[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3660":3,"related-3660":53},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":22,"tags":24,"search_phrases":30,"slug":33,"view_count":34,"doi":35,"paper":36,"created_at":52},3660,"Designing a framework for Sustainable Smart Agriculture with Mobile Robots and CNN","https:\u002F\u002Fdoi.org\u002F10.33423\u002F25102b43","Advances in artificial intelligence, robotics, and computer vision enable intelligent gardening systems that reduce manual monitoring. This study presents a smart gardening framework integrating a mobile robot with convolutional neural network (CNN) flower recognition. The robot captures garden images, while the CNN classifies flower species and provides plant-specific information for future care decisions. Using 50 training images and 50 independent testing images, the model achieved 92.0% training accuracy and 95.0% testing accuracy. The framework connects robotic image acquisition with automated visual recognition and provides a foundation for future irrigation, fertilization, and autonomous garden-management functions.","人工智能、机器人技术和计算机视觉的进步使得智能园艺系统能够减少人工监测。本研究提出了一种智能园艺框架，将移动机器人与卷积神经网络（CNN）花卉识别相结合。机器人采集花园图像，CNN对花卉种类进行分类，并提供植物特定信息，以供未来养护决策使用。使用50张训练图像和50张独立测试图像，该模型达到了92.0%的训练准确率和95.0%的测试准确率。该框架将机器人图像采集与自动视觉识别相连接，并为未来的灌溉、施肥和自主花园管理功能奠定了基础。",null,"Journal of Strategic Innovation and Sustainability","2026-09-28T00:00:00Z","论文",10,false,56,{"impact":17,"substance":18,"depth":19,"authority":19,"freshness":13,"relevant":20,"comment":21},8,14,12,1,"论文提出移动机器人与CNN花卉识别结合的智能园艺框架，测试准确率95%，但样本仅50张、规模偏小，属细分领域方法探索，公共价值有限。",[23],{"name":10,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","计算机视觉","农业机器人","花卉识别",[31,32],"移动机器人 CNN 花卉识别","智能园艺 自动灌溉","移动机器人CNN花卉识别-3660",0,"10.33423\u002F25102b43",{"doi":35,"openalex_id":37,"authors":38,"venue":10,"cited_by_count":34,"oa_url":9,"card":45,"direction":49,"ingested_from":51},"W7214523094",[39,41,43],{"name":40,"orcid":9},"Ting Zhang",{"name":42,"orcid":9},"Oanh Phan",{"name":44,"orcid":9},"Jiang Lu",{"tldr":46,"method":47,"finding":48,"direction":49,"opportunity":50},"提出移动机器人结合CNN识别花卉的智能园艺框架，测试准确率达95%。","移动机器人采集图像，CNN用50张训练和50张测试图像分类花卉。","CNN花卉识别测试准确率95%，为自动灌溉施肥等管理奠定基础。","智慧农业 \u002F 农业物联网","可扩展至多作物识别与实时决策，并融合物联网实现闭环精准管理。","openalex","2026-09-28T23:30:08.100458Z",{"total":54,"page":20,"page_size":54,"items":55},6,[56,109,149,208,230,273],{"id":57,"title":58,"url":59,"summary":60,"summary_zh":61,"content":9,"source_name":62,"source_url":59,"published_at":63,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":64,"score_detail":65,"sources":69,"tags":71,"search_phrases":74,"slug":77,"view_count":34,"doi":78,"paper":79,"created_at":108},3513,"Accelerated development and deployment of computer vision models for invasive aquatic pests","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-72212-8","Abstract Aquatic non-indigenous species (NIS) incur significant cultural, environmental and economic costs worldwide, and human surveillance is expensive and impractical at large scales. Computer vision models (CVMs) deployed on remote and autonomous vehicles can reduce the burden on trained human observers, but aquatic environments present unique challenges and lack frameworks for biosecurity-focused development and deployment. We present a practical framework for developing and deploying new species-specific CVMs for real-time use across surface vessel and remote vehicle platforms. We demonstrate the utility of our framework through its application to several taxonomically and ecologically diverse NIS in New Zealand: Mediterranean fanworm Sabella spallanzanii , South African oxygen weed Lagarosiphon major, and exotic Caulerpa ( Caulerpa brachypus and C. parvifolia ). For S. spallanzanii , we demonstrate efficient training from small datasets and deployment for real-time detection. Using exotic Caulerpa , we show how CVMs can be rapidly improved during an early incursion response. For L. major , standardised field validation methods enable the comparison of CVM and human detection rates across diverse locations and operating conditions. Collectively, these case studies demonstrate that our framework enables accurate detection and the robust assessment of model effectiveness under realistic field conditions and can be effectively applied to imagery from multiple platforms.","摘要 水生外来非本土物种（NIS）在全球范围内造成显著的文化、环境和经济损失，而人工监测成本高昂且难以大规模实施。部署在远程和自主载具上的计算机视觉模型（CVM）可减轻对训练有素的人类观察者的依赖，但水生环境面临独特挑战，且缺乏以生物安全为重点的开发和部署框架。我们提出了一个实用框架，用于开发和部署新的物种特异性CVM，以在水面船只和远程载具平台上实时使用。我们通过将该框架应用于新西兰多个在分类学和生态学上具有多样性的NIS来展示其效用：地中海缨鳃虫 Sabella spallanzanii、南非氧草 Lagarosiphon major 以及外来Caulerpa（Caulerpa brachypus 和 C. parvifolia）。对于 S. spallanzanii，我们展示了从小型数据集进行高效训练并部署用于实时检测。利用外来Caulerpa，我们展示了CVM如何在入侵早期响应期间快速改进。对于 L. major，标准化野外验证方法使得能够在不同地点和操作条件下比较CVM与人类检测率。总体而言，这些案例研究表明，我们的框架能够在现实野外条件下实现准确检测和对模型有效性的稳健评估，并可有效应用于来自多个平台的图像。","Scientific Reports","2026-09-23T00:00:00Z",80,{"impact":66,"substance":67,"depth":66,"authority":18,"freshness":17,"relevant":20,"comment":68},18,22,"提出面向水生入侵物种的计算机视觉模型开发与部署框架，含多物种实地验证，方法新颖且具生物安全应用价值，但属新西兰案例，对国内农业信息化影响有限。",[70],{"name":62,"url":59},[25,26,27,72,73],"入侵物种监测","水生生物安全",[75,76],"新西兰 入侵水生物种 计算机视觉","Caulerpa 实时检测 模型","新西兰入侵水生物种计算机视觉-3513","10.1038\u002Fs41598-026-72212-8",{"doi":78,"openalex_id":80,"authors":81,"venue":62,"cited_by_count":34,"oa_url":100,"card":101,"direction":107,"ingested_from":51},"W7214089404",[82,85,87,89,91,94,97],{"name":83,"orcid":84},"Rose A. Pearson","https:\u002F\u002Forcid.org\u002F0000-0002-4700-2113",{"name":86,"orcid":9},"Gareth Preston",{"name":88,"orcid":9},"Jeremy Bulleid",{"name":90,"orcid":9},"Svenja David",{"name":92,"orcid":93},"Felix Vaux","https:\u002F\u002Forcid.org\u002F0000-0002-2882-7996",{"name":95,"orcid":96},"Daniel Clements","https:\u002F\u002Forcid.org\u002F0000-0001-9319-5588",{"name":98,"orcid":99},"Leigh W. Tait","https:\u002F\u002Forcid.org\u002F0000-0001-9153-139X","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-72212-8_reference.pdf",{"tldr":102,"method":103,"finding":104,"direction":105,"opportunity":106},"提出一套开发与部署水生入侵物种计算机视觉模型的实用框架，并在新西兰三种入侵物种上验证。","基于水面船与遥控平台图像，小数据集训练、实时检测与标准化野外验证。","框架能实现实时准确检测，并在真实野外条件下稳健评估模型效果。","农业人工智能与决策模型","可迁移至农业入侵生物监测，探索小样本跨平台模型与野外标准化评估体系。","数字乡村与农业信息化","2026-09-25T23:30:42.003495Z",{"id":110,"title":111,"url":112,"summary":113,"summary_zh":114,"content":9,"source_name":115,"source_url":112,"published_at":116,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":117,"score_detail":118,"sources":122,"tags":124,"search_phrases":127,"slug":130,"view_count":34,"doi":9,"paper":131,"created_at":148},3489,"RootQuantV2: Adapting a Vision Foundation Model for Root-Trait Regression from Minirhizotron Imagery","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.25567","A lack of high-throughput phenotyping solutions for root traits in field-grown crops has severely constrained understanding and improvement of below-ground traits and processes. Minirhizotrons are the standard non-destructive root-phenotyping method in field environments. Computer vision solutions are needed to allow automated trait estimation at scale, but training data is scarce and human annotations are often inaccessible because they reside in proprietary software that only exports per-image scalar totals of root length and surface area. Nevertheless, large numeric archives of these root traits already exist. RootQuant showed that the traits can be predicted directly from the whole image by regression, thus removing manually traced masks from the pipeline; RootQuantV2 takes that idea further by replacing RootQuant's CNN backbone with a self-supervised ViT. We adapt a frozen DINOv3 ViT-L\u002F16 with a hybrid parameter-efficient scheme. Training only 11.9M parameters (3.78% of the model), RootQuantV2 achieves length and area $R^2$ of 0.950 and 0.930, respectively, while lowering length\u002Farea RMSE by 24.3%\u002F20.7% over RootQuant. RootQuantV2 thus repurposes legacy numeric archives for high-throughput, automated root trait estimation.","田间作物根系性状缺乏高通量表型分析解决方案，严重制约了对地下性状与过程的理解和改良。微根管（minirhizotron）是田间环境下标准的非破坏性根系表型分析方法。实现规模化自动化性状估计需要计算机视觉解决方案，但训练数据稀缺，且人工标注往往难以获取，因为这些标注存于专有软件中，而该软件仅能导出每幅图像根系长度和表面积的标量汇总值。尽管如此，这些根系性状的大型数值档案已经存在。RootQuant表明，可通过回归直接从整幅图像预测这些性状，从而将人工勾画的分割掩膜从流程中移除；RootQuantV2进一步推进了这一思路，将RootQuant的CNN骨干网络替换为自监督ViT。我们采用混合参数高效方案对冻结的DINOv3 ViT-L\u002F16进行适配。仅训练11.9M参数（占模型的3.78%），RootQuantV2在长度和面积上的$R^2$分别达到0.950和0.930，同时相较RootQuant将长度\u002F面积RMSE降低了24.3%\u002F20.7%。因此，RootQuantV2将遗留数值档案重新用于高通量、自动化的根系性状估计。","arXiv (Cornell University)","2026-09-22T00:00:00Z",77,{"impact":119,"substance":67,"depth":66,"authority":120,"freshness":17,"relevant":20,"comment":121},16,13,"将自监督视觉基础模型用于微根管图像根系性状回归，仅训练3.78%参数即显著提升精度，为田间根系高通量表型提供可复用方案。",[123],{"name":115,"url":112},[25,26,27,125,126],"根系表型","高通量育种",[128,129],"RootQuantV2 根系表型","DINOv3 微根管 根系","RootQuantV2根系表型-3489",{"doi":9,"openalex_id":132,"authors":133,"venue":115,"cited_by_count":34,"oa_url":141,"card":142,"direction":146,"ingested_from":51},"W7214246102",[134,136,138],{"name":135,"orcid":9},"Kinjalk Parth",{"name":137,"orcid":9},"Sebastian Varela",{"name":139,"orcid":140},"Andrew D. B. Leakey","https:\u002F\u002Forcid.org\u002F0000-0001-6251-024X","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2609.25567",{"tldr":143,"method":144,"finding":145,"direction":146,"opportunity":147},"用冻结DINOv3 ViT微调回归，从微根管图像直接预测根系长度和面积。","冻结DINOv3 ViT-L\u002F16，混合参数高效微调，仅训11.9M参数。","长度和面积R²达0.950和0.930，RMSE比RootQuant降低24.3%\u002F20.7%。","农业遥感与作物表型","可探索将此类基础模型回归范式迁移到其他稀缺标注的田间表型性状，并融合多模态数据。","2026-09-25T23:30:24.097147Z",{"id":150,"title":151,"url":152,"summary":153,"summary_zh":154,"content":9,"source_name":155,"source_url":152,"published_at":63,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":64,"score_detail":156,"sources":158,"tags":160,"search_phrases":163,"slug":166,"view_count":34,"doi":167,"paper":168,"created_at":207},3366,"Brazilian Insect Survey: A Platform for Pest Management","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs13744-026-01426-2","Abstract Modern agriculture faces significant challenges in integrated pest management, where collecting, connecting, and processing monitoring data in real time are essential. This study presents the Brazilian Insect Survey (BIS), a web-based platform designed to centralize and streamline phytosanitary data management, enabling the integration of computer vision, field experimentation, and population modeling within a unified digital ecosystem. The platform is organized into functional modules that support experimental data management (TrapSystem and AgroExperiment), automated insect detection and counting from digital images (InsectCV and AphidCV), and simulation of aphid population dynamics (ABISM). These components operate in synergy, supported by distributed processing infrastructure, to ensure scalable data handling and efficient analytical workflows. Multi-year field applications demonstrate that integrating field data acquisition and automated image analysis with mechanistic population modeling enables timely, model-driven interventions that reduce aphid infestation levels and protect crop yield potential under variable environmental conditions, providing empirical evidence for queryPlease check if the captured keywords are correct.the effectiveness of combining AI-based insect detection with population dynamics modeling in operational integrated pest management. The case studies presented here demonstrate that the BIS platform successfully integrates computer vision, field experimentation, and population modeling within a modular digital ecosystem, highlighting its potential to enhance decision-making and advance data-driven integrated pest management.","摘要 现代农业在有害生物综合治理方面面临重大挑战，其中实时采集、连接和处理监测数据至关重要。本研究提出了巴西昆虫调查平台（Brazilian Insect Survey，BIS），这是一个基于网络的平台，旨在集中化和简化植物检疫数据管理，使计算机视觉、田间试验和种群建模能够整合在一个统一的数字生态系统中。该平台按功能模块组织，支持实验数据管理（TrapSystem和AgroExperiment）、基于数字图像的昆虫自动检测与计数（InsectCV和AphidCV），以及蚜虫种群动态模拟（ABISM）。这些组件在分布式处理基础设施的支持下协同运行，以确保可扩展的数据处理和高效的分析工作流。多年田间应用表明，将田间数据采集和自动图像分析与机制性种群建模相结合，能够实现及时的、模型驱动的干预，从而在多变的环境条件下降低蚜虫侵染水平并保护作物产量潜力，为将基于人工智能的昆虫检测与种群动态建模相结合在实际有害生物综合治理中的有效性提供了经验证据。本文所呈现的案例研究表明，BIS平台成功地将计算机视觉、田间试验和种群建模整合在一个模块化数字生态系统中，凸显了其在增强决策能力和推进数据驱动有害生物综合治理方面的潜力。","Neotropical Entomology",{"impact":66,"substance":67,"depth":66,"authority":18,"freshness":17,"relevant":20,"comment":157},"巴西昆虫调查平台将计算机视觉与种群动态模型整合进IPM实践，方法新颖、多年田间验证，对智慧农业病虫害监测有参考价值。",[159],{"name":155,"url":152},[25,26,27,161,162],"病虫害监测","种群模型",[164,165],"Brazil 蚜虫 监测平台","农业人工智能 病虫害监测 计算机视觉 智慧农业","Brazil蚜虫监测平台-3366","10.1007\u002Fs13744-026-01426-2",{"doi":167,"openalex_id":169,"authors":170,"venue":155,"cited_by_count":34,"oa_url":152,"card":202,"direction":107,"ingested_from":51},"W7214074071",[171,174,176,179,182,184,187,190,193,196,199],{"name":172,"orcid":173},"Telmo De Cesaro Júnior","https:\u002F\u002Forcid.org\u002F0000-0002-2886-229X",{"name":175,"orcid":9},"Bárbara Stella Wehrmann",{"name":177,"orcid":178},"Alexandre Tagliari Lazzaretti","https:\u002F\u002Forcid.org\u002F0009-0004-8345-8216",{"name":180,"orcid":181},"Roberto Wiest","https:\u002F\u002Forcid.org\u002F0000-0001-7982-6603",{"name":183,"orcid":9},"Jorge Luis Boeira Bavaresco",{"name":185,"orcid":186},"Brenda Slongo Taca","https:\u002F\u002Forcid.org\u002F0009-0000-6408-5523",{"name":188,"orcid":189},"Nicolas Welfer Kirinus","https:\u002F\u002Forcid.org\u002F0009-0003-5260-3161",{"name":191,"orcid":192},"Crislaine Sartori Suzana Milan","https:\u002F\u002Forcid.org\u002F0000-0003-0562-7286",{"name":194,"orcid":195},"Jayme Garcia Arnal Barbedo","https:\u002F\u002Forcid.org\u002F0000-0002-1156-8270",{"name":197,"orcid":198},"Douglas Lau","https:\u002F\u002Forcid.org\u002F0000-0001-8648-0102",{"name":200,"orcid":201},"Rafael Rieder","https:\u002F\u002Forcid.org\u002F0000-0002-7435-9054",{"tldr":203,"method":204,"finding":205,"direction":49,"opportunity":206},"巴西昆虫调查平台整合计算机视觉、田间试验与种群建模，实现害虫实时监测与治理。","构建BIS网络平台，集成InsectCV\u002FAphidCV图像识别、TrapSys","AI昆虫检测与种群动态建模结合可降低蚜虫侵害并保护作物产量。","可探索多害虫跨区域监测数据标准化与模型迁移，构建开放植保决策生态。","2026-09-24T23:30:25.977846Z",{"id":209,"title":210,"url":211,"summary":212,"summary_zh":9,"content":213,"source_name":214,"source_url":9,"published_at":63,"category":215,"cover_url":9,"hotness":13,"is_selected":14,"score":216,"score_detail":217,"sources":220,"tags":222,"search_phrases":225,"slug":228,"view_count":34,"doi":9,"paper":9,"created_at":229},3292,"AI巡田机器狗、采摘机器人、虫情监测仪！智慧农业装备亮相2026年中国农民丰收节山东主场活动","https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQwOTg2NA.html","在2026年中国农民丰收节山东主场活动现场，一批智慧农业新装备集中亮相：巡田机器狗替代人工开展田间作业，搭载多光谱、可见光相机与环境监测传感器，依托自研AI算法可精准完成病虫害识别、作物长势研判、土壤墒情监测；便携式一体虫情监测仪可灵活拆装，冬季无虫害时可拆卸收纳；地形移动作业机器人搭载机械臂，更换不同抓手就能实现果蔬采摘；管式墒情仪采用太阳能供电，直接插地即可投入使用。","![Image 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6](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Fshang.png)\n\n*   [闪电号>](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Fmp.html?2&0)\n*   [专栏>](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Fmp.html?2&0&%E4%B8%93%E6%A0%8F)\n*   [乡村季风农业家](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Fmpdetail\u002F100200.html?catid=100200)\n\n AI 巡田机器狗、采摘机器人、虫情监测仪！智慧农业装备亮相2026年中国农民丰收节山东主场活动 \n\n[乡村季风农业家](javascript:;)\n\n[2026-09-22 15:43:05](javascript:;)\n\n[Video 3](https:\u002F\u002Fstream7-transcode.iqilu.com\u002F10361\u002Fsucaiku\u002F202609\u002F22\u002Ff4534bc69de84c47b8bceeb954d0626c_902.mp4)\n在 2026年中国农民丰收节山东主场活动现场，一批智慧农业新装备集中亮相，为粮食稳产增产注入科技动能。\n\n巡田机器狗替代人工开展田间作业，搭载多光谱、可见光相机与环境监测传感器，依托自研 AI 算法，可精准完成病虫害识别、作物长势研判、土壤墒情监测；便携式一体虫情监测仪可灵活拆装，冬季无虫害时可拆卸收纳，有效降低设备运维成本；地形移动作业机器人搭载机械臂，更换不同抓手就能实现果蔬采摘；管式墒情仪采用太阳能供电，直接插地即可投入使用，工业级外壳坚固耐用，保障土壤数据精准采集。\n\n从前端田间感知、终端数据分析到后端作业执行，整套设备闭环联动，科学管控田间生产，助力减少农药化肥使用，实现农田提质增产。以科技赋能农耕，用装备守护耕耘，让智慧农业护航齐鲁大地岁岁丰收！\n\n《乡村季风》记者 郑莹莹 马成波\n\n来源： 乡村季风 \n\n编辑： 郑莹莹 \n\n责编： 梁丽 \n\n审校： 张洁 \n\n主编： 方翔 \n\n 相关推荐 \n\n[![Image 7](https:\u002F\u002Fimg12.iqilu.com\u002F1\u002Fextraction\u002F202609\u002F23\u002F86b0f4ade3b24b269f107f8e38e0e8ee-1.jpg)](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMjcyNw.html)\n\n[省水省肥还增产！丰收节解锁智慧种植新利器#智能水肥机#智慧农业#中国农民丰收节#农业黑科技#节水节肥#现代化种植](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMjcyNw.html)\n\n 分享到：\n\n[![Image 8](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_weixin.png)](http:\u002F\u002Fservice.weibo.com\u002Fshare\u002Fshare.php?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMjcyNw.html&title=%E7%9C%81%E6%B0%B4%E7%9C%81%E8%82%A5%E8%BF%98%E5%A2%9E%E4%BA%A7%EF%BC%81%E4%B8%B0%E6%94%B6%E8%8A%82%E8%A7%A3%E9%94%81%E6%99%BA%E6%85%A7%E7%A7%8D%E6%A4%8D%E6%96%B0%E5%88%A9%E5%99%A8#%E6%99%BA%E8%83%BD%E6%B0%B4%E8%82%A5%E6%9C%BA#%E6%99%BA%E6%85%A7%E5%86%9C%E4%B8%9A#%E4%B8%AD%E5%9B%BD%E5%86%9C%E6%B0%91%E4%B8%B0%E6%94%B6%E8%8A%82#%E5%86%9C%E4%B8%9A%E9%BB%91%E7%A7%91%E6%8A%80#%E8%8A%82%E6%B0%B4%E8%8A%82%E8%82%A5#%E7%8E%B0%E4%BB%A3%E5%8C%96%E7%A7%8D%E6%A4%8D&pic=&summary=%E6%9D%A5%E8%87%AA%E9%97%AA%E7%94%B5%E6%96%B0%E9%97%BB%E5%AE%A2%E6%88%B7%E7%AB%AF&appkey=#_loginLayer_1596177107797)[![Image 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15](https:\u002F\u002Fimg12.iqilu.com\u002F10361\u002Fsucaiku\u002Fcompress\u002F202609\u002F23\u002Faf5e6c9a65864a19a0fc6905e3584006.png)](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMjAzMg.html)\n\n[东阿阿胶牵头培育的“鲁西黑驴”获批国家畜禽新品种](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMjAzMg.html)\n\n 分享到：\n\n[![Image 16](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_weixin.png)](http:\u002F\u002Fservice.weibo.com\u002Fshare\u002Fshare.php?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMjAzMg.html&title=%E4%B8%9C%E9%98%BF%E9%98%BF%E8%83%B6%E7%89%B5%E5%A4%B4%E5%9F%B9%E8%82%B2%E7%9A%84%E2%80%9C%E9%B2%81%E8%A5%BF%E9%BB%91%E9%A9%B4%E2%80%9D%E8%8E%B7%E6%89%B9%E5%9B%BD%E5%AE%B6%E7%95%9C%E7%A6%BD%E6%96%B0%E5%93%81%E7%A7%8D&pic=&summary=%E6%9D%A5%E8%87%AA%E9%97%AA%E7%94%B5%E6%96%B0%E9%97%BB%E5%AE%A2%E6%88%B7%E7%AB%AF&appkey=#_loginLayer_1596177107797)[![Image 17](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_pengyou.png)](https:\u002F\u002Fsns.qzone.qq.com\u002Fcgi-bin\u002Fqzshare\u002Fcgi_qzshare_onekey?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMjAzMg.html&title=%E4%B8%9C%E9%98%BF%E9%98%BF%E8%83%B6%E7%89%B5%E5%A4%B4%E5%9F%B9%E8%82%B2%E7%9A%84%E2%80%9C%E9%B2%81%E8%A5%BF%E9%BB%91%E9%A9%B4%E2%80%9D%E8%8E%B7%E6%89%B9%E5%9B%BD%E5%AE%B6%E7%95%9C%E7%A6%BD%E6%96%B0%E5%93%81%E7%A7%8D&summary=%E6%9D%A5%E8%87%AA%E9%97%AA%E7%94%B5%E6%96%B0%E9%97%BB%E5%AE%A2%E6%88%B7%E7%AB%AF&desc=&site=)[![Image 18](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_qq.png)](https:\u002F\u002Fconnect.qq.com\u002Fwidget\u002Fshareqq\u002Findex.html?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMjAzMg.html&title=%E4%B8%9C%E9%98%BF%E9%98%BF%E8%83%B6%E7%89%B5%E5%A4%B4%E5%9F%B9%E8%82%B2%E7%9A%84%E2%80%9C%E9%B2%81%E8%A5%BF%E9%BB%91%E9%A9%B4%E2%80%9D%E8%8E%B7%E6%89%B9%E5%9B%BD%E5%AE%B6%E7%95%9C%E7%A6%BD%E6%96%B0%E5%93%81%E7%A7%8D&source=&summary=%E6%9D%A5%E8%87%AA%E9%97%AA%E7%94%B5%E6%96%B0%E9%97%BB%E5%AE%A2%E6%88%B7%E7%AB%AF&desc=&pics=https:\u002F\u002Fimg12.iqilu.com\u002F10361\u002Fsucaiku\u002Fcompress\u002F202609\u002F23\u002Faf5e6c9a65864a19a0fc6905e3584006.png)\n\n[![Image 19](https:\u002F\u002Fimg12.iqilu.com\u002F1\u002Fextraction\u002F202609\u002F23\u002F5046dad3db3047c58c60ff919b872503-1.jpg)](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTYwMA.html)\n\n[懒人的福音！加热5分钟就能喝的乌鸡人参汤](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTYwMA.html)\n\n 分享到：\n\n[![Image 20](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_weixin.png)](http:\u002F\u002Fservice.weibo.com\u002Fshare\u002Fshare.php?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTYwMA.html&title=%E6%87%92%E4%BA%BA%E7%9A%84%E7%A6%8F%E9%9F%B3%EF%BC%81%E5%8A%A0%E7%83%AD5%E5%88%86%E9%92%9F%E5%B0%B1%E8%83%BD%E5%96%9D%E7%9A%84%E4%B9%8C%E9%B8%A1%E4%BA%BA%E5%8F%82%E6%B1%A4&pic=&summary=%E6%9D%A5%E8%87%AA%E9%97%AA%E7%94%B5%E6%96%B0%E9%97%BB%E5%AE%A2%E6%88%B7%E7%AB%AF&appkey=#_loginLayer_1596177107797)[![Image 21](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_pengyou.png)](https:\u002F\u002Fsns.qzone.qq.com\u002Fcgi-bin\u002Fqzshare\u002Fcgi_qzshare_onekey?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTYwMA.html&title=%E6%87%92%E4%BA%BA%E7%9A%84%E7%A6%8F%E9%9F%B3%EF%BC%81%E5%8A%A0%E7%83%AD5%E5%88%86%E9%92%9F%E5%B0%B1%E8%83%BD%E5%96%9D%E7%9A%84%E4%B9%8C%E9%B8%A1%E4%BA%BA%E5%8F%82%E6%B1%A4&summary=%E6%9D%A5%E8%87%AA%E9%97%AA%E7%94%B5%E6%96%B0%E9%97%BB%E5%AE%A2%E6%88%B7%E7%AB%AF&desc=&site=)[![Image 22](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_qq.png)](https:\u002F\u002Fconnect.qq.com\u002Fwidget\u002Fshareqq\u002Findex.html?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTYwMA.html&title=%E6%87%92%E4%BA%BA%E7%9A%84%E7%A6%8F%E9%9F%B3%EF%BC%81%E5%8A%A0%E7%83%AD5%E5%88%86%E9%92%9F%E5%B0%B1%E8%83%BD%E5%96%9D%E7%9A%84%E4%B9%8C%E9%B8%A1%E4%BA%BA%E5%8F%82%E6%B1%A4&source=&summary=%E6%9D%A5%E8%87%AA%E9%97%AA%E7%94%B5%E6%96%B0%E9%97%BB%E5%AE%A2%E6%88%B7%E7%AB%AF&desc=&pics=https:\u002F\u002Fimg12.iqilu.com\u002F1\u002Fextraction\u002F202609\u002F23\u002F5046dad3db3047c58c60ff919b872503-1.jpg)\n\n[![Image 23](https:\u002F\u002Fimg12.iqilu.com\u002F1\u002Fextraction\u002F202609\u002F23\u002F4f3081202f01498bb2032b3cae283fa8-1.jpg)](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTM1NA.html)\n\n[“裙膜升地温”技术让葡萄避免冻害抽干 安全越冬](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTM1NA.html)\n\n 分享到：\n\n[![Image 24](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_weixin.png)](http:\u002F\u002Fservice.weibo.com\u002Fshare\u002Fshare.php?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTM1NA.html&title=%E2%80%9C%E8%A3%99%E8%86%9C%E5%8D%87%E5%9C%B0%E6%B8%A9%E2%80%9D%E6%8A%80%E6%9C%AF%E8%AE%A9%E8%91%A1%E8%90%84%E9%81%BF%E5%85%8D%E5%86%BB%E5%AE%B3%E6%8A%BD%E5%B9%B2)[![Image 25](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_pengyou.png)](https:\u002F\u002Fsns.qzone.qq.com\u002Fcgi-bin\u002Fqzshare\u002Fcgi_qzshare_onekey?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTM1NA.html&title=%E2%80%9C%E8%A3%99%E8%86%9C%E5%8D%87%E5%9C%B0%E6%B8%A9%E2%80%9D%E6%8A%80%E6%9C%AF%E8%AE%A9%E8%91%A1%E8%90%84%E9%81%BF%E5%85%8D%E5%86%BB%E5%AE%B3%E6%8A%BD%E5%B9%B2)[![Image 26](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_qq.png)](https:\u002F\u002Fconnect.qq.com\u002Fwidget\u002Fshareqq\u002Findex.html?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTM1NA.html&title=%E2%80%9C%E8%A3%99%E8%86%9C%E5%8D%87%E5%9C%B0%E6%B8%A9%E2%80%9D%E6%8A%80%E6%9C%AF%E8%AE%A9%E8%91%A1%E8%90%84%E9%81%BF%E5%85%8D%E5%86%BB%E5%AE%B3%E6%8A%BD%E5%B9%B2)\n\n[![Image 27](https:\u002F\u002Fimg12.iqilu.com\u002F1\u002Fextraction\u002F202609\u002F23\u002F8fc17233cf67410c8a323dc82a760247-1.jpg)](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTIyOA.html)\n\n[山东临沭 花生种植高手集结 共同见证“三只虎”大肥力#农科频道#“三只虎杯”中国花生王大赛](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTIyOA.html)\n\n 分享到：\n\n[![Image 28](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_weixin.png)](http:\u002F\u002Fservice.weibo.com\u002Fshare\u002Fshare.php?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTIyOA.html&title=%E5%B1%B1%E4%B8%9C%E4%B8%B4%E6%B2%AD)[![Image 29](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_pengyou.png)](https:\u002F\u002Fsns.qzone.qq.com\u002Fcgi-bin\u002Fqzshare\u002Fcgi_qzshare_onekey?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTIyOA.html&title=%E5%B1%B1%E4%B8%9C%E4%B8%B4%E6%B2%AD)[![Image 30](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_qq.png)](https:\u002F\u002Fconnect.qq.com\u002Fwidget\u002Fshareqq\u002Findex.html?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTIyOA.html&title=%E5%B1%B1%E4%B8%9C%E4%B8%B4%E6%B2%AD)\n\n[![Image 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42](https:\u002F\u002Fimg11.iqilu.com\u002F21\u002F2025\u002F12\u002F22\u002F98dd3ce409421c836719db34b19a3449.png)](https:\u002F\u002Fapp-h5.iqilu.com\u002Ftopic\u002FMTgyNTE6MWUyZWY.htm?orgid=21#\u002F)\n\n[](https:\u002F\u002Fapp-h5.iqilu.com\u002Ftopic\u002FMTgyNTE6MWUyZWY.htm?orgid=21#\u002F)[聆听金声力量！聚焦第三届“金声奖”获得者彭文馨](https:\u002F\u002Fapp-h5.iqilu.com\u002Ftopic\u002FMTgyNTE6MWUyZWY.htm?orgid=21#\u002F)\n\n[![Image 43](https:\u002F\u002Fimg11.iqilu.com\u002F21\u002F2025\u002F11\u002F25\u002F62","闪电新闻 2026年09月23日","报道",59,{"impact":119,"substance":18,"depth":19,"authority":218,"freshness":17,"relevant":20,"comment":219},9,"省级丰收节上的智慧农业装备集中展示，涵盖巡田机器狗、采摘机器人、虫情监测仪等，具备一定行业参考价值，但属活动报道，信息增量有限。",[221],{"name":214,"url":211},[25,26,223,28,224],"山东","田间监测",[226,227],"中国农民丰收节 山东 智慧农业装备","巡田机器狗 采摘机器人 虫情监测仪","中国农民丰收节山东智慧农业装备-3292","2026-09-24T00:03:57.106255Z",{"id":231,"title":232,"url":233,"summary":234,"summary_zh":235,"content":9,"source_name":236,"source_url":233,"published_at":116,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":237,"score_detail":238,"sources":242,"tags":244,"search_phrases":247,"slug":250,"view_count":34,"doi":251,"paper":252,"created_at":272},3257,"Blueberry flow and mass estimation from harvester conveyor videos via foundation-model-assisted labeling and vision-based sensing","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112432","Despite rising labor costs and growing labor shortages, adoption of machine harvesting in fresh market blueberry production has been limited by issues such as high rates of bruising and the significant loss of fruits on the ground. Machine settings strongly affect fruit quality and harvesting performance, yet they are manually configured based on operator experience, with no real-time performance feedback. Towards addressing these limitations, this paper presents a sensing proof-of-concept for analyzing berry flow through the harvester by estimating the pixel-wise proportion of blue vs green berries and volumetric flow rate from overhead RGB-D images of the harvester’s conveyor. An automated labeling pipeline combining a domain-specific YOLOv8 patch detector with SAM2-based mask generation was developed to generate a realistic large-scale berry dataset from unlabeled images captured during commercial over-the-row blueberry harvesting operations. Compared to a baseline SegFormer model trained on a smaller dataset of manually annotated images, our fine-tuned SegFormer – trained and validated on the large-scale automatically generated dataset – achieves superior performance, with green berry IoU improving from 0.377 to 0.817 and recall increasing from 0.414 to 0.911. A scene-level YOLOv8-l detection model trained and evaluated on the same dataset achieves mAP(50-95) of 0.966 and 0.898 for blue and green berries, respectively. Both models’ performance is further confirmed by an independent evaluation on a manually annotated subset of the validation images. We also compared two approaches for predicting harvest yields across five machine-setting trials: a depth-based method using the RGB-D sensor and a detection-based method using only RGB images, achieving R 2 = 0.989 and R 2 = 0.894 , respectively, against ground-truth load cell measurements from these trials. To support future research, we publicly release our datasets and models.","尽管劳动力成本上升且劳动力短缺日益加剧，但鲜食蓝莓生产中机器采收的采用仍受到果实瘀伤率高和落地果实损失严重等问题的限制。机器设置对果实品质和采收性能有很大影响，但目前仍依赖操作人员经验进行手动配置，且没有实时性能反馈。针对这些局限，本文提出了一种传感概念验证方法，通过估计采收机传送带上方RGB-D图像中蓝莓与绿果的逐像素比例以及体积流量，来分析通过采收机的果实流。我们开发了一种自动标注流程，将领域特定的YOLOv8小块检测器与基于SAM2的掩膜生成相结合，从商业跨行蓝莓采收作业期间采集的无标注图像中生成逼真的大规模蓝莓数据集。与在较小规模人工标注图像数据集上训练的基线SegFormer模型相比，我们经过微调的SegFormer——在大规模自动生成数据集上训练和验证——取得了更优性能，绿果IoU从0.377提升至0.817，召回率从0.414提升至0.911。在同一数据集上训练和评估的场景级YOLOv8-l检测模型，对蓝莓和绿果分别达到0.966和0.898的mAP(50-95)。两个模型的性能还通过在验证图像人工标注子集上的独立评估得到进一步确认。我们还比较了在五次机器设置试验中预测采收产量的两种方法：使用RGB-D传感器的基于深度的方法和使用仅RGB图像的基于检测的方法，相对于这些试验中来自称重传感器的真实测量值，分别达到R² = 0.989和R² = 0.894。为支持未来研究，我们公开了数据集和模型。","Computers and Electronics in Agriculture",83,{"impact":66,"substance":239,"depth":240,"authority":18,"freshness":218,"relevant":20,"comment":241},23,19,"该研究提出基于基础模型辅助标注与视觉传感的蓝莓流量与产量估计方法，方法新颖、数据规模大且公开数据集与模型，对智慧农业与农业机器人领域有较高参考价值。",[243],{"name":236,"url":233},[25,26,28,245,246],"机器视觉","蓝莓采收",[248,249],"蓝莓采收 机器视觉 产量估计","蓝莓收获机 传送带 视觉检测","蓝莓采收机器视觉产量估计-3257","10.1016\u002Fj.compag.2026.112432",{"doi":251,"openalex_id":253,"authors":254,"venue":236,"cited_by_count":34,"oa_url":233,"card":267,"direction":105,"ingested_from":51},"W7214013690",[255,257,259,261,264],{"name":256,"orcid":9},"A. M. Aahad",{"name":258,"orcid":9},"Pico Sankari",{"name":260,"orcid":9},"Wei Q. Yang",{"name":262,"orcid":263},"Siniša Todorović","https:\u002F\u002Forcid.org\u002F0000-0001-5793-5921",{"name":265,"orcid":266},"Joseph R. Davidson","https:\u002F\u002Forcid.org\u002F0000-0003-4388-2210",{"tldr":268,"method":269,"finding":270,"direction":105,"opportunity":271},"用收割机传送带RGB-D视频估计蓝莓流量与质量，并公开数据集和模型。","YOLOv8+SAM2自动标注，SegFormer分割，RGB-D深度与检测法估","自动标注使绿果IoU从0.377升至0.817，深度法估产R²达0.989。","可延伸至收割机参数实时闭环调控，用视觉反馈优化机器设置以减损提质。","2026-09-23T23:30:01.713624Z",{"id":274,"title":275,"url":276,"summary":277,"summary_zh":9,"content":9,"source_name":278,"source_url":9,"published_at":279,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":64,"score_detail":280,"sources":282,"tags":284,"search_phrases":287,"slug":290,"view_count":34,"doi":9,"paper":291,"created_at":299},3253,"Visuomotor Robotic Pruning in Planar Orchills Using Hybrid Reinforcement Learning（基于混合强化学习的平面果园视觉运动机器人修剪）","https:\u002F\u002Farxivtldr.org\u002Fabs\u002F2609.24906","arXiv发表研究：休眠期树木修剪是劳动密集型但对维持现代高产果园至关重要的工作。本文针对现代平面树形训练系统（V型棚架苹果、UFO樱桃）的修剪任务，提出端到端管线学习闭环视觉运动控制器。控制器完全使用仿真和合成生成数据进行训练，以零样本方式部署到真实果园。管线包括平面果园树网格的合成生成、基于物理的果园仿真器构建、通过运动规划自动收集成功修剪轨迹，以及结合离线演示与在线仿真推出相结合的新型混合强化学习算法。控制器使用腕部相机的光流输入，避免完整三维重建需求，连续引导切割器穿越杂乱分支环境到指定切割点并在真实果园38次物理试验中展示零样本仿真到真实迁移。在V-Trellis苹果上达到49.9%成功率，UFO樱桃上46.0%。","arXiv (Abhinav Jain, Cindy Grimm, Stefan Lee)","2026-09-20T00:00:00Z",{"impact":66,"substance":67,"depth":66,"authority":120,"freshness":218,"relevant":20,"comment":281},"仿真到真实零样本迁移的果园修剪机器人研究，方法新颖、数据扎实，对智慧果园机械化有参考价值。",[283],{"name":278,"url":276},[25,26,28,285,286],"强化学习","果园管理",[288,289],"V-Trellis 苹果 机器人修剪","UFO 樱桃 零样本迁移","V-Trellis苹果机器人修剪-3253",{"doi":9,"openalex_id":9,"authors":292,"venue":9,"cited_by_count":34,"oa_url":9,"card":293,"direction":105,"ingested_from":298},[],{"tldr":294,"method":295,"finding":296,"direction":105,"opportunity":297},"提出端到端视觉运动控制器，用仿真合成数据训练，零样本迁移到真实果园完成修剪。","合成数据生成、物理仿真器、运动规划收集轨迹、混合强化学习、腕部相机光流。","真实果园38次试验零样本迁移成功，苹果49.9%、樱桃46.0%成功率。","可探索多季节、多树种泛化及真实数据微调，提升复杂冠层下的鲁棒性。","agent","2026-09-23T00:04:33.854148Z"]