[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2506":3,"related-2506":95},{"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":63,"slug":66,"view_count":40,"doi":67,"paper":68,"created_at":94},2506,"LitchiInst: Instance segmentation of the main fruit-bearing branch via fruit-branch association for robotic litchi harvesting","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104586","Accurate picking point localisation is a fundamental challenge in robotic harvesting. For litchi, this requires precise identification of the main fruit-bearing branch (MFBB). Current methods, however, rely on implicit structural association, failing to learn the explicit spatial correlation between fruits and branches, which leads to frequent misidentification. To address this, LitchiInst is proposed, a real-time instance segmentation framework that explicitly models this fruit-branch dependency. The core of LitchiInst is a Structure Association Guidance (SAG) mechanism, which employs SAG queries and a dedicated SAG loss to enforce spatial correlation and enable structure-aware discrimination. The framework is further enhanced by an Enhanced Feature Pyramid Network (EFPN) to capture fine-grained MFBB features and a Mask-to-3D head to convert segmentation masks into stable 3D picking points for robotic execution. LitchiInst improves MFBB average precision by 31.06% relative to the baseline while maintaining real-time processing. In real-robot experiments across three testing settings, LitchiInst achieved a 92.0% MFBB detection success rate. These results further demonstrate its practical potential as a visual guidance module for automated litchi harvesting.","准确的采摘点定位是机器人采收面临的一项基本挑战。对于荔枝而言，这需要精确识别主结果枝（MFBB）。然而，现有方法依赖隐式结构关联，无法学习果实与枝条之间显式的空间相关性，导致误识别频发。为解决这一问题，提出了LitchiInst，一种实时实例分割框架，能够显式建模这种果枝依赖关系。LitchiInst的核心是结构关联引导（SAG）机制，该机制采用SAG查询和专用的SAG损失来强化空间相关性并实现结构感知判别。该框架还通过增强特征金字塔网络（EFPN）捕获细粒度MFBB特征，并利用Mask-to-3D头将分割掩码转换为稳定的3D采摘点以供机器人执行。相较于基线，LitchiInst将MFBB平均精度提升了31.06%，同时保持实时处理能力。在三种测试设置下的真实机器人实验中，LitchiInst实现了92.0%的MFBB检测成功率。这些结果进一步证明了其作为自动化荔枝采收视觉引导模块的实际潜力。",null,"Biosystems Engineering","2026-09-14T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"提出显式建模果枝空间关联的实时实例分割框架，主结果枝识别精度提升31.06%、真机成功率92%，对荔枝采摘机器人视觉引导有实质推进，但属细分作物技术进展，未达产业级突破。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","采摘机器人","荔枝","机器视觉",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":41,"reason":62},[38,45,51,57],{"code":39,"score":40,"supported":41,"title":42,"abstract":43,"tags":44},"B2",4,true,[],[29],[29],{"code":46,"score":40,"supported":41,"title":47,"abstract":48,"tags":50},"F6",[],[49],"robotic harvesting",[28],{"code":52,"score":53,"supported":14,"title":54,"abstract":55,"tags":56},"I2",2,[],[],[27,30],{"code":58,"score":21,"supported":14,"title":59,"abstract":60,"tags":61},"I1",[],[],[26],"多个产业证据相当，需要确认主归属",[64,65],"农业人工智能 采摘机器人 智慧农业 机器视觉","农业人工智能 采摘机器人","农业人工智能采摘机器人智慧农业机器视觉-2506","10.1016\u002Fj.biosystemseng.2026.104586",{"doi":67,"openalex_id":69,"authors":70,"venue":10,"cited_by_count":86,"oa_url":6,"card":87,"direction":91,"ingested_from":93},"W7213074904",[71,74,76,79,81,83],{"name":72,"orcid":73},"Yukun Qian","https:\u002F\u002Forcid.org\u002F0000-0001-9095-4024",{"name":75,"orcid":9},"Wenchang Chai",{"name":77,"orcid":78},"Haitao Wang","https:\u002F\u002Forcid.org\u002F0000-0002-8394-6410",{"name":80,"orcid":9},"Zhiyang Mai",{"name":82,"orcid":9},"Liangliang Zhou",{"name":84,"orcid":85},"Hejun Wu","https:\u002F\u002Forcid.org\u002F0000-0001-9758-5698",0,{"tldr":88,"method":89,"finding":90,"direction":91,"opportunity":92},"提出LitchiInst实例分割框架，通过果实-枝条关联实现荔枝主结果枝实时分割与采摘点定位。","结构关联引导机制、增强特征金字塔网络和Mask-to-3D头，基于荔枝图像数据。","主结果枝平均精度提升31.06%，真实机器人实验检测成功率达92.0%。","农业人工智能与决策模型","可探索果实-枝条显式关联机制在其他果园采摘中的迁移，并融合时序信息提升遮挡下的鲁棒性。","openalex","2026-09-15T23:30:05.541830Z",{"total":96,"page":21,"page_size":96,"items":97},6,[98,164,232,301,356,407],{"id":99,"title":100,"url":101,"summary":102,"summary_zh":103,"content":9,"source_name":104,"source_url":101,"published_at":105,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":106,"score_detail":107,"sources":110,"tags":112,"primary_topic":9,"secondary_topics":114,"classification":115,"search_phrases":140,"slug":143,"view_count":144,"doi":145,"paper":146,"created_at":163},4174,"Deep learning-based multi-view perception and adaptive grasp pose correction for cooperative dual-arm fruit harvesting under occlusion","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.aiia.2026.09.004","This study presents a failure-recovery cooperative harvesting pipeline for a dual-arm agricultural robot, where each arm harvests independently and switches to cooperative manipulation only when a single-view grasp on an occluded fruit fails. Such cooperation is necessary because accurate grasp pose inference degrades substantially when target fruits are heavily occluded by surrounding foliage or branches; existing approaches, including explicit 6D pose estimation and shape-completion-based methods, depend on reconstructing geometric information that is often unavailable under heavy occlusion. To address this limitation, the proposed framework, referred to as the Cooperative Active Harvesting Robot Scheme (Co-AHRS), integrates direct grasp pose inference from incomplete point clouds, active multi-view perception, and closed-loop visual servoing. Instead of reconstructing hidden geometry, the framework leverages Contact-GraspNet to generate feasible grasp candidates directly from partial observations. When the initial grasp attempt fails—due to either inaccurate pose inference under heavy occlusion or physical displacement of the target fruit during approach—the opposite arm is reassigned as an auxiliary observer whose upward-facing viewpoint enables closed-loop visual servoing, continuously estimating the 3D positional error between the gripper and the target fruit for real-time pose correction. The Co-AHRS was evaluated through a laboratory implementation and achieved harvest success rates of 89.2% and 78.5% under unoccluded and occluded conditions, respectively. The proposed framework also substantially outperformed frustum-based localization (75.0% and 55.2%) and explicit 6D pose estimation (57.1% and 7.7%). A further evaluation on a second configuration built from two same-model manipulators verified that the two arms harvest in parallel and quantified the occluded condition by a visible surface ratio of 0.549 ± 0.101. These results demonstrate that direct grasp inference combined with cooperative multi-view visual feedback is an effective strategy for autonomous fruit harvesting in occlusion-prone orchard environments.","本研究提出了一种面向双臂农业机器人的失败恢复协同采收流水线：每条机械臂独立执行采收，仅当对遮挡果实的单视角抓取失败时，才切换至协同操作。这种协同是必要的，因为当目标果实被周围枝叶严重遮挡时，准确抓取位姿推断会显著退化；现有方法，包括显式6D位姿估计和基于形状补全的方法，依赖于重建几何信息，而这些信息在严重遮挡下往往无法获得。为解决这一局限，所提出的框架——称为协同主动采收机器人方案（Cooperative Active Harvesting Robot Scheme，Co-AHRS）——集成了从不完整点云直接进行抓取位姿推断、主动多视角感知以及闭环视觉伺服。该框架不重建隐藏几何结构，而是利用Contact-GraspNet直接从部分观测中生成可行抓取候选。当初始抓取尝试失败时——原因可能是严重遮挡下的位姿推断不准确，或目标果实在接近过程中发生物理位移——对侧机械臂被重新分配为辅助观察者，其朝上的视角可实现闭环视觉伺服，持续估计夹爪与目标果实之间的三维位置误差，以进行实时位姿校正。Co-AHRS通过实验室实现进行了评估，在无遮挡和有遮挡条件下分别取得了89.2%和78.5%的采收成功率。所提出的框架也显著优于基于视锥的定位（75.0%和55.2%）以及显式6D位姿估计（57.1%和7.7%）。在由两台同型号机械臂构成的第二种配置上进行的进一步评估验证了两条机械臂可并行采收，并以0.549 ± 0.101的可见表面比例量化了遮挡条件。这些结果表明，直接抓取推断结合协同多视角视觉反馈是在易遮挡果园环境中实现自主果实采收的有效策略。","Artificial Intelligence in Agriculture","2026-10-01T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":108,"relevant":21,"comment":109},8,"提出双臂协同主动多视角感知与闭环视觉伺服抓取修正框架，遮挡条件下采摘成功率达78.5%，方法新颖、对比充分，对果园自动化采摘有实质参考价值。",[111],{"name":104,"url":101},[26,27,28,30,113],"双臂协同",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":116},{"candidates":117,"agricultural_evidence":41,"reason":62},[118,125,130,136],{"code":46,"score":13,"supported":41,"title":119,"abstract":120,"tags":124},[],[121,122,123],"农业机器人","agricultural robot","harvesting robot",[28],{"code":52,"score":13,"supported":41,"title":126,"abstract":128,"tags":129},[127],"deep learning",[],[27,30],{"code":39,"score":131,"supported":41,"title":132,"abstract":133,"tags":135},3,[],[134],"orchard",[],{"code":58,"score":21,"supported":14,"title":137,"abstract":138,"tags":139},[],[],[26],[141,142],"双臂采摘机器人 遮挡 抓取","Co-AHRS 多视角感知","双臂采摘机器人遮挡抓取-4174",5,"10.1016\u002Fj.aiia.2026.09.004",{"doi":145,"openalex_id":147,"authors":148,"venue":104,"cited_by_count":86,"oa_url":156,"card":157,"direction":162,"ingested_from":93},"W7215102066",[149,151,153],{"name":150,"orcid":9},"Jeongil Seo",{"name":152,"orcid":9},"Han Ul Yoon",{"name":154,"orcid":155},"Kwang-Eun Ko","https:\u002F\u002Forcid.org\u002F0000-0001-6225-8080","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2589721726001133\u002Fpdf",{"tldr":158,"method":159,"finding":160,"direction":91,"opportunity":161},"提出双臂协作采摘框架，单臂失败时另一臂辅助多视角感知并闭环修正抓取位姿。","Contact-GraspNet直接从残缺点云推理抓取，配合主动多视角与视觉伺服","无遮挡与遮挡下采摘成功率89.2%和78.5%，显著优于6D位姿估计等方法。","可探索多臂任务分配与失败恢复策略学习，提升密集遮挡果园的自主采摘鲁棒性。","农业遥感与作物表型","2026-10-04T23:30:27.274509Z",{"id":165,"title":166,"url":167,"summary":168,"summary_zh":9,"content":9,"source_name":169,"source_url":9,"published_at":170,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":171,"score_detail":172,"sources":174,"tags":176,"primary_topic":178,"secondary_topics":184,"classification":195,"search_phrases":219,"slug":222,"view_count":144,"doi":9,"paper":223,"created_at":231},3249,"设施番茄采摘机器人识别定位与采摘方法研究——江苏大学雷志龙等 基于改进YOLO v5-HSV融合算法识别准确率95.01%","http:\u002F\u002Fwww.qikanzj.com\u002Fhek\u002Fnyjxxb\u002Fmulu\u002F455758.html","智慧农业(中英文)期刊发表江苏大学雷志龙、刘畅、王权团队研究：针对设施单果番茄采摘需求，设计一款设施番茄智能采摘平台，主要由升降机构、采摘机构、识别与定位系统等部分组成。平台整体结构由低压一体化伺服滚珠丝杠副升降机构、六轴协作机械臂和力控末端执行器组成。基于改进YOLO v5-HSV融合算法来识别检测，通过对H分量进行图像阈值分割，提高对成熟目标果实识别的准确率，有效排除未成熟番茄和枝叶背景的干扰；通过眼在手外的标定方法，使用ZED双目相机进行定位。搭建的设施番茄采摘机样机平台在现场采摘试验中识别准确率达到95.01%，采摘成功率为87.96%，单果平均采摘时间为14.56s。","智慧农业(中英文)·江苏大学","2026-09-22T00:00:00Z",82,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":13,"relevant":21,"comment":173},"核心期刊论文，方法有改进、数据完整，识别准确率与采摘成功率等指标明确，对设施农业智能装备研发有参考价值。",[175],{"name":169,"url":167},[26,27,28,30,177],"设施番茄",{"code":179,"slug":180,"name":181,"path":182,"parent_code":183},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[185,190],{"code":52,"slug":186,"name":187,"path":188,"parent_code":189},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":46,"slug":191,"name":192,"path":193,"parent_code":194},"agricultural-robots","机器人与无人农场","\u002Ftopic\u002Finputs-equipment\u002Fagricultural-robots","F",{"status":196,"locked":14,"version":34,"method":35,"evidence":197},"classified",{"candidates":198,"agricultural_evidence":41},[199,205,211,215],{"code":52,"score":200,"supported":41,"title":201,"abstract":203,"tags":204},13,[202],"YOLO",[202],[27,30],{"code":179,"score":206,"supported":41,"title":207,"abstract":209,"tags":210},12,[208],"番茄",[208],[177],{"code":46,"score":20,"supported":41,"title":212,"abstract":213,"tags":214},[28],[],[28],{"code":58,"score":40,"supported":41,"title":216,"abstract":217,"tags":218},[],[26],[26],[220,221],"江苏大学 设施番茄 采摘机器人","YOLO v5 番茄 识别定位","江苏大学设施番茄采摘机器人-3249",{"doi":9,"openalex_id":9,"authors":224,"venue":9,"cited_by_count":86,"oa_url":9,"card":225,"direction":91,"ingested_from":230},[],{"tldr":226,"method":227,"finding":228,"direction":91,"opportunity":229},"设计设施番茄智能采摘平台，融合改进YOLO v5与HSV实现识别定位与采摘。","改进YOLO v5-HSV融合算法、ZED双目相机眼在手外标定、六轴机械臂力控末","识别准确率95.01%，采摘成功率87.96%，单果平均采摘时间14.56秒。","可探索多果簇、遮挡与弱光环境下识别定位鲁棒性，并优化采摘效率与末端力控。","agent","2026-09-23T00:04:33.401768Z",{"id":233,"title":234,"url":235,"summary":236,"summary_zh":237,"content":9,"source_name":238,"source_url":235,"published_at":239,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":240,"score_detail":241,"sources":244,"tags":246,"primary_topic":248,"secondary_topics":253,"classification":254,"search_phrases":274,"slug":275,"view_count":131,"doi":276,"paper":277,"created_at":300},2116,"Intelligent robotic systems for flower harvesting: A review of vision algorithms, end-effectors, and motion planning strategies","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112415","Intelligent robotic systems for flower harvesting: A review of vision algorithms, end-effectors, and motion planning strategies。Computers and Electronics in Agriculture","用于花卉采收的智能机器人系统：视觉算法、末端执行器与运动规划策略综述。《农业计算机与电子》","Computers and Electronics in Agriculture","2026-09-10T00:00:00Z",78,{"impact":17,"substance":242,"depth":17,"authority":19,"freshness":108,"relevant":21,"comment":243},20,"核心期刊综述系统梳理花卉采摘机器人的视觉算法、末端执行器与运动规划，对设施花卉智能化有实质参考价值，值得进入每日精选。",[245],{"name":238,"url":235},[26,27,28,247,30],"花卉产业",{"code":249,"slug":250,"name":251,"path":252,"parent_code":183},"B4","flowers","花卉与观赏植物","\u002Ftopic\u002Fhorticulture\u002Fflowers",[],{"status":196,"locked":14,"version":34,"method":35,"evidence":255},{"candidates":256,"agricultural_evidence":41},[257,262,266,270],{"code":249,"score":40,"supported":41,"title":258,"abstract":259,"tags":261},[],[260],"花卉",[247],{"code":52,"score":53,"supported":14,"title":263,"abstract":264,"tags":265},[],[],[27,30],{"code":46,"score":21,"supported":14,"title":267,"abstract":268,"tags":269},[],[],[28],{"code":58,"score":21,"supported":14,"title":271,"abstract":272,"tags":273},[],[],[26],[64,65],"农业人工智能采摘机器人智慧农业机器视觉-2116","10.1016\u002Fj.compag.2026.112415",{"doi":276,"openalex_id":278,"authors":279,"venue":238,"cited_by_count":86,"oa_url":235,"card":295,"direction":91,"ingested_from":93},"W7212108390",[280,283,286,289,291,293],{"name":281,"orcid":282},"Jiao Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-6947-6002",{"name":284,"orcid":285},"Dingxuan Zhao","https:\u002F\u002Forcid.org\u002F0000-0002-3372-9477",{"name":287,"orcid":288},"Seyed Mohamad Javidan","https:\u002F\u002Forcid.org\u002F0000-0003-0447-8645",{"name":290,"orcid":9},"Yiannis Ampatzidis",{"name":292,"orcid":9},"Qiyang Li",{"name":294,"orcid":9},"Zhao Zhang",{"tldr":296,"method":297,"finding":298,"direction":91,"opportunity":299},"综述花卉采摘智能机器人系统，涵盖视觉算法、末端执行器与运动规划策略。","文献综述，梳理视觉算法、末端执行器与运动规划三类关键技术。","花卉采摘机器人需视觉、执行器与运动规划协同优化，仍面临复杂环境挑战。","花卉采摘专用视觉算法与柔性末端执行器研究不足，可探索多臂协同与实时运动规划。","2026-09-11T23:30:01.725533Z",{"id":302,"title":303,"url":304,"summary":305,"summary_zh":9,"content":9,"source_name":306,"source_url":304,"published_at":307,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":308,"score_detail":309,"sources":313,"tags":315,"primary_topic":318,"secondary_topics":322,"classification":323,"search_phrases":335,"slug":338,"view_count":86,"doi":339,"paper":340,"created_at":355},5539,"Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10473-z","Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems。Precision Agriculture","Precision Agriculture","2026-10-22T00:00:00Z",79,{"impact":310,"substance":311,"depth":17,"authority":19,"freshness":13,"relevant":21,"comment":312},16,21,"核心期刊新论文，提出鲁棒且可解释的RGB作物行检测方法，对视觉导航农机具参考价值明确，但属细分技术进展，未达重大突破层级。",[314],{"name":306,"url":304},[26,27,316,30,317],"精准农业","作物行检测",{"code":58,"slug":319,"name":320,"path":321,"parent_code":189},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":196,"locked":14,"version":34,"method":35,"evidence":324},{"candidates":325,"agricultural_evidence":41},[326,331],{"code":58,"score":144,"supported":41,"title":327,"abstract":328,"tags":330},[],[329],"precision agriculture",[26,316],{"code":52,"score":53,"supported":14,"title":332,"abstract":333,"tags":334},[],[],[27,30],[336,337],"RGB 作物行检测","农业人工智能 作物行检测 智慧农业 机器视觉","RGB作物行检测-5539","10.1007\u002Fs11119-026-10473-z",{"doi":339,"openalex_id":341,"authors":342,"venue":306,"cited_by_count":86,"oa_url":9,"card":9,"direction":9,"ingested_from":93},"W7223117828",[343,346,349,352],{"name":344,"orcid":345},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":347,"orcid":348},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":350,"orcid":351},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":353,"orcid":354},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","2026-10-22T11:20:09.678587Z",{"id":357,"title":358,"url":359,"summary":360,"summary_zh":9,"content":9,"source_name":361,"source_url":9,"published_at":362,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":363,"score_detail":364,"sources":366,"tags":368,"primary_topic":371,"secondary_topics":372,"classification":373,"search_phrases":395,"slug":398,"view_count":53,"doi":9,"paper":399,"created_at":406},4649,"[预印本]面向自主熟果采摘的智能语音引导UAV系统:基于视觉分类与自适应路径规划(IVG-UAV: An Intelligent Voice-Guided UAV System for Autonomous Ripe Fruit Harvesting with Vision-Based Classification and Adaptive Path Planning)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.09398","arXiv论文(2610.09398,2026年10月7日提交)提出IVG-UAV系统集成Whisper语音识别、LLM、计算机视觉成熟度分类与Gazebo仿真中的自适应路径规划,实现热带大型种植园自动采摘任务。系统在受控仿真环境验证,实际田间作业、复杂光照与多机协同性能待测。[预印本未经同行评议,仅仿真验证]。","arXiv 2026-10-07 [预印本]","2026-10-07T00:00:00Z",62,{"impact":206,"substance":17,"depth":310,"authority":108,"freshness":108,"relevant":21,"comment":365},"预印本提出语音引导无人机自主采摘系统，方法集成有新意但仅仿真验证，属细分领域技术进展。",[367],{"name":361,"url":359},[26,369,27,28,370],"无人机","语音交互",{"code":52,"slug":186,"name":187,"path":188,"parent_code":189},[],{"status":196,"locked":14,"version":34,"method":35,"evidence":374},{"candidates":375,"agricultural_evidence":41},[376,381,387,391],{"code":52,"score":40,"supported":41,"title":377,"abstract":378,"tags":380},[],[379],"计算机视觉",[27],{"code":382,"score":131,"supported":41,"title":383,"abstract":384,"tags":386},"A7",[],[385],"间作",[],{"code":46,"score":21,"supported":14,"title":388,"abstract":389,"tags":390},[],[],[28],{"code":58,"score":21,"supported":14,"title":392,"abstract":393,"tags":394},[],[],[26],[396,397],"IVG-UAV 采摘无人机","Whisper 语音识别 农业机器人","IVG-UAV采摘无人机-4649",{"doi":9,"openalex_id":9,"authors":400,"venue":9,"cited_by_count":86,"oa_url":9,"card":401,"direction":91,"ingested_from":230},[],{"tldr":402,"method":403,"finding":404,"direction":91,"opportunity":405},"提出IVG-UAV系统，用语音、视觉与路径规划实现仿真中自主采摘熟果。","集成Whisper语音识别、LLM、视觉成熟度分类与Gazebo自适应路径规划。","仿真环境验证可行，但真实田间、复杂光照与多机协同性能待测。","可研究真实果园复杂光照下的鲁棒视觉分类与多机协同采摘调度。","2026-10-11T00:09:47.894542Z",{"id":408,"title":409,"url":410,"summary":411,"summary_zh":9,"content":9,"source_name":412,"source_url":9,"published_at":413,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":308,"score_detail":414,"sources":416,"tags":418,"primary_topic":9,"secondary_topics":421,"classification":422,"search_phrases":438,"slug":441,"view_count":131,"doi":9,"paper":442,"created_at":449},4285,"Lightweight AI Gives Harvesting Robots a Sharper Eye for Green Jujubes","https:\u002F\u002Fscienmag.com\u002Flightweight-ai-gives-harvesting-robots-a-sharper-eye-for-green-jujubes","广西师范大学朱红燕团队与浙江大学何勇合作，研发协作感知框架用于青枣采摘机器人。框架包含两个组件：RDA-YOLO是基于YOLOv11n-POSE构建的轻量级神经网络，引入DSConv降低计算开销，设计C3k2_RepViT改善多尺度特征融合，自定义ARC-PoseHead采用对数尺度极坐标回归和亚像素细化提升关键点定位精度；SAM2用于检测框内的细粒度轮廓分割，评估遮挡状态并确定收获优先级。","BMC Plant Biology 2026-10-03 Zhu Hongyan et al. Guangxi Normal Univ & Zhejiang Univ","2026-10-03T00:00:00Z",{"impact":17,"substance":311,"depth":17,"authority":19,"freshness":108,"relevant":21,"comment":415},"轻量化姿态估计与分割协作框架提升青枣采摘机器人遮挡识别与优先级判断，方法新颖、来源核心期刊，具细分领域参考价值。",[417],{"name":412,"url":410},[26,27,419,28,420],"目标检测","青枣",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":423},{"candidates":424,"agricultural_evidence":14,"reason":437},[425,429,433],{"code":52,"score":144,"supported":41,"title":426,"abstract":427,"tags":428},[],[202],[27,419],{"code":46,"score":40,"supported":41,"title":430,"abstract":431,"tags":432},[],[28],[28],{"code":58,"score":21,"supported":14,"title":434,"abstract":435,"tags":436},[],[],[26],"农业相关性需要复核",[439,440],"广西师范大学 青枣 采摘机器人","RDA-YOLO 青枣 检测","广西师范大学青枣采摘机器人-4285",{"doi":9,"openalex_id":9,"authors":443,"venue":9,"cited_by_count":86,"oa_url":9,"card":444,"direction":91,"ingested_from":230},[],{"tldr":445,"method":446,"finding":447,"direction":91,"opportunity":448},"研发协作感知框架，让青枣采摘机器人精准识别与定位果实。","基于YOLOv11n-POSE的RDA-YOLO轻量网络结合SAM2分割，优化关","框架能有效评估遮挡并确定收获优先级，提升采摘机器人感知精度。","可探索轻量模型在复杂果园环境下的泛化能力及多机器人协同采摘决策。","2026-10-06T00:05:04.907455Z"]