009-0002-3211-6844",{"name":399,"orcid":400},"Dragan H. Stojanovic","https:\u002F\u002Forcid.org\u002F0000-0003-4893-3814",{"name":402,"orcid":403},"Miloš Ilić","https:\u002F\u002Forcid.org\u002F0000-0002-9696-2874",{"name":405,"orcid":406},"Aleksandar Milosavljević","https:\u002F\u002Forcid.org\u002F0000-0003-3057-7727",{"tldr":408,"method":409,"finding":410,"direction":411,"opportunity":412},"提出两级边缘AI框架，用RGB图像实时分类苹果叶片病害。","分布式边缘计算+轻量模型，先二分类再共识多分类。","两级系统准确率与F1均达95.50%，多分类F1为96.44%。","智慧农业 \u002F 农业物联网","仅在受控实验室验证，可延伸至田间复杂光照与多作物泛化及边缘能耗优化。","2026-10-20T23:30:33.830748Z",{"id":415,"title":416,"url":417,"summary":418,"summary_zh":8,"content":8,"source_name":419,"source_url":8,"published_at":420,"category":131,"cover_url":8,"hotness":13,"is_selected":14,"score":421,"score_detail":422,"sources":424,"tags":426,"primary_topic":431,"secondary_topics":432,"classification":433,"search_phrases":444,"slug":447,"view_count":65,"doi":8,"paper":448,"created_at":456},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。","智慧农业(中英文)(2026,8(4))","2026-10-13T00:00:00Z",72,{"impact":17,"substance":351,"depth":18,"authority":19,"freshness":382,"relevant":21,"comment":423},"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。",[425],{"name":419,"url":417},[26,427,428,429,430],"云南","柑橘","智慧灌区","蒸腾模拟",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":434},{"candidates":435,"agricultural_evidence":46},[436,440],{"code":32,"score":45,"supported":46,"title":437,"abstract":438,"tags":439},[428],[428],[428],{"code":51,"score":52,"supported":46,"title":441,"abstract":442,"tags":443},[],[26],[26],[445,446],"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",{"doi":8,"openalex_id":8,"authors":449,"venue":8,"cited_by_count":65,"oa_url":8,"card":450,"direction":411,"ingested_from":455},[],{"tldr":451,"method":452,"finding":453,"direction":411,"opportunity":454},"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。","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"]