[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1382":3,"related-1382":93},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":69,"slug":72,"view_count":73,"doi":74,"paper":75,"created_at":92},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。",null,"Smart Agricultural Technology","2026-09-01T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,22,19,13,7,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","目标检测","柑橘","产量估算",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":46},[45,52,58,64],{"code":33,"score":17,"supported":46,"title":47,"abstract":49,"tags":51},true,[48],"citrus",[30,50,48],"orchard",[30],{"code":53,"score":21,"supported":46,"title":54,"abstract":55,"tags":57},"I1",[],[27,56],"smart agriculture",[27],{"code":59,"score":60,"supported":14,"title":61,"abstract":62,"tags":63},"I2",2,[],[],[28,29],{"code":65,"score":22,"supported":14,"title":66,"abstract":67,"tags":68},"I3",[],[],[31],[70,71],"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",3,"10.1016\u002Fj.atech.2026.102529",{"doi":74,"openalex_id":76,"authors":77,"venue":10,"cited_by_count":84,"oa_url":6,"card":85,"direction":89,"ingested_from":91},"W7204959516",[78,80,82],{"name":79,"orcid":9},"Saleh A. Farhoud",{"name":81,"orcid":9},"Yasmine A. Zaghloul",{"name":83,"orcid":9},"Omar M. Shehata",0,{"tldr":86,"method":87,"finding":88,"direction":89,"opportunity":90},"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。","openalex","2026-09-02T23:30:04.506009Z",{"total":94,"page":22,"page_size":94,"items":95},6,[96,148,205,264,330,374],{"id":97,"title":98,"url":99,"summary":100,"summary_zh":9,"content":9,"source_name":101,"source_url":9,"published_at":102,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":103,"score_detail":104,"sources":106,"tags":108,"primary_topic":111,"secondary_topics":112,"classification":113,"search_phrases":134,"slug":137,"view_count":138,"doi":9,"paper":139,"created_at":147},3962,"华中农业大学柑橘类果实泛化识别与定位研究——YOLOv8n-Light轻量化部署","https:\u002F\u002Fnews.foodmate.net\u002F2026\u002F10\u002Fwap_754150.html","华中农业大学工学院马京奥、刘洁副教授团队研究YOLOv8n-Light模型在柑橘类果实识别中的应用，模型可同时识别橙、橘、柚三种果实，计算量较原始YOLOv8降低58%，工作站CPU推理速度提升45.9%（达15.35FPS），树莓派4B推理速度提升38.2%（达0.76FPS）；多轴机械臂采摘作业控制中，果实中心坐标X、Y、Z方向平均误差均控制在10mm内，半径误差小于6mm。312次果园采摘测试验证高兼容性与实用性。研究得到国家重点研发计划、国家柑橘产业技术体系资助。","食品伙伴网·2026年10月01日","2026-10-01T00:00:00Z",82,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":105},"高校团队提出轻量化柑橘识别模型并完成果园实测，方法新颖、数据扎实，对智能采摘装备有参考价值。",[107],{"name":101,"url":99},[27,28,109,30,110],"采摘机器人","轻量化模型",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":114},{"candidates":115,"agricultural_evidence":46},[116,121,125,130],{"code":33,"score":117,"supported":46,"title":118,"abstract":119,"tags":120},12,[30],[30],[30],{"code":59,"score":60,"supported":14,"title":122,"abstract":123,"tags":124},[],[],[28,110],{"code":126,"score":22,"supported":14,"title":127,"abstract":128,"tags":129},"F6",[],[],[109],{"code":53,"score":22,"supported":14,"title":131,"abstract":132,"tags":133},[],[],[27],[135,136],"华中农业大学 柑橘 采摘机器人","YOLOv8n-Light 柑橘识别","华中农业大学柑橘采摘机器人-3962",5,{"doi":9,"openalex_id":9,"authors":140,"venue":9,"cited_by_count":84,"oa_url":9,"card":141,"direction":89,"ingested_from":146},[],{"tldr":142,"method":143,"finding":144,"direction":89,"opportunity":145},"提出YOLOv8n-Light轻量化模型，实现橙橘柚三种柑橘果实泛化识别与定位，并部署于树莓派和采摘","改进YOLOv8n轻量化模型，工作站与树莓派4B部署，多轴机械臂采摘测试312次","计算量降58%，工作站CPU推理15.35FPS，果实中心坐标误差\u003C10mm，半径误差\u003C6mm。","可探索多品种泛化模型在边缘设备上的精度-速度权衡，及跨季节、遮挡场景下的鲁棒性提升。","agent","2026-10-02T11:53:16.558461Z",{"id":149,"title":150,"url":151,"summary":152,"summary_zh":9,"content":9,"source_name":153,"source_url":151,"published_at":154,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":155,"sources":160,"tags":162,"primary_topic":165,"secondary_topics":166,"classification":167,"search_phrases":183,"slug":186,"view_count":73,"doi":187,"paper":188,"created_at":204},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","Biosystems Engineering","2026-09-30T00:00:00Z",{"impact":156,"substance":157,"depth":17,"authority":158,"freshness":13,"relevant":22,"comment":159},16,21,14,"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。",[161],{"name":153,"url":151},[27,28,29,163,164],"果园管理","激光雷达",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":168},{"candidates":169,"agricultural_evidence":46},[170,175,179],{"code":33,"score":171,"supported":46,"title":172,"abstract":173,"tags":174},11,[50],[50],[],{"code":59,"score":60,"supported":14,"title":176,"abstract":177,"tags":178},[],[],[28,29],{"code":53,"score":22,"supported":14,"title":180,"abstract":181,"tags":182},[],[],[27],[184,185],"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","10.1016\u002Fj.biosystemseng.2026.104604",{"doi":187,"openalex_id":189,"authors":190,"venue":153,"cited_by_count":84,"oa_url":151,"card":9,"direction":9,"ingested_from":91},"W7214801786",[191,193,195,198,201],{"name":192,"orcid":9},"Jintack Jeon",{"name":194,"orcid":9},"Huijong Chung",{"name":196,"orcid":197},"Gookhwan Kim","https:\u002F\u002Forcid.org\u002F0000-0002-7278-3476",{"name":199,"orcid":200},"Yong–Joo Kim","https:\u002F\u002Forcid.org\u002F0000-0002-1212-9018",{"name":202,"orcid":203},"Dae-Hyun Lee","https:\u002F\u002Forcid.org\u002F0000-0001-9544-5974","2026-09-30T23:30:05.310491Z",{"id":206,"title":207,"url":208,"summary":209,"summary_zh":9,"content":9,"source_name":210,"source_url":9,"published_at":211,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":212,"score_detail":213,"sources":217,"tags":219,"primary_topic":222,"secondary_topics":223,"classification":224,"search_phrases":251,"slug":254,"view_count":73,"doi":9,"paper":255,"created_at":263},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",80,{"impact":17,"substance":214,"depth":17,"authority":20,"freshness":215,"relevant":22,"comment":216},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。",[218],{"name":210,"url":208},[27,28,220,29,221],"边缘计算","蓝莓",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":225},{"candidates":226,"agricultural_evidence":46},[227,232,238,242,246],{"code":33,"score":228,"supported":46,"title":229,"abstract":230,"tags":231},9,[50],[],[221],{"code":233,"score":73,"supported":46,"title":234,"abstract":235,"tags":237},"I6",[],[236],"农业大数据",[],{"code":59,"score":60,"supported":14,"title":239,"abstract":240,"tags":241},[],[],[28,29],{"code":53,"score":22,"supported":14,"title":243,"abstract":244,"tags":245},[],[],[27],{"code":247,"score":22,"supported":14,"title":248,"abstract":249,"tags":250},"I5",[],[],[220],[252,253],"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":9,"openalex_id":9,"authors":256,"venue":9,"cited_by_count":84,"oa_url":9,"card":257,"direction":261,"ingested_from":146},[],{"tldr":258,"method":259,"finding":260,"direction":261,"opportunity":262},"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 ","农业人工智能与决策模型","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":265,"title":266,"url":267,"summary":268,"summary_zh":269,"content":9,"source_name":270,"source_url":267,"published_at":271,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":272,"score_detail":273,"sources":276,"tags":278,"primary_topic":281,"secondary_topics":282,"classification":283,"search_phrases":303,"slug":305,"view_count":60,"doi":306,"paper":307,"created_at":329},2284,"Lightweight nighttime citrus fruit detection in UAV imagery using an enhanced NightNet","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112378","Lightweight nighttime citrus fruit detection in UAV imagery using an enhanced NightNet。Computers and Electronics in Agriculture","基于增强型NightNet的无人机图像轻量化夜间柑橘果实检测。《计算机与电子农业》","Computers and Electronics in Agriculture","2026-09-11T00:00:00Z",77,{"impact":156,"substance":157,"depth":274,"authority":158,"freshness":228,"relevant":22,"comment":275},17,"核心期刊论文，提出轻量化夜间柑橘检测方法，方法新颖且面向无人机夜间作业场景，具备细分领域参考价值。",[277],{"name":270,"url":267},[27,279,28,280,30],"无人机","遥感",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":284},{"candidates":285,"agricultural_evidence":46},[286,291,295,299],{"code":33,"score":287,"supported":46,"title":288,"abstract":289,"tags":290},15,[48],[30,48],[30],{"code":53,"score":22,"supported":14,"title":292,"abstract":293,"tags":294},[],[],[27],{"code":59,"score":22,"supported":14,"title":296,"abstract":297,"tags":298},[],[],[28],{"code":65,"score":22,"supported":14,"title":300,"abstract":301,"tags":302},[],[],[280],[304,253],"农业人工智能 智慧农业 无人机 柑橘","农业人工智能智慧农业无人机柑橘-2284","10.1016\u002Fj.compag.2026.112378",{"doi":306,"openalex_id":308,"authors":309,"venue":270,"cited_by_count":84,"oa_url":9,"card":323,"direction":327,"ingested_from":91},"W7212284586",[310,312,315,317,319,321],{"name":311,"orcid":9},"Zhixian Wu",{"name":313,"orcid":314},"Juntao Xiong","https:\u002F\u002Forcid.org\u002F0000-0002-1095-812X",{"name":316,"orcid":9},"Zhen Liu",{"name":318,"orcid":9},"Sitong Li",{"name":320,"orcid":9},"Jingru Peng",{"name":322,"orcid":9},"Sidi Chen",{"tldr":324,"method":325,"finding":326,"direction":327,"opportunity":328},"提出增强NightNet实现无人机夜间柑橘果实轻量检测。","基于NightNet改进的轻量网络，用于无人机夜间图像。","增强NightNet在夜间无人机图像中实现轻量且准确的柑橘检测。","农业遥感与作物表型","夜间无人机果实检测的轻量化与低照度增强可进一步结合多光谱或时序信息。","2026-09-13T23:30:02.020275Z",{"id":331,"title":332,"url":333,"summary":334,"summary_zh":9,"content":9,"source_name":335,"source_url":9,"published_at":336,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":103,"score_detail":337,"sources":339,"tags":341,"primary_topic":344,"secondary_topics":345,"classification":346,"search_phrases":361,"slug":364,"view_count":365,"doi":9,"paper":366,"created_at":373},2270,"华中农大实现柑橘黄龙病早期无损诊断 提LLM辅助拉曼光谱相似度融合框架","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7682626369448165922","华中农业大学工学院联合园艺林学学院在Agriculture Communications发表研究,提出LLM辅助的多通道光谱相似度融合框架(LAMSSF),依托少量经qPCR验证的参考样本,实现柑橘HLB早期无损诊断,降低模型对大规模标注数据的依赖。","华中农业大学 Agriculture Communications","2026-09-06T16:00:00Z",{"impact":18,"substance":18,"depth":17,"authority":158,"freshness":94,"relevant":22,"comment":338},"华中农大提出LLM辅助拉曼光谱融合框架，实现柑橘黄龙病早期无损诊断，方法新颖且降低标注依赖，具备行业推广价值。",[340],{"name":335,"url":333},[27,28,342,30,343],"病害检测","光谱遥感",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":347},{"candidates":348,"agricultural_evidence":46},[349,353,357],{"code":33,"score":117,"supported":46,"title":350,"abstract":351,"tags":352},[30],[30],[30],{"code":53,"score":22,"supported":14,"title":354,"abstract":355,"tags":356},[],[],[27],{"code":59,"score":22,"supported":14,"title":358,"abstract":359,"tags":360},[],[],[28],[362,363],"农业人工智能 光谱遥感 智慧农业 病害检测","农业人工智能 光谱遥感","农业人工智能光谱遥感智慧农业病害检测-2270",4,{"doi":9,"openalex_id":9,"authors":367,"venue":9,"cited_by_count":84,"oa_url":9,"card":368,"direction":261,"ingested_from":146},[],{"tldr":369,"method":370,"finding":371,"direction":261,"opportunity":372},"提出LLM辅助多通道光谱相似度融合框架，实现柑橘黄龙病早期无损诊断。","拉曼光谱多通道相似度融合，LLM辅助，少量qPCR验证参考样本。","少量标注样本即可实现HLB早期无损诊断，降低对大规模标注数据依赖。","可探索LLM辅助光谱融合在更多作物病害早期诊断中的迁移与少样本泛化能力。","2026-09-13T00:04:04.913308Z",{"id":375,"title":376,"url":377,"summary":378,"summary_zh":9,"content":379,"source_name":380,"source_url":9,"published_at":381,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":382,"score_detail":383,"sources":386,"tags":388,"primary_topic":390,"secondary_topics":391,"classification":392,"search_phrases":408,"slug":410,"view_count":73,"doi":9,"paper":411,"created_at":413},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 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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":287,"substance":384,"depth":17,"authority":117,"freshness":365,"relevant":22,"comment":385},20,"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。",[387],{"name":380,"url":377},[27,28,29,389],"芒果检测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":393},{"candidates":394,"agricultural_evidence":46},[395,400,404],{"code":33,"score":19,"supported":46,"title":396,"abstract":398,"tags":399},[397,50],"芒果",[397],[],{"code":59,"score":60,"supported":14,"title":401,"abstract":402,"tags":403},[],[],[28,29],{"code":53,"score":22,"supported":14,"title":405,"abstract":406,"tags":407},[],[],[27],[409,253],"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":9,"openalex_id":9,"authors":412,"venue":9,"cited_by_count":84,"oa_url":9,"card":9,"direction":9,"ingested_from":146},[],"2026-09-01T00:03:40.048486Z"]