[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3249":3,"related-3249":44},{"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":21,"tags":23,"search_phrases":29,"slug":32,"view_count":33,"doi":8,"paper":34,"created_at":43},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。",null,"智慧农业(中英文)·江苏大学","2026-09-22T00:00:00Z","论文",10,false,82,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":12,"relevant":19,"comment":20},18,22,14,1,"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。",[22],{"name":9,"url":6},[24,25,26,27,28],"智慧农业","农业人工智能","采摘机器人","机器视觉","设施番茄",[30,31],"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",0,{"doi":8,"openalex_id":8,"authors":35,"venue":8,"cited_by_count":33,"oa_url":8,"card":36,"direction":40,"ingested_from":42},[],{"tldr":37,"method":38,"finding":39,"direction":40,"opportunity":41},"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。","农业人工智能与决策模型","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",{"total":45,"page":19,"page_size":45,"items":46},6,[47,93,137,163,216,255],{"id":48,"title":49,"url":50,"summary":51,"summary_zh":52,"content":8,"source_name":53,"source_url":50,"published_at":54,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":55,"score_detail":56,"sources":59,"tags":61,"search_phrases":63,"slug":66,"view_count":33,"doi":67,"paper":68,"created_at":92},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","2026-09-14T00:00:00Z",81,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":57,"relevant":19,"comment":58},9,"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。",[60],{"name":53,"url":50},[24,25,26,62,27],"荔枝",[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":53,"cited_by_count":33,"oa_url":50,"card":86,"direction":40,"ingested_from":91},"W7213074904",[71,74,76,79,81,83],{"name":72,"orcid":73},"Yukun Qian","https:\u002F\u002Forcid.org\u002F0000-0001-9095-4024",{"name":75,"orcid":8},"Wenchang Chai",{"name":77,"orcid":78},"Haitao Wang","https:\u002F\u002Forcid.org\u002F0000-0002-8394-6410",{"name":80,"orcid":8},"Zhiyang Mai",{"name":82,"orcid":8},"Liangliang Zhou",{"name":84,"orcid":85},"Hejun Wu","https:\u002F\u002Forcid.org\u002F0000-0001-9758-5698",{"tldr":87,"method":88,"finding":89,"direction":40,"opportunity":90},"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":94,"title":95,"url":96,"summary":97,"summary_zh":98,"content":8,"source_name":99,"source_url":96,"published_at":100,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":101,"score_detail":102,"sources":106,"tags":108,"search_phrases":110,"slug":111,"view_count":33,"doi":112,"paper":113,"created_at":136},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":16,"substance":103,"depth":16,"authority":18,"freshness":104,"relevant":19,"comment":105},20,8,"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。",[107],{"name":99,"url":96},[24,25,26,109,27],"花卉产业",[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":112,"openalex_id":114,"authors":115,"venue":99,"cited_by_count":33,"oa_url":96,"card":131,"direction":40,"ingested_from":91},"W7212108390",[116,119,122,125,127,129],{"name":117,"orcid":118},"Jiao Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-6947-6002",{"name":120,"orcid":121},"Dingxuan Zhao","https:\u002F\u002Forcid.org\u002F0000-0002-3372-9477",{"name":123,"orcid":124},"Seyed Mohamad Javidan","https:\u002F\u002Forcid.org\u002F0000-0003-0447-8645",{"name":126,"orcid":8},"Yiannis Ampatzidis",{"name":128,"orcid":8},"Qiyang Li",{"name":130,"orcid":8},"Zhao Zhang",{"tldr":132,"method":133,"finding":134,"direction":40,"opportunity":135},"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":138,"title":139,"url":140,"summary":141,"summary_zh":8,"content":8,"source_name":142,"source_url":8,"published_at":54,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":55,"score_detail":143,"sources":145,"tags":147,"search_phrases":150,"slug":152,"view_count":33,"doi":153,"paper":154,"created_at":162},2852,"面向边缘部署的温室串番茄采摘机器人:YOLOv8n-BiFPN-WIoU+ROS分布式控制——Frontiers in Plant 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",{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":57,"relevant":19,"comment":144},"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。",[146],{"name":142,"url":140},[24,25,148,149,26],"边缘计算","串番茄",[151,65],"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智慧农业边缘计算-2852","10.3389\u002Ffpls.2026.1926482\u002Ffull",{"doi":153,"openalex_id":8,"authors":155,"venue":8,"cited_by_count":33,"oa_url":8,"card":156,"direction":160,"ingested_from":42},[],{"tldr":157,"method":158,"finding":159,"direction":160,"opportunity":161},"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。","智慧农业 \u002F 农业物联网","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":164,"title":165,"url":166,"summary":167,"summary_zh":168,"content":8,"source_name":169,"source_url":166,"published_at":170,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":171,"score_detail":172,"sources":176,"tags":178,"search_phrases":181,"slug":184,"view_count":33,"doi":185,"paper":186,"created_at":215},2801,"A comprehensive survey on machine vision applications in precision agriculture: current trends and future perspectives","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41060-026-01278-4","A comprehensive survey on machine vision applications in precision agriculture: current trends and future perspectives。International Journal of Data Science and Analytics","精准农业中机器视觉应用的综合综述：当前趋势与未来展望。《国际数据科学与分析杂志》","International Journal of Data Science and Analytics","2026-09-17T00:00:00Z",77,{"impact":16,"substance":103,"depth":173,"authority":174,"freshness":57,"relevant":19,"comment":175},17,13,"核心期刊发表的机器视觉精准农业综述，方法梳理与趋势判断具参考价值，但属综述类论文，产业影响有限。",[177],{"name":169,"url":166},[24,25,179,180,27],"精准农业","遥感监测",[182,183],"农业人工智能 智慧农业 机器视觉 精准农业","农业人工智能 智慧农业","农业人工智能智慧农业机器视觉精准农业-2801","10.1007\u002Fs41060-026-01278-4",{"doi":185,"openalex_id":187,"authors":188,"venue":169,"cited_by_count":33,"oa_url":8,"card":210,"direction":40,"ingested_from":91},"W7213471057",[189,191,193,195,198,200,202,205,208],{"name":190,"orcid":8},"Shirun Gu",{"name":192,"orcid":8},"Xinyuan Fan",{"name":194,"orcid":8},"Lihui Zhu",{"name":196,"orcid":197},"Caixia Song","https:\u002F\u002Forcid.org\u002F0000-0003-3897-7629",{"name":199,"orcid":8},"Lei Mu",{"name":201,"orcid":8},"Zichen Zhang",{"name":203,"orcid":204},"Rui Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-8634-3519",{"name":206,"orcid":207},"Tong Xu","https:\u002F\u002Forcid.org\u002F0000-0001-5564-192X",{"name":209,"orcid":8},"Zhiyuan Zhang",{"tldr":211,"method":212,"finding":213,"direction":40,"opportunity":214},"综述机器视觉在精准农业中的应用现状与未来趋势。","文献综述，梳理机器视觉在精准农业中的技术路线。","机器视觉已广泛用于作物监测、病虫害识别等，但落地仍受数据与算力限制。","可聚焦轻量化模型与边缘部署，解决田间实时性与数据稀缺问题。","2026-09-17T23:30:54.103781Z",{"id":217,"title":218,"url":219,"summary":220,"summary_zh":8,"content":8,"source_name":221,"source_url":219,"published_at":222,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":223,"score_detail":224,"sources":228,"tags":230,"search_phrases":233,"slug":235,"view_count":33,"doi":236,"paper":237,"created_at":254},2620,"A deep learning-enhanced vision system for precision target spraying in chinese cabbage cultivation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10444-4","A deep learning-enhanced vision system for precision target spraying in chinese cabbage cultivation。Precision Agriculture","Precision Agriculture","2026-09-16T00:00:00Z",70,{"impact":225,"substance":16,"depth":226,"authority":18,"freshness":12,"relevant":19,"comment":227},12,16,"核心期刊论文，方法新颖且时效性强，但属细分作物技术进展，产业影响范围有限。",[229],{"name":221,"url":219},[24,25,231,27,232],"精准施药","大白菜",[234,183],"农业人工智能 智慧农业 机器视觉 精准施药","农业人工智能智慧农业机器视觉精准施药-2620","10.1007\u002Fs11119-026-10444-4",{"doi":236,"openalex_id":238,"authors":239,"venue":221,"cited_by_count":33,"oa_url":8,"card":8,"direction":8,"ingested_from":91},"W7213275445",[240,242,244,246,249,252],{"name":241,"orcid":8},"Changxi Liu",{"name":243,"orcid":8},"Hang Shi",{"name":245,"orcid":8},"Hao Sun",{"name":247,"orcid":248},"Hui Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-8843-7298",{"name":250,"orcid":251},"Qingda Li","https:\u002F\u002Forcid.org\u002F0000-0002-9046-2094",{"name":253,"orcid":8},"Jun Hu","2026-09-16T23:30:03.326664Z",{"id":256,"title":257,"url":258,"summary":259,"summary_zh":260,"content":8,"source_name":261,"source_url":258,"published_at":262,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":263,"score_detail":264,"sources":266,"tags":268,"search_phrases":271,"slug":273,"view_count":33,"doi":274,"paper":275,"created_at":305},2288,"Field-based deep learning classification of cotton and weeds for machine-vision-assisted intra-row weed management","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102567","Timely and precise weed management is essential for reducing crop-weed competition, labour requirements, and unnecessary weed-control inputs in cotton production. Reliable discrimination between cotton plants and weeds is a prerequisite for automated intra-row weed management. This study evaluated two pretrained convolutional neural network models, MobileNetV2 and DenseNet169, for binary classification of cotton and weeds using field-acquired RGB images. Images were collected from a farmer-managed cotton field in Haryana and an experimental cotton field at ICAR-Indian Agricultural Research Institute, New Delhi, during May-September 2025 under natural field conditions. The dataset comprised 2,195 weed images representing six predominant weed species and 1,211 cotton images. Images were classified into two operational classes, cotton and weed. Training images were augmented to address the original class imbalance, whereas validation and test images were retained without augmentation. Transfer learning was used to fine-tune both models, and performance was evaluated using accuracy, precision, recall, F1-score, confusion matrices, and inference time. Both models achieved high classification performance. DenseNet169 attained a test accuracy of 99.47%, compared with 98.27% for MobileNetV2. The results indicate that pretrained CNNs can provide accurate image-level discrimination between cotton and weed vegetation under the investigated field conditions. DenseNet169 therefore shows promise as a visual-perception component for machine-vision-assisted intra-row weed management. However, the present study is limited to image-level classification and does not demonstrate plant localization, continuous machine operation, actuator coordination, or field-scale weed removal. Further validation under independent locations, varying weed densities, illumination conditions, machine travel speeds, and complete perception-decision-actuation pipelines is required.","及时、精准的杂草管理对于减少棉花生产中的棉草竞争、劳动力需求和不必要的除草投入至关重要。可靠地区分棉花植株与杂草是实现行内自动化杂草管理的前提。本研究评估了两种预训练卷积神经网络模型——MobileNetV2和DenseNet169，利用田间采集的RGB图像对棉花与杂草进行二分类。图像于2025年5月至9月期间，在自然田间条件下，采集自哈里亚纳邦一处农民管理的棉田以及新德里ICAR-印度农业研究所的实验棉田。数据集包含2，195幅杂草图像，涵盖六种主要杂草种类，以及1，211幅棉花图像。图像被分为棉花和杂草两个操作类别。训练图像经过增强处理以解决原始类别不平衡问题，而验证和测试图像则保留未增强状态。采用迁移学习对两种模型进行微调，并使用准确率、精确率、召回率、F1分数、混淆矩阵和推理时间评估性能。两种模型均取得了较高的分类性能。DenseNet169的测试准确率达到99.47%，而MobileNetV2为98.27%。结果表明，预训练卷积神经网络能够在所研究的田间条件下实现棉花与杂草植被在图像层面的准确区分。因此，DenseNet169有望作为机器视觉辅助行内杂草管理的视觉感知组件。然而，本研究仅限于图像层面的分类，并未展示植株定位、机器连续作业、执行器协调或田间规模除草。仍需在独立地点、不同杂草密度、光照条件、机器行进速度以及完整的感知-决策-执行流程下进行进一步验证。","Smart Agricultural Technology","2026-09-11T00:00:00Z",67,{"impact":225,"substance":16,"depth":226,"authority":174,"freshness":104,"relevant":19,"comment":265},"基于田间RGB图像的棉花与杂草深度学习分类研究，DenseNet169测试准确率达99.47%，方法扎实但仅限图像级分类，尚缺定位与执行环节验证，属细分领域技术进展。",[267],{"name":261,"url":258},[24,25,269,270,27],"棉花","杂草识别",[272,183],"农业人工智能 智慧农业 机器视觉 杂草识别","农业人工智能智慧农业机器视觉杂草识别-2288","10.1016\u002Fj.atech.2026.102567",{"doi":274,"openalex_id":276,"authors":277,"venue":261,"cited_by_count":33,"oa_url":258,"card":300,"direction":40,"ingested_from":91},"W7212315230",[278,280,283,286,289,291,294,296,298],{"name":279,"orcid":8},"Shaik Nasreen",{"name":281,"orcid":282},"Roaf Ahmad Parray","https:\u002F\u002Forcid.org\u002F0000-0002-8303-1990",{"name":284,"orcid":285},"Parveen Dhanger","https:\u002F\u002Forcid.org\u002F0000-0001-5250-0275",{"name":287,"orcid":288},"Rishi Raj","https:\u002F\u002Forcid.org\u002F0009-0002-7918-1141",{"name":290,"orcid":8},"Tapan Kumar Khura",{"name":292,"orcid":293},"P. Sahoo","https:\u002F\u002Forcid.org\u002F0000-0002-3888-8506",{"name":295,"orcid":8},"Tushar Dhar",{"name":297,"orcid":8},"Prajwal R",{"name":299,"orcid":8},"Sripriyanka S. Nalla",{"tldr":301,"method":302,"finding":303,"direction":40,"opportunity":304},"用MobileNetV2和DenseNet169对田间棉花与杂草图像做二分类，验证机器视觉除草可行性","迁移学习微调两种预训练CNN，使用2025年田间RGB图像共3406张。","DenseNet169测试准确率达99.47%，优于MobileNetV2的98.27%。","可延伸至植株定位、多光照与密度条件下的感知-决策-执行全流程田间验证。","2026-09-13T23:30:04.183782Z"]