[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3132":3,"related-3132":96},{"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":23,"tags":25,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":60,"slug":63,"view_count":64,"doi":65,"paper":66,"created_at":95},3132,"Vision-guided real-time hoof spraying robot for lameness prevention in rotary milking parlors","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112445","Vision-guided real-time hoof spraying robot for lameness prevention in rotary milking parlors。Computers and Electronics in Agriculture","旋转式挤奶厅中用于预防跛行的视觉引导实时蹄部喷药机器人。《计算机与电子农业》",null,"Computers and Electronics in Agriculture","2026-09-21T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"核心期刊发表的视觉引导蹄部喷药机器人研究，方法新颖、面向奶牛跛行防控，属智慧养殖细分领域实质进展。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业机器人","奶牛养殖","机器视觉","蹄病防控",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"unclassified","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":58,"reason":59},[38,43,48,53],{"code":39,"score":21,"supported":14,"title":40,"abstract":41,"tags":42},"C2",[],[],[28],{"code":44,"score":21,"supported":14,"title":45,"abstract":46,"tags":47},"F6",[],[],[27],{"code":49,"score":21,"supported":14,"title":50,"abstract":51,"tags":52},"I1",[],[],[26],{"code":54,"score":21,"supported":14,"title":55,"abstract":56,"tags":57},"I2",[],[],[29],true,"缺少足够的主题证据",[61,62],"蹄部喷药机器人 旋转挤奶厅","机器视觉 奶牛蹄病","蹄部喷药机器人旋转挤奶厅-3132",3,"10.1016\u002Fj.compag.2026.112445",{"doi":65,"openalex_id":67,"authors":68,"venue":10,"cited_by_count":87,"oa_url":9,"card":88,"direction":92,"ingested_from":94},"W7213890211",[69,72,75,77,79,81,84],{"name":70,"orcid":71},"Sicheng Li","https:\u002F\u002Forcid.org\u002F0000-0003-1174-3670",{"name":73,"orcid":74},"Wenshuai Ye","https:\u002F\u002Forcid.org\u002F0009-0006-8008-1788",{"name":76,"orcid":9},"Cifu Xu",{"name":78,"orcid":9},"Arshed Ahmed",{"name":80,"orcid":9},"Xiaowen Liu",{"name":82,"orcid":83},"Kun Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-5166-2245",{"name":85,"orcid":86},"Gang Liu","https:\u002F\u002Forcid.org\u002F0000-0002-1004-1653",0,{"tldr":89,"method":90,"finding":91,"direction":92,"opportunity":93},"研发视觉引导的实时蹄部喷药机器人，用于转盘式挤奶厅预防奶牛跛行。","机器视觉识别蹄部，结合机器人实时喷药控制。","视觉引导机器人可在转盘挤奶厅实时精准喷药，预防跛行。","智慧农业 \u002F 农业物联网","可延伸至多目标识别、动态跟踪与药量自适应优化，提升复杂场景鲁棒性。","openalex","2026-09-22T23:30:02.231112Z",{"total":97,"page":21,"page_size":97,"items":98},6,[99,170,228,280,330,370],{"id":100,"title":101,"url":102,"summary":103,"summary_zh":104,"content":9,"source_name":10,"source_url":102,"published_at":105,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":106,"score_detail":107,"sources":111,"tags":113,"primary_topic":9,"secondary_topics":116,"classification":117,"search_phrases":142,"slug":145,"view_count":146,"doi":147,"paper":148,"created_at":169},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。","2026-09-22T00:00:00Z",83,{"impact":17,"substance":108,"depth":109,"authority":19,"freshness":20,"relevant":21,"comment":110},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。",[112],{"name":10,"url":102},[26,114,27,29,115],"农业人工智能","蓝莓采收",[],{"status":118,"locked":14,"version":34,"method":35,"evidence":119},"needs_review",{"candidates":120,"agricultural_evidence":14,"reason":141},[121,128,133,137],{"code":122,"score":123,"supported":58,"title":124,"abstract":126,"tags":127},"F5",11,[125],"harvester",[125],[],{"code":54,"score":129,"supported":14,"title":130,"abstract":131,"tags":132},2,[],[],[114,29],{"code":44,"score":21,"supported":14,"title":134,"abstract":135,"tags":136},[],[],[27],{"code":49,"score":21,"supported":14,"title":138,"abstract":139,"tags":140},[],[],[26],"农业相关性需要复核",[143,144],"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",4,"10.1016\u002Fj.compag.2026.112432",{"doi":147,"openalex_id":149,"authors":150,"venue":10,"cited_by_count":87,"oa_url":102,"card":163,"direction":167,"ingested_from":94},"W7214013690",[151,153,155,157,160],{"name":152,"orcid":9},"A. M. Aahad",{"name":154,"orcid":9},"Pico Sankari",{"name":156,"orcid":9},"Wei Q. Yang",{"name":158,"orcid":159},"Siniša Todorović","https:\u002F\u002Forcid.org\u002F0000-0001-5793-5921",{"name":161,"orcid":162},"Joseph R. 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智能农机加速下乡","http:\u002F\u002Ftuopin.ce.cn\u002Fnews\u002F202610\u002Ft20261016_3260414.shtml","《山西省智慧农业行动计划（2026—2030年）》细化落地举措，围绕丘陵山地特点推进智慧农业先行先试、智慧农（牧、渔）场建设与技术创新。全省坡耕地1686.66万亩、占耕地总面积29.06%，为此加强丘陵山区智能农机研发引进，开展丘陵山区杂粮农机具研究，组建省级智慧农业产业技术体系。","**阅读提示**\n\n**我省依托科研院所与高校、企业联合攻关，量身研发本土化、适配型智能农机装备。当前，智能技术稳步融入耕种管收储全产业链条，推动农业生产实现精准管控、降本增效。同时，相关部门统筹推行农业社会化服务、落实专项政策补贴、开展定向技能培训等多项举措，打通技术落地堵点，构建智慧农业发展体系，让数字红利惠及广袤田野，书写黄土高原现代农业转型发展新答卷。**\n\n晨光破晓，阳光渐暖。太原市小店区北格镇梁家庄村的智慧农业大棚内，智能设备精准感知田间环境变化，保温卷帘随气温变化平稳升降，全程无需人工操作。\n\n这座由山西优班图科技股份有限公司联合山西农业大学（省农科院）共建的实验基地，是我省设施农业智能化升级的生动缩影，见证着传统农业向智慧农业转变的迭代蜕变。\n\n“大棚卷帘并非一次性全开，而是根据温度变化幅度循序渐进调节。”基地负责人李鹏飞介绍。大棚内各类传感器全天候实时采集温湿度、光照强度、二氧化碳浓度等核心环境数据，通过智能系统自动联动卷帘、通风、灌溉等设备，实现种植环境动态精准调控，彻底颠覆了传统“看天种地”的粗放模式，全面迈入“数据管棚”的智慧农业新阶段。\n\n农业新质生产力，核心是数字技术与农业生产的深度融合。2026年中央一号文件明确提出，要因地制宜发展农业新质生产力，促进人工智能与农业发展相结合，拓展无人机、物联网、机器人等应用场景。对标国家部署，《山西省智慧农业行动计划（2026—2030年）》细化落地举措，加快数字技术与农业深度融合，提升农业智能化水平，提高农业生产效率、资源利用率和农民收入，推动山西现代农业高质量发展。\n\n依托政策牵引、科研攻关、产业赋能，三晋大地各类智能农机、数字装备加速落地，贯穿农业生产全链条，为特色种养提质增效、群众稳定增收注入数字动能。\n\n**立足实际**\n\n**研发适配型智能农机**\n\n我省丘陵山地广、零散坡耕地多。第三次国土调查数据显示，全省坡耕地达1686.66万亩，占耕地总面积29.06%；梯田耕地达1421.68万亩，占耕地总面积24.49%。地块零散、坡度不均、田间道路狭窄，适配小块坡耕地的专用农机短缺，是丘陵山区农业提质增效的主要瓶颈。\n\n靶向破解本土生产难题，我省聚焦农业生产薄弱环节，联动科研机构、涉农企业、专业合作社精准攻关，量身研发适配丘陵山区作业的智能农机装备，补齐山区农业机械化、智能化短板。\n\n8月20日，娄烦县天池店乡南岔村马铃薯新优品标准化种植示范基地内，一垄垄马铃薯长势喜人，智能化种植设备的赋能成效尽显。\n\n“马铃薯长势旺盛，丘陵山区自走式马铃薯种植机发挥了重要作用。”娄烦润和美种植专业合作社负责人尤同义说。这款机具由山西省薯类产业技术体系专家赵旭志、贺孝兵团队研发，可一次性完成开沟、播种、起垄、镇压等一体化作业，通过起垄镇压保墒技术有效提升出苗率，精准解决了丘陵山区小地块大中型农机难进地、小型农机功能单一的行业痛点。\n\n不只马铃薯种植装备，多款适配山区地形的智能农机相继落地推广。针对农业生产各类细分痛点，山西农机科创持续发力、多点突破。\n\n由山西农业大学（省农科院）高粱研究所研究员白文斌团队研发的“大地智牛”全自主覆膜播种机器人，已在阳高、寿阳、襄垣、武乡、永和等多个山区县2000余亩耕地验证使用。该设备集成施肥、覆膜、铺设滴灌带、播种、覆土五道工序，可在6°及以下坡度耕地稳定作业，填补了国内丘陵山区覆膜播种智能农机的空白。设备作业时速稳定，可根据不规则曲线地块自主规划作业路径、动态调整速度，兼顾作业精度与生产效率。\n\n山西沃勃农业发展有限公司与山西农业大学（省农科院）园艺学院等科研部门，聚焦温室果蔬采摘用工紧缺、成本偏高、果实损伤率高的难题，融合多模态视觉与三维定位技术，成功研发履带式自主导航采摘机器人，实现果实精准识别定位、机械臂柔性低损采摘。一台机器人可替代2—3名采摘工，单棚年节约人力成本1.6万元以上。\n\n围绕玉米全生育期绿色除草需求，五台县城园丰农机制造有限公司联合清华大学天津高端装备研究所等单位，集成全地形移动平台、多模态感知组件、智能决策控制器、高精度导航系统、仿生物理除草与松土机构、复合能源系统及物联网作业平台，成功制造覆膜玉米智能除草机器人。该款机器人可自主建立地块与作物行地图，精准识别玉米苗株、杂草、地膜和障碍物，在实现清除田间杂草的同时完成土壤浅松。\n\n太原科技大学和山西农谷现代农业装备科技有限公司等单位，成功研制黄花菜智能采摘机器人。此款采摘机器人依托国际领先的并联机器人技术，可保障高速高精度作业；视觉系统利用高感光相机与多光谱融合增强方法，能快速识别花蕾进行轨迹规划；末端执行器采用了力位混合控制技术，可精准定位采摘点实现无损采摘；控制系统融合激光雷达等多传感器，可实现复杂田块自主导航与动态避障。\n\n一系列本土化、适配型智能农机的研发与落地，精准破解了我省丘陵山地农业生产的诸多短板，为推动山地农业从传统人工劳作向智能高效作业转型提供了全新选择。\n\n**全链赋能**\n\n**贯穿耕管收储全环节**\n\n8月23日，运城市盐湖区龙居镇南花村百忍堂冷库分选车间内，智能分拣设备全速运转，一颗颗红香酥梨经由传送轨道有序输送，通过多维度智能扫描，系统综合比对果体大小、重量、色泽、表皮瑕疵、内部品质等多项参数，依托智能算法自动分级、精准归类，实现标准化分选。\n\n“人工分拣没有统一标准，果品品相、大小参差不齐，很多优质梨果因分拣不规范，卖不上高端市场价。”冷库负责人张蓓坦言。智能分拣设备的投入使用，彻底补齐人工分拣的短板，让优质果品匹配高端价值，有效助力农户增收、产业提质。如今，智能化、数字化技术已全面渗透农业种植、田间管护、采收加工、仓储保鲜全链条，从单一设备示范应用，转向全流程、系统化智慧升级。\n\n田间管护更精细。春季小麦管护关键期，曲沃县曲村镇下坞村麦田里，搭载北斗定位的自由式喷灌机器人灵活作业，180米直径喷射范围实现360°无死角精准供水。相较于传统灌溉模式，设备节水55%左右，作业效率提升85%以上，既省水省力，又保障麦苗均匀受水、长势均衡。小麦灌浆期，临猗县统筹8支专业飞防队伍、119架植保无人机，对全县26.97万亩小麦开展“一喷三防”全覆盖作业，无人机按精准航线低空作业，均匀喷施药剂与叶面肥，筑牢粮食丰产防线。\n\n育苗耕种更高效。山西六新科技有限公司等单位联合研发的数智育苗机器人，搭载双模式自由切换、地块仿形自适应两大核心技术。设备可快速切换条播、撒播作业模式，自动根据苗圃凹凸地表调整播种高度，保证落种均匀；内置定量分料结构，适配大小不同农作物、蔬菜、林木、药材种子，可同步完成播种、覆土、轻度镇压一体化作业，整机机动灵活，适合露天苗圃大范围连续作业。\n\n田间作业更智能。山西恒赫百旺智能科技有限公司研发的丘陵山地无人设备，集成智能感知、精准决策、自主执行功能，可智能生成与坡地、梯田契合的自主飞行航线，实现定高、定速全自主飞行与定量施药作业。\n\n仓储保鲜更安全。太原先元科技有限公司等联合打造的粮食干燥仓储全流程管理系统，以中央集控调度机器人为核心，可智能识别粮食水分，动态调节作业温度、风量，精准完成粮食干燥处理；仓储阶段可24小时自动监测粮情、智能翻粮，全方位保障粮食存储安全。\n\n为持续拓宽智慧农业应用场景，今年7月，省工信厅、省农业农村厅联合发文，聚焦种植、养殖、农产品初加工三大重点领域，启动农业领域机器人典型应用场景遴选工作，以标杆场景引领农机装备创新迭代，推动全省农业机械化、智能化高质量发展。\n\n在北斗导航、智能感知、精准算法等技术支持下，智慧农机已成为三晋大地耕、种、管、收的生力军。\n\n左权县探索建立“县级统筹、农机到户、技术护航、补贴直达”四位一体工作模式，投放300余台配备北斗导航、无人驾驶功能的智能农机，实现耕、种、管、收全链条作业。据该县农业农村局工作人员介绍，智能农机依托导航系统规划作业路径，行距误差控制在2厘米以内，作业深度偏差不超1厘米，可有效杜绝漏耕、重耕问题，种子利用率提升15%以上，农业生产精细化、标准化水平显著提升。\n\n怀仁市依托智慧农机载体，以数据互联互通为支撑，搭建起涵盖耕、种、管、收各环节的智慧农事服务体系，助力当地农业由传统“会种地”向现代“慧种地”迭代升级，大力推行可感知、可控制、可决策的智慧农业发展模式。该市农业机械化发展中心主任郭亮介绍，自2023年起，当地逐步推广农机无人驾驶辅助系统，现有设备保有量约200套，有效降低人工与农资投入成本。\n\n**破解堵点**\n\n**构建长效发展体系**\n\n智能农机、数字装备的广泛应用，为我省农业转型升级、提质增效提供了有力支撑，但高端智能设备造价偏高、小农户购置能力不足等问题，仍然制约着全省智慧农业的规模化普及、全覆盖推广。\n\n“公司研发的履带式智能植保无人机可自主规划路线、24小时不间断作业，性能稳定高效，但价格偏高，目前主要推广于广西、海南等规模化果园种植区，省内仅万荣、临猗等林果大县落地应用。”山西添翼创新科技有限公司总经理闫太平道出行业共性难题。\n\n山西优班图科技股份有限公司等联合研发的智能水肥药一体机同样面临推广困境，设备可根据作物长势精准施肥喷药、节本增效，却因购置成本较高，让众多小农户望而却步。\n\n针对单家独户购置成本高、设备利用率低的痛点，各地立足小农户多、地块碎片化的农业实情，创新探索“智能农机共享+农业生产托管”模式，将高端智能装备转化为社会化服务的“公共农具”，让小农户便捷享受数字农业红利。\n\n社会化服务提质增效，助力普惠共享。临汾市翼城县益农区域农机社会化服务中心，依托全套智能农机装备开展规模化农业托管服务，全程托管土地2万余亩，单环节托管超10万亩，辐射带动周边大量小农户。通过统一智能化作业，实现亩均节本50元以上、增产15%，相关实践成功入选全国农机社会化服务典型案例，为全省智慧农机普惠化推广提供可借鉴的经验。\n\n政策精准托举，打通落地壁垒。相关部门积极落实农机购置与应用补贴政策，将农用无人机、田间智能监测设备、智能耕作机械等新型装备纳入补贴范围，推行优机优补、精准补贴，大幅降低经营主体购置成本。\n\n今年8月，省农机发展中心在清徐县举办农机购置与应用补贴产品现场演示评价活动。本次活动旨在严格规范补贴产品准入标准，夯实农机购置补贴政策实施基础，推动先进适用农机装备的推广应用。参评产品涵盖农用（植保）无人驾驶航空器、农用北斗辅助驾驶系统、北斗农机作业监测终端、田园管理机及微耕机五大品类148个型号。\n\n“我们研发智能农机的最终目标是让农民成为‘白领’，坐在办公室就能种地。”闫太平说，智能农机的广泛落地、高效应用，离不开高素质专业人才的支撑。\n\n今年7月，省农业农村厅印发了《2026年全省高素质农民培育工作实施方案》。根据方案，培育工作专门面向参与智慧农业项目的新型农业经营主体骨干、青年农民骨干、返乡入乡创业群体等重点人群，紧扣我省智慧农业发展核心需求，重点围绕智慧农业发展理念、物联网、大数据、人工智能等信息技术应用、智能农业装备应用、智慧农（牧、渔）场发展等开展培训，破解智能农机“无人会用、无人善用”的人才短板。\n\n同时，全省各地也在积极探索适应智能农机操作的高素质农民培养新途径。朔州市举办的农机手技能提升培训班专门增设北斗导航辅助驾驶、作业质量监测等智能终端操作课程，帮助农机手适应现代农业发展需求。\n\n交口县组织技术人员将无人机驾驶技术培训直接送到田间地头，围绕农业无人机的核心应用场景，从设备构造、飞行原理、安全规范到植保作业参数设置，进行从理论到实操的全流程教学。乡宁县培育30名持证飞手，组建本土专业技术队伍，可熟练操控植保无人机开展梯田智能化作业。\n\n从单点设备示范突破，到全链条技术赋能，我省逐步探索出一条“科研攻关+政策托举+服务赋能”的智慧农业发展路径。曾经依赖人力、看天吃饭的传统农业，正被数据驱动、智能管控的现代化农业重塑。智能农机不再是大型示范基地的专属配置，而是持续下沉千家万户田块的普惠工具，持续释放农业新质生产力效能，为黄土高原旱作农业现代化、三晋乡村全面振兴注入源源不断的数字动能。\n\n![Image 1](https:\u002F\u002Fnginx-sxrb.app.sxrbw.com\u002Fsxrb\u002Fepaper\u002Fpc\u002Fpic\u002F202610\u002F16\u002F49417_0ab510f3-011f-4e35-afe4-86c9214ec13f_l.jpg)\n\n大棚专用自主水肥药农情监测机器人正在进行喷药作业。本报记者 崔如意摄\n\n![Image 2](https:\u002F\u002Fnginx-sxrb.app.sxrbw.com\u002Fsxrb\u002Fepaper\u002Fpc\u002Fpic\u002F202610\u002F16\u002F49417_2321f7b6-ed58-493b-9175-92dbb5561970_l.jpg)\n\n丘陵山区自走式马铃薯种植机正在作业。本报记者 崔如意摄\n\n**相关链接**\n\n《山西省智慧农业行动计划（2026—2030年）》提出，我省将：\n\n1.推进智慧农业先行先试。开展“人工智能+”行动，打造以新技术集群和大模型为支撑的大田种植、设施农业、规模养殖等应用场景，示范推广智能化农机装备。鼓励各市、县因地制宜打造1—2个种植业、畜牧业、渔业等智慧农业应用场景。鼓励建设智慧农业应用引领区，推进种植方案与物联网智能装备、智能灌溉系统、智能农机设备、智慧管理平台等耦合应用。用好线上线下宣传渠道，总结推广智慧农业发展经验做法。\n\n2.推进智慧农（牧、渔）场建设。实施农（牧、渔）场智慧赋能计划，培育一批不同技术模式的智慧农（牧、渔）场。发挥规模主体带动作用，培育一批智慧农业发展社会化服务主体，集成推广智慧农业新技术、新装备。\n\n3.推进智慧农业技术创新。支持科研院校开展智慧农业技术模式研究、集成与推广，利用农业生产过程的数字化仿真、推演，形成最优种植、养殖管理方案。加快智能农机装备技术攻关，加强丘陵山区智能农机装备研发、引进，开展丘陵山区杂粮农机具研究。推进国家级智慧农业重点实验室建设。组建省级智慧农业产业技术体系，为智慧农业发展提供技术支撑。培育一批智慧农业领域科技领军企业，推进智慧农业技术研发、成果转化和产业化应用。促进应用场景需求和研发服务能力对接，推动新技术新产品应用推广。","中国经济网扶贫频道 2026-10-16","2026-10-16T00:00:00Z","报道",69,{"impact":239,"substance":17,"depth":179,"authority":123,"freshness":146,"relevant":21,"comment":240},20,"省级智慧农业综合报道，智能农机研发与全链应用案例扎实、信源多元，但时效偏旧，适合作为主题聚合素材而非当日精选。",[242],{"name":234,"url":231},[26,27,244,245,246],"智能农机","丘陵山区","农业社会化服务",{"code":122,"slug":248,"name":249,"path":250,"parent_code":251},"machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery","F",[],{"status":194,"locked":14,"version":34,"method":35,"evidence":254},{"candidates":255,"agricultural_evidence":58},[256,262,266,270],{"code":122,"score":257,"supported":58,"title":258,"abstract":260,"tags":261},12,[259],"农机",[259],[244],{"code":49,"score":146,"supported":58,"title":263,"abstract":264,"tags":265},[],[26],[26],{"code":44,"score":21,"supported":14,"title":267,"abstract":268,"tags":269},[],[],[27],{"code":271,"score":21,"supported":14,"title":272,"abstract":273,"tags":274},"G2",[],[],[246],[276,277],"山西 智能农机 丘陵山区","山西 农业新质生产力 智慧农业","山西智能农机丘陵山区-5504","2026-10-22T00:06:58.505233Z",{"id":281,"title":282,"url":283,"summary":284,"summary_zh":9,"content":285,"source_name":286,"source_url":9,"published_at":287,"category":288,"cover_url":9,"hotness":13,"is_selected":14,"score":289,"score_detail":290,"sources":292,"tags":294,"primary_topic":298,"secondary_topics":302,"classification":303,"search_phrases":325,"slug":328,"view_count":87,"doi":9,"paper":9,"created_at":329},5420,"广西印发具身智能产业创新发展五年行动计划 布局具身智能加现代农业场景","https:\u002F\u002Fwww.nanning.gov.cn\u002Fzt\u002Frdzt\u002Fyhyshj\u002Fyszc\u002Fbrphcx\u002Ft6740281.html","广西壮族自治区党委科技委员会办公室印发《具身智能产业创新发展五年行动计划（2026—2030年）》（桂科委办发〔2026〕12号），在农业领域明确要求凝练具身智能在农产品采摘、优势作物种植、养殖巡检等真实场景的技术需求，面向矿山开采、农产品采摘、海洋渔业、物流运输等场景开展特种机器人技术攻关。行动提出到2030年组织实施30项以上重点科技攻关项目、培育2家以上具身智能龙头企业、10家以上科技型中小企业、打造20个以上应用场景标杆，其中包括「具身智能+现代农业」场景。农业机器人与采摘装备由此获得明确的地方产业政策支持路径。","（十）挖掘特色应用场景需求。建立健全具身智能应用场景挖掘机制，面向企业、高校、科研院所、医疗机构等，在工业制造、教育培训、科研应用、民生服务等领域常态化征集具身智能应用场景需求，挖掘一批技术适配性强、应用价值高、示范效应好、可复制推广的具身智能应用场景。（责任单位：自治区数据局、发展改革委、教育厅、科技厅、工业和信息化厅、卫生健康委等）\n\n（十一）大力培育标杆应用场景。聚焦广西有色金属及关键金属材料、先进钢铁材料、现代绿色化工、新一代信息技术、机械及高端装备制造、新能源汽车及电池、林产加工及纸业、食品加工、生物医药及健康、绿色建材等十大支柱产业发展需求，围绕矿山无人化、高危环境巡检、材料分拣、精密装配、物料搬运、智能检测、远程运维、智能仓储等重要环节，打造“具身智能+制造业”应用场景；在优势作物种植、采摘、养殖巡检等现代农业领域开展创新应用与示范推广，打造“具身智能+现代农业”应用场景；在港口物流、能源、文旅等领域拓展具身智能应用，打造一批融合应用场景。到2030年，打造20个以上应用场景标杆。（责任单位：自治区科技厅、发展改革委、工业和信息化厅、交通运输厅、农业农村厅、文化和旅游厅、数据局等）","南宁市人民政府门户网站 2026-10-15","2026-10-15T00:00:00Z","政策",71,{"impact":17,"substance":239,"depth":179,"authority":123,"freshness":97,"relevant":21,"comment":291},"省级具身智能五年行动计划明确布局“具身智能+现代农业”场景，含2030年20个标杆等量化目标，政策条款具体，对农业机器人方向有实质参考价值。",[293],{"name":286,"url":283},[26,27,295,296,297],"具身智能","现代农业","产业规划",{"code":44,"slug":299,"name":300,"path":301,"parent_code":251},"agricultural-robots","机器人与无人农场","\u002Ftopic\u002Finputs-equipment\u002Fagricultural-robots",[],{"status":194,"locked":14,"version":34,"method":35,"evidence":304},{"candidates":305,"agricultural_evidence":58},[306,310,316,320],{"code":44,"score":146,"supported":58,"title":307,"abstract":308,"tags":309},[],[27],[27],{"code":311,"score":64,"supported":58,"title":312,"abstract":313,"tags":315},"D",[],[314],"渔业",[],{"code":49,"score":21,"supported":14,"title":317,"abstract":318,"tags":319},[],[],[26],{"code":321,"score":21,"supported":14,"title":322,"abstract":323,"tags":324},"J2",[],[],[296],[326,327],"广西 具身智能 现代农业","具身智能 农业机器人 应用场景","广西具身智能现代农业-5420","2026-10-21T00:09:54.769055Z",{"id":331,"title":332,"url":333,"summary":334,"summary_zh":9,"content":9,"source_name":335,"source_url":333,"published_at":336,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":337,"score_detail":338,"sources":341,"tags":343,"primary_topic":345,"secondary_topics":346,"classification":347,"search_phrases":359,"slug":362,"view_count":87,"doi":363,"paper":364,"created_at":369},5380,"Automation, Robotics, and Precision Agriculture Transformation Through Smart Harvest Systems","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.17987.98089","Automation, Robotics, and Precision Agriculture Transformation Through Smart Harvest Systems。OpenAlex","OpenAlex","2026-10-19T00:00:00Z",56,{"impact":17,"substance":339,"depth":13,"authority":257,"freshness":339,"relevant":21,"comment":340},8,"主题契合智慧农业，但摘要仅为标题重复、缺乏方法与数据细节，信息增量有限，暂不建议进入每日精选。",[342],{"name":335,"url":333},[26,27,185,344],"智能采收",{"code":49,"slug":188,"name":189,"path":190,"parent_code":191},[],{"status":194,"locked":14,"version":34,"method":35,"evidence":348},{"candidates":349,"agricultural_evidence":58},[350,355],{"code":49,"score":351,"supported":58,"title":352,"abstract":353,"tags":354},13,[201],[201],[26,185],{"code":44,"score":21,"supported":14,"title":356,"abstract":357,"tags":358},[],[],[27],[360,361],"农业机器人 智慧农业 智能采收 精准农业","农业机器人 智慧农业","农业机器人智慧农业智能采收精准农业-5380","10.13140\u002Frg.2.2.17987.98089",{"doi":363,"openalex_id":365,"authors":366,"venue":9,"cited_by_count":87,"oa_url":333,"card":9,"direction":92,"ingested_from":94},"W7222971426",[367],{"name":368,"orcid":9},"Joydeep Dey","2026-10-20T23:30:08.600540Z",{"id":371,"title":372,"url":373,"summary":374,"summary_zh":375,"content":9,"source_name":376,"source_url":373,"published_at":235,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":377,"score_detail":378,"sources":381,"tags":383,"primary_topic":387,"secondary_topics":388,"classification":389,"search_phrases":406,"slug":409,"view_count":21,"doi":410,"paper":411,"created_at":430},5240,"Low-Cost Car-Like Autonomous Mobile Robot Under-Canopy Row Following, and Detection of Patterns Associated with Diseases in Plantain Crops","https:\u002F\u002Fdoi.org\u002F10.3991\u002Fijoe.v22i10.61223","Labor constraints and the need for frequent visual inspection of lower plantain (Musa paradisiaca) leaves in Colombia’s Orinoquia region motivate the development of low-cost robotic tools capable of autonomous under-canopy navigation and systematic image capture. This study reports the current stage of an ongoing research effort on a low-cost, small-scale, car-like agricultural robot for autonomous under-canopy row-following and systematic image capture in plantain fields. The platform uses a modular ROS 2 workflow, a low-cost 2D LiDAR, an inertial measurement unit, and an ESP32-based actuation layer. LiDAR data were processed through an adaptive measurement cone guided by the vehicle heading error to estimate the lateral distance to the crop row during forward row-following motion, and a fuzzy controller generated steering references. Initial field tests across five trials provided preliminary evidence of consistent under-canopy navigation under the tested conditions. In the first rowfollowing segment, the median MAE and RMSE of the lateral error were 0.0258 m and 0.0342 m, respectively; after the row-to-row maneuver, they were 0.0356 m and 0.0602 m, respectively. Additionally, an upward-facing RGB camera captured under-canopy images that were processed offline to create probability-based visual summaries of leaf-condition patterns. The results show that low-cost robotic navigation can help structure under-canopy data collection for scalable crop health monitoring in plantain plantations.","哥伦比亚奥里诺基亚地区劳动力受限，且需要对下部车前草（Musa paradisiaca）叶片进行频繁目视检查，这促使人们开发能够自主冠层下导航并系统采集图像的低成本机器人工具。本研究报道了针对一种低成本、小型、类车式农业机器人开展的在研工作的当前阶段，该机器人用于车前草田间的自主冠层下行跟随与系统图像采集。该平台采用模块化ROS 2工作流、低成本二维激光雷达、惯性测量单元以及基于ESP32的驱动层。激光雷达数据通过由车辆航向误差引导的自适应测量锥进行处理，以在前进行跟随运动中估计与作物行的横向距离，并由模糊控制器生成转向参考。在五种试验条件下的初步田间测试提供了在所测条件下冠层下导航具有一致性的初步证据。在第一个行跟随段中，横向误差的中位MAE和RMSE分别为0.0258 m和0.0342 m；在行间机动之后，分别为0.0356 m和0.0602 m。此外，一台朝上的RGB相机采集了冠层下图像，并对其进行离线处理，以生成基于概率的叶片状况模式视觉摘要。结果表明，低成本机器人导航有助于构建冠层下数据采集流程，从而支持车前草种植园中可扩展的作物健康监测。","International Journal of Online and Biomedical Engineering (iJOE)",70,{"impact":257,"substance":239,"depth":379,"authority":257,"freshness":20,"relevant":21,"comment":380},17,"低成本ROS 2农业机器人实现冠层下自主行跟随与叶片病害图像采集，方法新颖、田间数据初步可靠，但属细分领域阶段性成果，公共影响有限。",[382],{"name":376,"url":373},[26,27,384,385,386],"自主导航","作物病害检测","香蕉种植",{"code":44,"slug":299,"name":300,"path":301,"parent_code":251},[],{"status":194,"locked":14,"version":34,"method":35,"evidence":390},{"candidates":391,"agricultural_evidence":58},[392,398,402],{"code":44,"score":393,"supported":58,"title":394,"abstract":395,"tags":397},7,[],[27,396],"agricultural robot",[27],{"code":122,"score":21,"supported":14,"title":399,"abstract":400,"tags":401},[],[],[384],{"code":49,"score":21,"supported":14,"title":403,"abstract":404,"tags":405},[],[],[26],[407,408],"低成本农业机器人 大蕉 冠层导航","plantain 病害 图像检测","低成本农业机器人大蕉冠层导航-5240","10.3991\u002Fijoe.v22i10.61223",{"doi":410,"openalex_id":412,"authors":413,"venue":376,"cited_by_count":87,"oa_url":424,"card":425,"direction":92,"ingested_from":94},"W7222629670",[414,417,419,421],{"name":415,"orcid":416},"Danna Celena Montenegro","https:\u002F\u002Forcid.org\u002F0009-0003-0909-7806",{"name":418,"orcid":9},"Iván D. Duarte",{"name":420,"orcid":9},"Andres Leonardo Jutinico",{"name":422,"orcid":423},"Henry Borrero Guerrero","https:\u002F\u002Forcid.org\u002F0000-0003-4243-4205","https:\u002F\u002Fonline-journals.org\u002Findex.php\u002Fi-joe\u002Farticle\u002Fdownload\u002F61223\u002F17460",{"tldr":426,"method":427,"finding":428,"direction":92,"opportunity":429},"开发低成本类车机器人实现车前草冠层下自主行跟随与图像采集。","ROS 2、2D LiDAR、IMU、ESP32及模糊控制器，离线处理RGB图像","横向误差中位数MAE约0.026-0.036米，验证了低成本导航可行性。","可研究多行自主切换、病害图像在线识别及复杂冠层下鲁棒导航。","2026-10-18T23:30:09.565705Z"]