[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3808":3,"related-3808":86},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":61,"slug":64,"view_count":65,"doi":66,"paper":67,"created_at":85},3808,"Orchard worker detection using spatial-context attention on 2D LiDAR data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104604","Orchard worker detection using spatial-context attention on 2D LiDAR data。Biosystems Engineering",null,"Biosystems Engineering","2026-09-30T00:00:00Z","论文",10,false,79,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":12,"relevant":20,"comment":21},16,21,18,14,1,"提出基于二维激光雷达与空间上下文注意力的果园工人检测方法，属农业人工智能与智能农机细分领域的方法创新，具备一定专业参考价值。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","目标检测","果园管理","激光雷达",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,50,56],{"code":31,"score":44,"supported":45,"title":46,"abstract":48,"tags":49},11,true,[47],"orchard",[47],[],{"code":51,"score":52,"supported":13,"title":53,"abstract":54,"tags":55},"I2",2,[],[],[26,27],{"code":57,"score":20,"supported":13,"title":58,"abstract":59,"tags":60},"I1",[],[],[25],[62,63],"2D LiDAR 果园 工人检测","农业人工智能 智慧农业 果园管理 激光雷达","2DLiDAR果园工人检测-3808",3,"10.1016\u002Fj.biosystemseng.2026.104604",{"doi":66,"openalex_id":68,"authors":69,"venue":9,"cited_by_count":83,"oa_url":6,"card":8,"direction":8,"ingested_from":84},"W7214801786",[70,72,74,77,80],{"name":71,"orcid":8},"Jintack Jeon",{"name":73,"orcid":8},"Huijong Chung",{"name":75,"orcid":76},"Gookhwan Kim","https:\u002F\u002Forcid.org\u002F0000-0002-7278-3476",{"name":78,"orcid":79},"Yong–Joo Kim","https:\u002F\u002Forcid.org\u002F0000-0002-1212-9018",{"name":81,"orcid":82},"Dae-Hyun Lee","https:\u002F\u002Forcid.org\u002F0000-0001-9544-5974",0,"openalex","2026-09-30T23:30:05.310491Z",{"total":87,"page":20,"page_size":87,"items":88},6,[89,140,196,258,301,370],{"id":90,"title":91,"url":92,"summary":93,"summary_zh":8,"content":8,"source_name":94,"source_url":8,"published_at":95,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":96,"score_detail":97,"sources":102,"tags":104,"primary_topic":107,"secondary_topics":108,"classification":109,"search_phrases":125,"slug":128,"view_count":129,"doi":8,"paper":130,"created_at":139},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",80,{"impact":18,"substance":98,"depth":18,"authority":99,"freshness":100,"relevant":20,"comment":101},22,13,9,"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。",[103],{"name":94,"url":92},[25,26,105,106,28],"农业机器人","强化学习",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":110},{"candidates":111,"agricultural_evidence":45},[112,117,121],{"code":31,"score":65,"supported":45,"title":113,"abstract":114,"tags":116},[],[115],"苹果",[],{"code":51,"score":52,"supported":13,"title":118,"abstract":119,"tags":120},[],[],[26,106],{"code":57,"score":20,"supported":13,"title":122,"abstract":123,"tags":124},[],[],[25],[126,127],"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",5,{"doi":8,"openalex_id":8,"authors":131,"venue":8,"cited_by_count":83,"oa_url":8,"card":132,"direction":136,"ingested_from":138},[],{"tldr":133,"method":134,"finding":135,"direction":136,"opportunity":137},"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。","农业人工智能与决策模型","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",{"id":141,"title":142,"url":143,"summary":144,"summary_zh":8,"content":8,"source_name":145,"source_url":8,"published_at":146,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":96,"score_detail":147,"sources":151,"tags":153,"primary_topic":156,"secondary_topics":157,"classification":158,"search_phrases":184,"slug":187,"view_count":65,"doi":8,"paper":188,"created_at":195},2490,"Lightweight Detection of Blueberries at Different Maturity Stages in Complex Orchard Environments(YOLOv12n+SHSA2C2f+知识蒸馏)","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F18\u002F1949","云南农业大学大数据学院联合云南省农业大数据工程技术研究中心 2026 年 9 月 10 日在《Agriculture》发表。针对小果、密集、遮挡、色彩过渡细微等复杂田间条件下的蓝莓成熟期检测难题,构建 4680 图像 60573 标注框田间数据集,重新设计 YOLOv12n 的 P3\u002FP4\u002FP5 检测结构为 P2\u002FP3\u002FP4 架构,引入轻量化 SHSA2C2f 单头自注意力特征增强模块,DINOv3 (ViT-S\u002F16) 引导的非对称知识蒸馏。精度导向 M4 模型 mAP@0.5 达 92.68%±0.04%、mAP@0.5:0.95 86.38%±0.10%,参数量仅 0.79 M;经 TensorRT FP16 转换后保留 92.75% mAP@0.5,可在 Jetson Orin Nano 上以 15.9 FPS 运行。","MDPI Agriculture 2026-09-10","2026-09-09T16:00:00Z",{"impact":18,"substance":148,"depth":18,"authority":99,"freshness":149,"relevant":20,"comment":150},23,8,"面向复杂果园的蓝莓成熟期轻量化检测研究，方法新颖、数据规模扎实且给出边缘部署实测性能，具备较高专业参考价值。",[152],{"name":145,"url":143},[25,26,154,27,155],"边缘计算","蓝莓",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":159},{"candidates":160,"agricultural_evidence":45},[161,165,171,175,179],{"code":31,"score":100,"supported":45,"title":162,"abstract":163,"tags":164},[47],[],[155],{"code":166,"score":65,"supported":45,"title":167,"abstract":168,"tags":170},"I6",[],[169],"农业大数据",[],{"code":51,"score":52,"supported":13,"title":172,"abstract":173,"tags":174},[],[],[26,27],{"code":57,"score":20,"supported":13,"title":176,"abstract":177,"tags":178},[],[],[25],{"code":180,"score":20,"supported":13,"title":181,"abstract":182,"tags":183},"I5",[],[],[154],[185,186],"农业人工智能 智慧农业 目标检测 边缘计算","农业人工智能 智慧农业","农业人工智能智慧农业目标检测边缘计算-2490",{"doi":8,"openalex_id":8,"authors":189,"venue":8,"cited_by_count":83,"oa_url":8,"card":190,"direction":136,"ingested_from":138},[],{"tldr":191,"method":192,"finding":193,"direction":136,"opportunity":194},"提出轻量YOLOv12n改进模型，实现复杂果园中多成熟期蓝莓的实时检测。","4680张田间图像数据集，P2\u002FP3\u002FP4结构、SHSA2C2f模块与DINOv","M4模型mAP@0.5达92.68%，仅0.79M参数，Jetson Orin Nano上15.9 ","可探索多作物通用轻量检测框架，或结合成熟度计数用于产量预测与采摘决策。","2026-09-15T00:04:27.061523Z",{"id":197,"title":198,"url":199,"summary":200,"summary_zh":201,"content":8,"source_name":202,"source_url":199,"published_at":203,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":204,"sources":208,"tags":210,"primary_topic":213,"secondary_topics":214,"classification":215,"search_phrases":237,"slug":240,"view_count":65,"doi":241,"paper":242,"created_at":257},1382,"Instance Segmentation and Multi-Object Tracking for Fruit Quality Grading and Dynamic Yield Estimation in Egyptian Citrus Orchards","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102529","Egypt ranks among the world’s leading citrus producers and exporters; however, orchard operations remain constrained by subjective, labor-intensive manual quality grading and time-consuming yield estimation that require specialized expertise. This study presents an AI-driven orchard-intelligence framework for automated fruit quality grading and dynamic yield estimation under the unstructured conditions of Egyptian orange farms. To address the lack of in-field public datasets, a domain-specific instance-segmentation dataset of 618 original pixel-level annotated images, expanded to 1,545 augmented training images, and 15 videos was constructed. This dataset was used to benchmark single-stage segmentation models (YOLOv8, YOLOv11) against a two-stage (Mask R-CNN), alongside a transformer-based bounding-box detection (RT-DETR), under dense canopy occlusion, clustered fruit instances, and variable natural illumination. Each fine-tuned model was evaluated across two stages: static quality grading and dynamic yield estimation. For static grading (Good vs. Defective), YOLOv11l ranked among the top tier of evaluated segmenters, reaching mean Average Precision (mAP) of 0.932 with balanced precision-recall behavior, while maintaining real-time feasibility at 26.1 ms per image. For dynamic yield estimation, a multi-object tracking pipeline was implemented using BoT-SORT with camera-motion compensation and a virtual line-crossing counting mechanism. The evaluation reveals a three-way dissociation: the highest-mAP static segmenter (YOLOv11l), the best dynamic total counter (RT-DETR, WMAPE = 5.66%, despite the lowest box-level static accuracy), and the best quality-resolved dynamic grader (YOLOv11s, WMAPE = 6.97% at 14.2 ms per frame) are three different systems; RT-DETR did not resolve fruit quality reliably under motion. Counting accuracy and grading capability are thus separable properties requiring separate evaluation. Overall, the framework enables objective fruit quality assessment and actionable in-field fruit counting to support harvest logistics and labor planning, providing a practical foundation for smart agriculture in the Egyptian citrus sector.","埃及位居全球领先的柑橘生产国和出口国之列；然而，果园作业仍受制于主观性强、劳动密集的人工质量分级以及耗时且需专业技能的产量估算。本研究提出了一种人工智能驱动的果园智能框架，用于在埃及橙园的非结构化条件下实现自动化果实质量分级和动态产量估算。为解决田间公开数据集缺乏的问题，构建了一个领域特定的实例分割数据集，包含618张原始像素级标注图像，扩充至1545张增强训练图像，以及15段视频。该数据集用于在密集树冠遮挡、果实簇生和自然光照多变条件下，对单阶段分割模型（YOLOv8、YOLOv11）与两阶段模型（Mask R-CNN）进行基准测试，同时对比基于Transformer的边界框检测模型（RT-DETR）。每个微调模型均经过两个阶段的评估：静态质量分级和动态产量估算。在静态分级（优质与缺陷）中，YOLOv11l在评估的分割模型中位居前列，平均精度（mAP）达到0.932，精确率与召回率表现均衡，同时以每张图像26.1毫秒的处理速度保持实时可行性。在动态产量估算方面，采用基于BoT-SORT的多目标跟踪流程，结合相机运动补偿和虚拟线跨越计数机制。评估结果揭示了三重分离现象：静态分割精度最高的模型（YOLOv11l）、动态总数计数最优的模型（RT-DETR，加权平均绝对百分比误差WMAPE = 5.66%，尽管其框级静态精度最低），以及质量分辨动态分级最佳的模型（YOLOv11s，每帧14.2毫秒时WMAPE = 6.97%）是三个不同的系统；RT-DETR在运动条件下无法可靠分辨果实质量。因此，计数精度和分级能力是可分离的属性，需要分别评估。总体而言，该框架实现了客观的果实质量评估和可操作的田间果实计数，以支持采收物流和劳动力规划，为埃及柑橘产业的智慧农业提供了实用基础。","Smart Agricultural Technology","2026-09-01T00:00:00Z",{"impact":18,"substance":98,"depth":205,"authority":99,"freshness":206,"relevant":20,"comment":207},19,7,"针对埃及柑橘园提出AI驱动的品质分级与动态产量估算框架，方法新颖且数据详实，对智慧农业有参考价值。",[209],{"name":202,"url":199},[25,26,27,211,212],"柑橘","产量估算",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":216},{"candidates":217,"agricultural_evidence":45},[218,223,228,232],{"code":31,"score":18,"supported":45,"title":219,"abstract":221,"tags":222},[220],"citrus",[211,47,220],[211],{"code":57,"score":206,"supported":45,"title":224,"abstract":225,"tags":227},[],[25,226],"smart agriculture",[25],{"code":51,"score":52,"supported":13,"title":229,"abstract":230,"tags":231},[],[],[26,27],{"code":233,"score":20,"supported":13,"title":234,"abstract":235,"tags":236},"I3",[],[],[212],[238,239],"农业人工智能 产量估算 智慧农业 目标检测","农业人工智能 产量估算","农业人工智能产量估算智慧农业目标检测-1382","10.1016\u002Fj.atech.2026.102529",{"doi":241,"openalex_id":243,"authors":244,"venue":202,"cited_by_count":83,"oa_url":199,"card":251,"direction":255,"ingested_from":84},"W7204959516",[245,247,249],{"name":246,"orcid":8},"Saleh A. Farhoud",{"name":248,"orcid":8},"Yasmine A. Zaghloul",{"name":250,"orcid":8},"Omar M. Shehata",{"tldr":252,"method":253,"finding":254,"direction":255,"opportunity":256},"构建柑橘果园AI框架，实现果实质量分级与动态产量估计，并评估多种分割与跟踪模型。","构建实例分割数据集，微调YOLOv8\u002Fv11、Mask R-CNN、RT-DET","静态分级最优为YOLOv11l，动态计数最优为RT-DETR，质量动态分级最优为YOLOv11s，计","智慧农业 \u002F 农业物联网","可探索多任务协同优化，如设计统一模型同时满足静态分级与动态计数，或引入时序信息提升运动场景下的质量分级鲁棒性。","2026-09-02T23:30:04.506009Z",{"id":259,"title":260,"url":261,"summary":262,"summary_zh":8,"content":263,"source_name":264,"source_url":8,"published_at":265,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":266,"score_detail":267,"sources":273,"tags":275,"primary_topic":277,"secondary_topics":278,"classification":279,"search_phrases":295,"slug":297,"view_count":65,"doi":8,"paper":298,"created_at":300},1188,"A Feature Enhancement Framework for Joint Mango Fruit and Stem Detection in Complex Orchard Environments (复杂果园环境中芒果果实与果柄联合检测的特征增强框架)","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888","MDPI Agriculture 2026, 16(17), 1888, 2026年8月31日发表,作者来自华南农业大学工程学院和农业装备技术国家重点实验室。研究提出MangoNET,基于YOLOv11n的芒果果实与果柄联合检测框架,适用于复杂果园环境。P2高分辨率检测头保留小目标的精细空间信息,SPPF-ELAN聚合局部和上下文特征用于部分可见对象,SENet在光照变化下重新校准通道响应,WIoU v3调节具有不同定位质量的边界框样本。数据集包含1782张台农和金煌芒果原始图像,仅对训练集应用数据增强,将训练集大小从1172张增加到2886张图像。MangoNET果实和果柄F1分数分别为0.920和0.916,mAP50和mAP50-95值为0.941和0.690。","Next Article in Journal\n\n[Straw Biochar Incorporation Reduces CH 4 Emissions in Double-Cropping Rice Paddies and Partially Alleviates Warming-Induced Yield Losses](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1889)\n\n Previous Article in Journal\n\n[Tracking Insects in Controlled Experiments](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1887)\n\n Previous Article in Special Issue\n\n[ShuffleNetV2-hSimKD: A Lightweight Network for Plant Disease Detection](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F15\u002F1686)\n\n## Journals\n\n[Active Journals](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fjournals)[Find a Journal](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fjournalfinder)[Journal Proposal](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fjournals\u002Fproposal)[Proceedings Series](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fproceedings)\n\n## [Topics](https:\u002F\u002Fwww.mdpi.com\u002Ftopics)\n\n## Information\n\n[For Authors](https:\u002F\u002Fwww.mdpi.com\u002Fauthors)[For Reviewers](https:\u002F\u002Fwww.mdpi.com\u002Freviewers)[For Editors](https:\u002F\u002Fwww.mdpi.com\u002Feditors)[For Librarians](https:\u002F\u002Fwww.mdpi.com\u002Flibrarians)[For Publishers](https:\u002F\u002Fwww.mdpi.com\u002Fpublishing_services)[For Societies](https:\u002F\u002Fwww.mdpi.com\u002Fsocieties)[For Conference Organizers](https:\u002F\u002Fwww.mdpi.com\u002Fconference_organizers)\n\n[Open Access Policy](https:\u002F\u002Fwww.mdpi.com\u002Fopenaccess)[Institutional Open Access Program](https:\u002F\u002Fwww.mdpi.com\u002Fioap)[Special Issues Guidelines](https:\u002F\u002Fwww.mdpi.com\u002Fspecial_issues_guidelines)[Editorial Process](https:\u002F\u002Fwww.mdpi.com\u002Feditorial_process)[Research and Publication Ethics](https:\u002F\u002Fwww.mdpi.com\u002Fethics)[Article Processing Charge](https:\u002F\u002Fwww.mdpi.com\u002Fapc)[Awards](https:\u002F\u002Fwww.mdpi.com\u002Fawards)[Testimonials](https:\u002F\u002Fwww.mdpi.com\u002Ftestimonials)\n\n## [Author Services](https:\u002F\u002Fwww.mdpi.com\u002Fauthors\u002Fservices)\n\n## Initiatives\n\n[Sciforum](https:\u002F\u002Fsciforum.net\u002F)[MDPI Books](https:\u002F\u002Fwww.mdpi.com\u002Fbooks)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002F)[Scilit](https:\u002F\u002Fwww.scilit.com\u002F)[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002F)[Encyclopedia](https:\u002F\u002Fencyclopedia.pub\u002F)[JAMS](https:\u002F\u002Fjams.pub\u002F)[Proceedings Series](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fproceedings)\n\n## About\n\n[Overview](https:\u002F\u002Fwww.mdpi.com\u002Fabout)[Contact](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fcontact)[Careers](https:\u002F\u002Fcareers.mdpi.com\u002F)[News](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fannouncements)[Press](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fpress)[Blog](http:\u002F\u002Fblog.mdpi.com\u002F)\n\n[Sign In \u002F Sign Up](https:\u002F\u002Fwww.mdpi.com\u002Fuser\u002Flogin)\n\n## Notice\n\n You can make submissions to other journals [here](https:\u002F\u002Fsusy.mdpi.com\u002Fuser\u002Fmanuscripts\u002Fupload). \n\n[_clear_](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888)\n\n## Notice\n\nYou are accessing a machine-readable page. 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[Abstract](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888#)\n\nAltmetric[_share_ Share](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888# \"Share\")[_announcement_ Help](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888# \"Help\")[_format\\_quote_ Cite](javascript:void(0);)\n\n## Need Help?\n\n### Support\n\nFind support for a specific problem in the support section of our website.\n\n[Get Support](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fcontactform)\n\n### Feedback\n\nPlease let us know what you think of our products and services.\n\n[Give Feedback](https:\u002F\u002Fwww.mdpi.com\u002Ffeedback\u002Fsend)\n\n### Information\n\nVisit our dedicated information section to learn more about MDPI.\n\n[Get Information](https:\u002F\u002Fwww.mdpi.com\u002Fauthors)\n\n[_clear_](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888)\n\n## JSmol 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Lu\n\n[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002F3614159?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[Scilit](https:\u002F\u002Fscilit.com\u002Fscholars?q=Jiahuan%20Lu)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002Fsearch?condition_blocks=[{%22value%22:%22Jiahuan+Lu%22,%22type%22:%22author%22,%22operator%22:null}]&sort_field=relevance&sort_dir=desc&page=1&exact_match=true)[Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Jiahuan+Lu)\n\n 1,2[](mailto:jhlu@scau.edu.cn)[![Image 10: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788181901)](https:\u002F\u002Forcid.org\u002F0009-0004-3223-6453), \n\n Qihan Deng\n\n![Image 11](https:\u002F\u002Fwww.mdpi.com\u002Fbundles\u002Fmdpisciprofileslink\u002Fimg\u002Funknown-user.png)Qihan 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Li\n\n[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002F893272?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[Scilit](https:\u002F\u002Fscilit.com\u002Fscholars?q=Jiehao%20Li)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002Fsearch?condition_blocks=[{%22value%22:%22Jiehao+Li%22,%22type%22:%22author%22,%22operator%22:null}]&sort_field=relevance&sort_dir=desc&page=1&exact_match=true)[Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Jiehao+Li)\n\n 1,2,*[](mailto:jiehao.li@scau.edu.cn)[![Image 17: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788181901)](https:\u002F\u002Forcid.org\u002F0000-0002-4946-4434)\n\n1\n\nCollege of Engineering, South China Agricultural University, Guangzhou 510642, China\n\n2\n\nNational Key Laboratory of Agricultural Equipment Technology, South China Agricultural University, Guangzhou 510642, China\n\n*\n\nAuthor to whom correspondence should be addressed. \n\n_Agriculture_**2026**, _16_(17), 1888; https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16171888 (registering DOI) \n\nSubmission received: 30 July 2026 \u002F Revised: 27 August 2026 \u002F Accepted: 28 August 2026 \u002F Published: 31 August 2026\n\n (This article belongs to the Special Issue [Smart Sensor-Based Systems for Crop Monitoring](https:\u002F\u002Fwww.mdpi.com\u002Fjournal\u002Fagriculture\u002Fspecial_issues\u002F84MWNN99A6))\n\n[Download _keyboard\\_arrow\\_down_](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888)\n\n[Download PDF](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888\u002Fpdf?version=1788183021)\n\n[Download Supplementary Material](javascript:void(0);)\n\n[Versions Notes](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888\u002Fnotes)\n\n## Abstract\n\n Reliable joint detection of mango fruits and stems is an essential upstream perception task for robotic harvesting, but remains challenging because stems are small, slender, frequently occluded, and visually degraded by illumination variation. This study proposes MangoNET, a YOLOv11n-based framework for joint mango fruit and stem detection in complex orchard environments. A P2 high-resolution detection head preserves fine spatial information for small targets, while SPPF-ELAN aggregates local and contextual features for partially visible objects. SENet recalibrates channel responses under illumination variation, and WIoU v3 regulates bounding-box samples with different localization qualities. A dataset containing 1782 original images of Tainong and Jinhuang mangoes was collected from two orchards and data augmentation was applied only to the training set, increasing its size from 1172 to 2886 images through rotation, contrast adjustment, and Gaussian noise addition. MangoNET achieved fruit and stem F1-scores of 0.920 and 0.916, respectively, with mAP50 and mAP50–95 values of 0.941 and 0.690. Compared with YOLOv11n, mAP50 and mAP50–95 increased by 1.6 and 2.9 percentage points, respectively, while stem recall increased from 0.877 to 0.906. Source-image-independent five-fold cross-validation yielded mean mAP50 and mAP50–95 values of 0.944 and 0.711, respectively. Pilot evaluations using images acquired by a UAV and an RGB-D camera in a geographically distinct orchard suggested that MangoNET could maintain detection performance in a different orchard environment. MangoNET s","Agriculture 2026, 16(17), 1888","2026-08-31T08:00:00Z",69,{"impact":268,"substance":269,"depth":18,"authority":270,"freshness":271,"relevant":20,"comment":272},15,20,12,4,"提出芒果果实与果柄联合检测的特征增强框架，方法新颖，数据详实，对农业机器人采摘有实际价值。",[274],{"name":264,"url":261},[25,26,27,276],"芒果检测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":280},{"candidates":281,"agricultural_evidence":45},[282,287,291],{"code":31,"score":205,"supported":45,"title":283,"abstract":285,"tags":286},[284,47],"芒果",[284],[],{"code":51,"score":52,"supported":13,"title":288,"abstract":289,"tags":290},[],[],[26,27],{"code":57,"score":20,"supported":13,"title":292,"abstract":293,"tags":294},[],[],[25],[296,186],"农业人工智能 智慧农业 目标检测 芒果检测","农业人工智能智慧农业目标检测芒果检测-1188",{"doi":8,"openalex_id":8,"authors":299,"venue":8,"cited_by_count":83,"oa_url":8,"card":8,"direction":8,"ingested_from":138},[],"2026-09-01T00:03:40.048486Z",{"id":302,"title":303,"url":304,"summary":305,"summary_zh":306,"content":8,"source_name":307,"source_url":304,"published_at":308,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":309,"score_detail":310,"sources":313,"tags":315,"primary_topic":318,"secondary_topics":319,"classification":320,"search_phrases":344,"slug":347,"view_count":83,"doi":348,"paper":349,"created_at":369},5401,"A HIERARCHICAL DISTRIBUTED EDGE AI FRAMEWORK FOR REAL-TIME APPLE LEAF DISEASE CLASSIFICATION","https:\u002F\u002Fdoi.org\u002F10.22190\u002Ffuacr260506002g","The application of artificial intelligence (AI) in agriculture has transformed crop management, particularly in the detection of plant diseases. Apple trees, a key agricultural commodity, are vulnerable to various diseases, which can severely impact crop yields. Traditional disease detection methods, often reliant on manual inspections, are time-consuming and prone to error. In response, this study proposes a novel two-stage edge AI framework for apple leaf disease classification that integrates RGB imagery with distributed edge computing. The first stage performs binary classification to distinguish between healthy and diseased leaves, achieving an F1 score of 95.50%. This optimization is achieved by utilizing lightweight models on edge devices, thereby enhancing computational efficiency. The second stage employs a consensus-based multiclass classification approach, achieving a 96.44% F1 score in identifying specific diseases. This dual-stage system, supported by an edge AI architecture, ensures real-time disease detection while minimizing resource consumption and enhancing scalability in large-scale agricultural environments. The full system achieved a 95.50% accuracy and F1 score on the special dataset. The results demonstrate the system’s efficacy in controlled laboratory conditions, offering a promising solution for precise, real-time agricultural monitoring. This study underscores the potential of distributed edge AI systems for scalable and efficient disease detection in precision agriculture.","人工智能（AI）在农业中的应用已深刻改变了作物管理方式，尤其是在植物病害检测方面。苹果树作为重要的农业经济作物，易受多种病害侵袭，严重影响作物产量。传统的病害检测方法通常依赖人工检查，耗时且易出错。为此，本研究提出了一种新颖的两阶段边缘AI框架，用于苹果叶片病害分类，该框架将RGB图像与分布式边缘计算相结合。第一阶段执行二分类以区分健康叶片与患病叶片，F1分数达到95.50%。该优化通过在边缘设备上采用轻量级模型实现，从而提升了计算效率。第二阶段采用基于共识的多类分类方法，在识别具体病害方面F1分数达到96.44%。该双阶段系统在边缘AI架构的支持下，实现了实时病害检测，同时最大限度地降低了资源消耗，并增强了在大规模农业环境中的可扩展性。完整系统在专用数据集上达到了95.50%的准确率和F1分数。结果表明，该系统在受控实验室条件下具有有效性，为精准、实时的农业监测提供了一种有前景的解决方案。本研究凸显了分布式边缘AI系统在精准农业中实现可扩展且高效病害检测的潜力。","Facta Universitatis Series Automatic Control and Robotics","2026-10-18T00:00:00Z",76,{"impact":18,"substance":17,"depth":311,"authority":270,"freshness":149,"relevant":20,"comment":312},17,"该研究提出两级边缘AI框架，在苹果叶片病害识别上取得95%以上F1值，方法新颖、数据可靠，对精准农业实时监测有参考价值，但尚处实验室验证阶段，产业影响有限。",[314],{"name":307,"url":304},[25,26,154,316,317],"作物病害识别","苹果病害",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":321},{"candidates":322,"agricultural_evidence":45},[323,328,334,339],{"code":31,"score":18,"supported":45,"title":324,"abstract":326,"tags":327},[325],"apple",[33,115,325],[317],{"code":57,"score":206,"supported":45,"title":329,"abstract":330,"tags":333},[],[331,332],"精准农业","precision agriculture",[25],{"code":180,"score":206,"supported":45,"title":335,"abstract":336,"tags":338},[],[154,337],"edge computing",[154],{"code":51,"score":129,"supported":45,"title":340,"abstract":341,"tags":343},[],[342],"artificial intelligence",[316,26],[345,346],"苹果叶片病害 边缘AI","分布式边缘计算 病害分类","苹果叶片病害边缘AI-5401","10.22190\u002Ffuacr260506002g",{"doi":348,"openalex_id":350,"authors":351,"venue":307,"cited_by_count":83,"oa_url":304,"card":364,"direction":136,"ingested_from":84},"W7222964752",[352,355,358,361],{"name":353,"orcid":354},"Milan Grujev","https:\u002F\u002Forcid.org\u002F0009-0002-3211-6844",{"name":356,"orcid":357},"Dragan H. Stojanovic","https:\u002F\u002Forcid.org\u002F0000-0003-4893-3814",{"name":359,"orcid":360},"Miloš Ilić","https:\u002F\u002Forcid.org\u002F0000-0002-9696-2874",{"name":362,"orcid":363},"Aleksandar Milosavljević","https:\u002F\u002Forcid.org\u002F0000-0003-3057-7727",{"tldr":365,"method":366,"finding":367,"direction":255,"opportunity":368},"提出两级边缘AI框架，用RGB图像实时分类苹果叶片病害。","分布式边缘计算+轻量模型，先二分类再共识多分类。","两级系统准确率与F1均达95.50%，多分类F1为96.44%。","仅在受控实验室验证，可延伸至田间复杂光照与多作物泛化及边缘能耗优化。","2026-10-20T23:30:33.830748Z",{"id":371,"title":372,"url":373,"summary":374,"summary_zh":8,"content":8,"source_name":375,"source_url":8,"published_at":376,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":377,"score_detail":378,"sources":380,"tags":382,"primary_topic":385,"secondary_topics":386,"classification":387,"search_phrases":407,"slug":410,"view_count":83,"doi":8,"paper":411,"created_at":418},5130,"DefoliationCAM:面向葡萄藤食叶严重度实时评估的边缘AI系统 [预印本]","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.20879","Fuchen Li等提出DefoliationCAM(D-CAM),一种太阳能供电的边缘AI系统,用于估算葡萄藤树冠食叶程度并通过LoRa将结果传输至Web仪表盘。流水线使用YOLOX-Nano在4K图像中定位葡萄藤主干,并使用基于MobileNet的轻量级通道注意力分类器对320×320像素的斑块进行分类。29,674个田间采集斑块被标注为背景或四种损伤等级之一;合并中度和重度类别将类别不平衡比从约40:1降至8.8:1。合成增强使macro-F1从0.768提升至0.838。分类器以更少的参数量(1.08M vs 23.52M)在macro-F1上优于ResNet-50。利用TensorRT FP16在NVIDIA Jetson Orin Nano上每张4K图像处理时间约1.5秒,完整运行周期57-60秒。两台设备为期五周的田间部署验证了自主太阳能运行、LoRa通信和远程食叶监测的可行性。","arXiv 2610.20879 2026 年 10 月 14 日","2026-10-14T00:00:00Z",75,{"impact":16,"substance":98,"depth":18,"authority":44,"freshness":149,"relevant":20,"comment":379},"预印本提出太阳能边缘AI系统，实现葡萄藤食叶严重度实时评估与LoRa远程监测，方法新颖、数据规模大、田间验证充分，具智慧农业应用价值。",[381],{"name":375,"url":373},[25,26,154,383,384],"葡萄藤","病害评估",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":388},{"candidates":389,"agricultural_evidence":45},[390,395,399,403],{"code":31,"score":44,"supported":45,"title":391,"abstract":393,"tags":394},[392],"葡萄",[392],[],{"code":57,"score":20,"supported":13,"title":396,"abstract":397,"tags":398},[],[],[25],{"code":51,"score":20,"supported":13,"title":400,"abstract":401,"tags":402},[],[],[26],{"code":180,"score":20,"supported":13,"title":404,"abstract":405,"tags":406},[],[],[154],[408,409],"DefoliationCAM 葡萄藤 食叶","Jetson Orin Nano 边缘AI 葡萄","DefoliationCAM葡萄藤食叶-5130",{"doi":8,"openalex_id":8,"authors":412,"venue":8,"cited_by_count":83,"oa_url":8,"card":413,"direction":255,"ingested_from":138},[],{"tldr":414,"method":415,"finding":416,"direction":255,"opportunity":417},"提出太阳能边缘AI系统DefoliationCAM，实时评估葡萄藤食叶严重度并远程传输。","YOLOX-Nano定位主干，MobileNet通道注意力分类，TensorRT","轻量分类器macro-F1达0.838，优于ResNet-50，田间部署验证可行。","可探索多作物迁移、损伤等级细化及边缘模型自适应更新以提升泛化性。","2026-10-17T00:13:44.932340Z"]