[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3441":3,"related-3441":46},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":8,"paper":36,"created_at":45},3441,"《Sensing Technologies in Robotic Manipulators for Low-Damage Fruit and Vegetable Grasping: Principles, Integration, and Applications》","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F19\u002F2040","江苏大学王立峰等综述了果蔬低损采摘机器人感知技术：首先分析果蔬几何、机械和质量相关特性所对应的感知需求，然后系统考察光学、电学、声学感知技术的工作原理、信息特征和功能适配性；基于感知模块与机械手之间的空间关系，将现有传感器集成策略分为外置式、手掌中心式、手指接触式、嵌入式共形和多位置协同配置；探讨生物变异性、环境干扰、结构耦合、传感器耐久性和多模态信息融合等关键挑战。",null,"《Agriculture》2026, 16(19), 2040 \u002F 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苗中华 农业机器人","具身智能 农业机器人 五位一体","上海大学苗中华农业机器人-3252",{"doi":8,"openalex_id":8,"authors":74,"venue":8,"cited_by_count":35,"oa_url":8,"card":75,"direction":79,"ingested_from":44},[],{"tldr":76,"method":77,"finding":78,"direction":79,"opportunity":80},"系统阐述具身智能驱动的农业机器人技术体系，提出五位一体研究框架。","构建'感知-决策-模拟-进化-诊断'五位一体框架，综述关键技术。","具身智能可突破非结构化环境等瓶颈，为农业机器人提供新范式。","农业人工智能与决策模型","可探索具身智能在农业非结构化场景的落地验证与虚实迁移效率提升。","2026-09-23T00:04:33.744630Z",{"id":83,"title":84,"url":85,"summary":86,"summary_zh":8,"content":8,"source_name":87,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":88,"score_detail":89,"sources":93,"tags":95,"search_phrases":98,"slug":101,"view_count":35,"doi":8,"paper":102,"created_at":109},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",82,{"impact":18,"substance":90,"depth":18,"authority":91,"freshness":12,"relevant":21,"comment":92},22,14,"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。",[94],{"name":87,"url":85},[26,66,28,96,97],"机器视觉","设施番茄",[99,100],"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":8,"openalex_id":8,"authors":103,"venue":8,"cited_by_count":35,"oa_url":8,"card":104,"direction":79,"ingested_from":44},[],{"tldr":105,"method":106,"finding":107,"direction":79,"opportunity":108},"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 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时代自主农机核心问题——如何在复杂动态田间条件下将异构环境信息转化为可靠自适应可扩展的机器自主性，系统综述自主农机的技术基础、应用领域、性能优势与采用约束，并构建围绕\"观测、异质性、基础设施\"的一体化框架。研究不再按机器类型分类技术，而是考察多模态感知、AI 决策、自主导航控制、精准执行、多机协同之间的耦合关系，特别关注环境不确定性、实时决策、互操作性和系统级可扩展性的挑战。识别从孤立任务自动化向数据驱动自适应网络化农业自主性的渐进过渡，强调个体组件改进未必带来系统级性能提升，除非具备兼容的计算、通信、机械和制度基础设施。","J-STAGE \u002F Research Disclosure Journal","2026-09-18T00:00:00Z",78,{"impact":18,"substance":119,"depth":120,"authority":19,"freshness":121,"relevant":21,"comment":122},21,17,9,"核心期刊综述，提出观测-异质性-基础设施一体化框架，对智慧农业自主农机研究有参考价值，但属学术综述、产业影响有限。",[124],{"name":115,"url":113},[26,66,27,126,127],"自主农机","农业4.0",[129,130],"自主农机 智慧农业 一体化框架","Hongjin Li Chunjiang Gao 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Science","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1926482\u002Ffull","论文提出了一种可在边缘部署的温室串番茄采摘机器人,集成YOLOv8n-BiFPN-WIoU视觉检测模型与基于ROS的分布式控制架构。BiFPN加权特征融合结构增强了跨尺度特征表示,WIoU改进了部分遮挡和重叠目标的定位稳健性。在独立测试集上,该模型Precision达88.534%、Recall 89.377%、F1 88.954%、mAP@0.5为92.338%、mAP@0.5:0.95为71.368%,相比基线YOLOv8n分别提升2.143、2.779、2.460、4.587和5.675个百分点。视觉检测器与ROS机器人系统集成,协调目标感知、轨道站点间运动和机械臂操作。","Frontiers in Plant Science","2026-09-14T00:00:00Z",81,{"impact":18,"substance":90,"depth":18,"authority":91,"freshness":121,"relevant":21,"comment":149},"方法改进与实测指标扎实、面向边缘部署的温室串番茄采摘机器人研究，对智慧农业具参考价值，但属细分技术进展，未达产业级突破。",[151],{"name":145,"url":143},[26,66,153,154,28],"边缘计算","串番茄",[156,157],"农业人工智能 采摘机器人 智慧农业 边缘计算","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边缘计算-2852","10.3389\u002Ffpls.2026.1926482\u002Ffull",{"doi":159,"openalex_id":8,"authors":161,"venue":8,"cited_by_count":35,"oa_url":8,"card":162,"direction":42,"ingested_from":44},[],{"tldr":163,"method":164,"finding":165,"direction":42,"opportunity":166},"提出边缘部署的温室串番茄采摘机器人，融合改进YOLOv8n检测与ROS分布式控制。","YOLOv8n结合BiFPN加权特征融合与WIoU损失，集成ROS分布式控制架构","模型mAP@0.5达92.338%，较基线YOLOv8n提升4.587个百分点，可完成采摘。","可探索轻量化模型在更多边缘设备上的泛化部署，及多机器人协同采摘调度优化。","2026-09-18T00:03:30.570025Z",{"id":169,"title":170,"url":171,"summary":172,"summary_zh":173,"content":8,"source_name":174,"source_url":171,"published_at":146,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":147,"score_detail":175,"sources":177,"tags":179,"search_phrases":181,"slug":183,"view_count":35,"doi":184,"paper":185,"created_at":209},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。","Biosystems Engineering",{"impact":18,"substance":90,"depth":18,"authority":91,"freshness":121,"relevant":21,"comment":176},"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。",[178],{"name":174,"url":171},[26,66,28,180,96],"荔枝",[182,157],"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智慧农业机器视觉-2506","10.1016\u002Fj.biosystemseng.2026.104586",{"doi":184,"openalex_id":186,"authors":187,"venue":174,"cited_by_count":35,"oa_url":171,"card":203,"direction":79,"ingested_from":208},"W7213074904",[188,191,193,196,198,200],{"name":189,"orcid":190},"Yukun Qian","https:\u002F\u002Forcid.org\u002F0000-0001-9095-4024",{"name":192,"orcid":8},"Wenchang Chai",{"name":194,"orcid":195},"Haitao Wang","https:\u002F\u002Forcid.org\u002F0000-0002-8394-6410",{"name":197,"orcid":8},"Zhiyang Mai",{"name":199,"orcid":8},"Liangliang Zhou",{"name":201,"orcid":202},"Hejun Wu","https:\u002F\u002Forcid.org\u002F0000-0001-9758-5698",{"tldr":204,"method":205,"finding":206,"direction":79,"opportunity":207},"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%。","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",{"id":211,"title":212,"url":213,"summary":214,"summary_zh":8,"content":8,"source_name":215,"source_url":8,"published_at":216,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":88,"score_detail":217,"sources":219,"tags":221,"search_phrases":223,"slug":226,"view_count":35,"doi":8,"paper":227,"created_at":234},2397,"System-Level Smart Robotic Harvesting for High-Value Greenhouse Crops: A Review","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1795","Agronomy 2026, 16(18):1795（2026-09-13）。江苏大学Junyi Wang等综述188项主要研究，以'具身智能'为分析视角连接感知、可采性评估、决策、操控、反馈、故障传播与恢复。研究表明系统集成是主要瓶颈：可观测性不完整、场景状态短、接触不确定性、结果验证弱导致完整任务成功率、有效下降效。提出六维生产相关评价体系。","MDPI Agronomy | 2026-09-13","2026-09-13T00:00:00Z",{"impact":18,"substance":90,"depth":18,"authority":91,"freshness":12,"relevant":21,"comment":218},"江苏大学团队基于188项研究的系统级综述，指出系统集成是可观测性、接触不确定性与故障恢复等瓶颈，并提出六维生产评价体系，对温室智能采摘研发具有较高参考价值。",[220],{"name":215,"url":213},[26,67,68,28,222],"温室种植",[224,225],"农业机器人 采摘机器人 具身智能 智慧农业","农业机器人 采摘机器人","农业机器人采摘机器人具身智能智慧农业-2397",{"doi":8,"openalex_id":8,"authors":228,"venue":8,"cited_by_count":35,"oa_url":8,"card":229,"direction":79,"ingested_from":44},[],{"tldr":230,"method":231,"finding":232,"direction":79,"opportunity":233},"综述188项研究，从具身智能视角分析温室高价值作物智能采收的系统集成瓶颈。","综述188项研究，以具身智能视角串联感知、可采性评估、决策、操控与反馈。","系统集成是主要瓶颈，可观测性不完整与接触不确定性导致完整任务成功率下降。","可探索面向温室采收的具身智能系统集成与六维生产评价体系，弥补任务级成功率验证空白。","2026-09-14T00:06:33.089876Z"]