[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3356":3,"related-3356":64},{"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,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":63},3356,"Simultaneous Maturity Recognition and Yield Counting of Truss and Individual Tomatoes Using Improved YOLOv8-EME and Optimized ByteTrack Tracker","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fhorticulturae12101199","Crop maturity detection and yield estimation are critical components of protected agriculture, supporting optimized harvest timing, fruit quality control, and coordination of production and marketing. Existing studies on detection and counting predominantly address a single category—either truss or individual fruits. To advance automation and intelligence in production management, this study proposes a tomato detection and counting system that integrates an improved YOLOv8-EME model with the ByteTrack algorithm, enabling simultaneous detection and counting of truss and individual fruits with maturity classification. The improved YOLOv8-EME model combines the EfficientNet architecture with the EffectiveSE attention mechanism, improving feature extraction and computational efficiency. In addition, the optimized network structure yields a lightweight design, reducing FLOPs to 6.9 G. The model attains a mean Average Precision (mAP) of 0.942, 0.883, 0.850, and 0.956 for the Ripe, Raw, Medium-Raw, and Truss categories, respectively. A proposed cross-line counting method integrated with an improved ByteTrack algorithm mitigates target loss, ID drift, and duplicate counting through ID drift association, trajectory fusion, historical trajectory cues, and a cooldown scheme. These designs significantly improve detection and counting accuracy for truss and fruit maturity, achieving a counting accuracy of 94%. The system offers efficient and accurate technical support for tomato detection and counting in smart agriculture.","作物成熟度检测与产量估测是设施农业的关键环节，可为优化采收时机、果实品质控制及产销协调提供支撑。现有检测与计数研究大多仅针对单一类别，即串收番茄或单个果实。为推动生产管理的自动化与智能化，本研究提出了一种融合改进YOLOv8-EME模型与ByteTrack算法的番茄检测与计数系统，可实现串收番茄与单个果实的同步检测与计数，并进行成熟度分类。改进后的YOLOv8-EME模型将EfficientNet架构与EffectiveSE注意力机制相结合，提升了特征提取能力与计算效率。此外，优化后的网络结构实现了轻量化设计，浮点运算次数（FLOPs）降至6.9 G。该模型在成熟、未成熟、半熟和串收四个类别上的平均精度均值（mAP）分别为0.942、0.883、0.850和0.956。所提出的跨线计数方法与改进的ByteTrack算法相结合，通过ID漂移关联、轨迹融合、历史轨迹线索和冷却机制，缓解了目标丢失、ID漂移和重复计数问题。这些设计显著提升了串收番茄和果实成熟度的检测与计数精度，计数准确率达到94%。该系统为智慧农业中的番茄检测与计数提供了高效、准确的技术支持。",null,"Horticulturae","2026-09-23T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,22,18,13,9,1,"该研究提出改进YOLOv8-EME与优化ByteTrack的番茄检测计数系统，可同时识别串收与单果成熟度并计数，方法新颖、数据详实，对智慧农业采摘自动化有实用价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","目标检测","番茄","产量估测",[33,34],"YOLOv8 番茄 成熟度检测","番茄 串收 产量计数","YOLOv8番茄成熟度检测-3356",0,"10.3390\u002Fhorticulturae12101199",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":55,"direction":61,"ingested_from":62},"W7214123905",[41,43,46,48,50,52],{"name":42,"orcid":9},"Liying Shi",{"name":44,"orcid":45},"Sen Lin","https:\u002F\u002Forcid.org\u002F0000-0001-6521-3152",{"name":47,"orcid":9},"Haihang Zhao",{"name":49,"orcid":9},"Tianlong Sun",{"name":51,"orcid":9},"Dongdong Sun",{"name":53,"orcid":54},"Yuchen Yang","https:\u002F\u002Forcid.org\u002F0000-0001-5977-1617",{"tldr":56,"method":57,"finding":58,"direction":59,"opportunity":60},"提出改进YOLOv8-EME与ByteTrack结合的番茄检测计数系统，可同时识别串收与单果成熟度并","改进YOLOv8-EME（EfficientNet+EffectiveSE）结合","模型mAP达0.942\u002F0.883\u002F0.850\u002F0.956，计数准确率94%，FLOPs仅6.9G。","农业人工智能与决策模型","可探索多作物、多生长阶段通用模型，并融合边缘部署与产量预测决策。","智慧农业 \u002F 农业物联网","openalex","2026-09-24T23:30:10.615639Z",{"total":65,"page":22,"page_size":65,"items":66},6,[67,120,148,174,217,258],{"id":68,"title":69,"url":70,"summary":71,"summary_zh":72,"content":9,"source_name":73,"source_url":70,"published_at":74,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":75,"score_detail":76,"sources":82,"tags":84,"search_phrases":86,"slug":89,"view_count":36,"doi":90,"paper":91,"created_at":119},2741,"A tomato maturity detection method against occlusion and variable illumination","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112423","A tomato maturity detection method against occlusion and variable illumination。Computers and Electronics in Agriculture","一种抗遮挡和可变光照的番茄成熟度检测方法","Computers and Electronics in Agriculture","2026-09-16T00:00:00Z",68,{"impact":77,"substance":19,"depth":78,"authority":79,"freshness":80,"relevant":22,"comment":81},12,16,14,8,"核心期刊论文，针对遮挡与光照变化下的番茄成熟度检测提出新方法，属农业人工智能细分领域实质进展，但应用范围有限，未达重大突破层级。",[83],{"name":73,"url":70},[27,28,29,30,85],"作物表型",[87,88],"农业人工智能 作物表型 智慧农业 目标检测","农业人工智能 作物表型","农业人工智能作物表型智慧农业目标检测-2741","10.1016\u002Fj.compag.2026.112423",{"doi":90,"openalex_id":92,"authors":93,"venue":73,"cited_by_count":36,"oa_url":9,"card":114,"direction":59,"ingested_from":62},"W7213429689",[94,97,100,103,106,109,111],{"name":95,"orcid":96},"Hao Meng","https:\u002F\u002Forcid.org\u002F0000-0001-7511-2910",{"name":98,"orcid":99},"Wenzhe Li","https:\u002F\u002Forcid.org\u002F0009-0008-1630-4697",{"name":101,"orcid":102},"Di Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3911-8159",{"name":104,"orcid":105},"Hui Zhao","https:\u002F\u002Forcid.org\u002F0009-0005-4192-5776",{"name":107,"orcid":108},"Ximing Li","https:\u002F\u002Forcid.org\u002F0000-0003-4022-1273",{"name":110,"orcid":9},"Dongdong Cui",{"name":112,"orcid":113},"Fernando Auat Cheein","https:\u002F\u002Forcid.org\u002F0000-0002-6347-7696",{"tldr":115,"method":116,"finding":117,"direction":59,"opportunity":118},"提出一种抗遮挡和光照变化的番茄成熟度检测方法。","基于深度学习的图像检测，针对遮挡与光照变化优化。","该方法在遮挡和变光照下仍能准确检测番茄成熟度。","可探索多模态融合与轻量化部署，提升田间复杂场景实时检测鲁棒性。","2026-09-17T23:30:01.491379Z",{"id":121,"title":122,"url":123,"summary":124,"summary_zh":9,"content":9,"source_name":125,"source_url":9,"published_at":126,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":127,"score_detail":128,"sources":130,"tags":132,"search_phrases":135,"slug":138,"view_count":36,"doi":9,"paper":139,"created_at":147},3322,"PB-DETR：群养生猪多行为识别的自适应液体神经网络编码器与动态监督框架","https:\u002F\u002Fjky.yangtzeu.edu.cn\u002Finfo\u002F1265\u002F6884.htm","长江大学计算机科学学院研究生郭宇衡作为第一作者，在计算机与农业交叉领域顶级期刊《Computers and Electronics in Agriculture》发表题为PB-DETR: multi-behaviour recognition of group-housed pigs with adaptive liquid neural network encoder and dynamic supervision的研究论文。长江大学智慧农业交叉创新研究中心主任、计科学院詹炜教授为通讯作者。研究将液体神经网络机制引入Transformer编码器，设计自适应液体时间常数增强模块，构建多尺度特征交互和动态质量感知优化策略，进而降低模型对固定背景信息的依赖。基于PB-DETR框架构建的模型平均精度(mAP)达到0.893，参数量为14.58M，计算量为49.12GFLOPs；在不同养殖场景之间的测试中表现出较好稳定性。","《Computers and Electronics in Agriculture》2026","2026-09-21T00:00:00Z",78,{"impact":78,"substance":18,"depth":19,"authority":79,"freshness":80,"relevant":22,"comment":129},"农业人工智能顶刊论文，方法新颖、指标明确，对智慧养殖行为识别有实质参考价值，值得进入每日精选。",[131],{"name":125,"url":123},[27,28,133,29,134],"智慧养殖","生猪行为识别",[136,137],"长江大学 詹炜 生猪行为识别","PB-DETR 群养生猪 多行为识别","长江大学詹炜生猪行为识别-3322",{"doi":9,"openalex_id":9,"authors":140,"venue":9,"cited_by_count":36,"oa_url":9,"card":141,"direction":59,"ingested_from":146},[],{"tldr":142,"method":143,"finding":144,"direction":59,"opportunity":145},"提出PB-DETR框架，实现群养生猪多行为识别，mAP达0.893。","引入液体神经网络改进Transformer编码器，结合多尺度特征交互与动态监督。","模型在群养生猪多行为识别中mAP达0.893，跨场景稳定性较好。","可探索液体神经网络在其它畜禽行为识别中的泛化能力及轻量化部署。","agent","2026-09-24T00:04:02.413623Z",{"id":149,"title":150,"url":151,"summary":152,"summary_zh":9,"content":9,"source_name":153,"source_url":9,"published_at":154,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":127,"score_detail":155,"sources":157,"tags":159,"search_phrases":162,"slug":165,"view_count":36,"doi":9,"paper":166,"created_at":173},2904,"Decoupled Foundation Models:基于YOLO26m+SAM2+DINOv2的湿度诱导番茄叶坏死实例分割与检测,登MDPI Agriculture 16(18)1997","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F18\u002F1997","本研究针对温室番茄相对湿度过高引发的非生物胁迫(生理性叶坏死,与生物感染症状相似),提出多步AI管道自动化分割与分类坏死叶斑。采集218张RGB图像、3218个标注(棕色坏死斑\u002F黄色坏死斑\u002F无坏死),系统评估6种端到端实例分割管道(YOLO26m检测+SAM2零样本分割+微调DINOv2或EfficientNet-B3分类);微调DINOv2宏F1达0.926,优于EfficientNet-B3、ResNet-50、Swin-Small基线(0.886-0.901);最佳配置mAP@50=0.828,较YOLO26m单模型提升约8%。","MDPI Agriculture","2026-09-17T00:00:00Z",{"impact":78,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":156},"方法组合新颖、数据规模与对比基线扎实，对温室番茄生理性叶坏死自动识别有实用价值，值得进入每日精选。",[158],{"name":153,"url":151},[27,28,160,30,161],"设施农业","病害识别",[163,164],"番茄叶坏死 实例分割","农业人工智能 智慧农业 病害识别 设施农业","番茄叶坏死实例分割-2904",{"doi":9,"openalex_id":9,"authors":167,"venue":9,"cited_by_count":36,"oa_url":9,"card":168,"direction":59,"ingested_from":146},[],{"tldr":169,"method":170,"finding":171,"direction":59,"opportunity":172},"用YOLO26m+SAM2+DINOv2多步管道分割并分类高湿诱导的番茄叶坏死斑。","218张RGB图像、3218个标注，评估6种实例分割管道并微调DINOv2分类。","微调DINOv2宏F1达0.926，最佳配置mAP@50=0.828，较单模型提升约8%。","可探索零样本基础模型在多种非生物胁迫症状上的泛化与轻量化温室部署。","2026-09-19T00:06:09.021594Z",{"id":175,"title":176,"url":177,"summary":178,"summary_zh":179,"content":9,"source_name":180,"source_url":177,"published_at":74,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":181,"score_detail":182,"sources":185,"tags":187,"search_phrases":190,"slug":193,"view_count":36,"doi":194,"paper":195,"created_at":216},2798,"Deep Learning-Based Small-Object Detection in UAV Imagery: A Symmetry-Informed Review of Scale Variation and Scenario Constraints","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsym18091548","As UAV platforms are increasingly employed in military reconnaissance, disaster monitoring, precision agriculture, and other remote-sensing applications, small-object detection in UAV imagery is a critical yet challenging task. Existing reviews primarily address either general small-object detection or general object detection in UAV imagery, whereas reviews specifically devoted to small-object detection in UAV imagery remain scarce. This review examines existing deep learning-based research on small-object detection in UAV imagery from a problem-driven and deployment-oriented perspective. A symmetry-informed perspective was also adopted to analyze how variations in object-scale, viewpoint, imaging quality, and scene conditions affected feature representation and detection stability. Building on five major challenges in UAV small-object detection, a three-tier framework encompassing detection paradigms, key technical strategies, and scenario-specific constraints was established. Representative UAV datasets were further examined to characterize object-scale distributions, while existing methods were reviewed in terms of multiscale representation, feature enhancement, training optimization, and lightweight deployment. The applicability of different technical approaches was then discussed across five typical UAV scenarios. In addition, under relatively consistent experimental conditions, representative detection models based on two-stage, one-stage, and Transformer-based paradigms were analyzed in terms of detection accuracy, computational efficiency, and deployment feasibility. Finally, current limitations and future directions were summarized for scale-robust, scenario-adaptive, and resource-aware small-object detection in UAV imagery.","随着无人机平台在军事侦察、灾害监测、精准农业等遥感应用中的日益普及，无人机图像中的小目标检测成为一项关键但具有挑战性的任务。现有综述主要关注通用小目标检测或无人机图像中的通用目标检测，而专门针对无人机图像中小目标检测的综述仍然匮乏。本综述从问题驱动和部署导向的视角，审视了现有的基于深度学习的无人机图像小目标检测研究。同时采用对称性视角，分析了目标尺度、视角、成像质量和场景条件的变化如何影响特征表示和检测稳定性。基于无人机小目标检测中的五大挑战，构建了一个涵盖检测范式、关键技术策略和场景特定约束的三层框架。进一步考察了代表性无人机数据集以刻画目标尺度分布，同时从多尺度表示、特征增强、训练优化和轻量化部署等方面综述了现有方法。随后讨论了不同技术方法在五种典型无人机场景中的适用性。此外，在相对一致的实验条件下，对基于两阶段、单阶段和Transformer范式的代表性检测模型在检测精度、计算效率和部署可行性方面进行了分析。最后，总结了当前局限性及未来方向，以推动无人机图像中尺度鲁棒、场景自适应和资源感知的小目标检测研究。","Symmetry",76,{"impact":78,"substance":183,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":184},20,"无人机小目标检测的系统性综述，提出对称性视角与三层框架，对农业遥感与精准农业部署有实质参考价值。",[186],{"name":180,"url":177},[27,188,28,189,29],"无人机","农业遥感",[191,192],"农业人工智能 农业遥感 智慧农业 目标检测","农业人工智能 农业遥感","农业人工智能农业遥感智慧农业目标检测-2798","10.3390\u002Fsym18091548",{"doi":194,"openalex_id":196,"authors":197,"venue":180,"cited_by_count":36,"oa_url":177,"card":210,"direction":214,"ingested_from":62},"W7213430256",[198,200,203,205,207],{"name":199,"orcid":9},"Ling Wen",{"name":201,"orcid":202},"Anmin Gong","https:\u002F\u002Forcid.org\u002F0000-0002-2715-2296",{"name":204,"orcid":9},"Caihong Ma",{"name":206,"orcid":9},"Shengzhou Ma",{"name":208,"orcid":209},"Yanzhe Zhang","https:\u002F\u002Forcid.org\u002F0009-0008-6529-1247",{"tldr":211,"method":212,"finding":213,"direction":214,"opportunity":215},"综述无人机图像小目标检测的深度学习研究，提出对称性视角与三层框架。","文献综述，分析代表性数据集与两阶段、单阶段、Transformer模型。","尺度变化和场景约束是核心挑战，需尺度鲁棒、场景自适应和资源感知检测。","农业遥感与作物表型","可探索农业场景下尺度自适应与轻量化部署的平衡，以及跨场景泛化的小目标检测。","2026-09-17T23:30:37.479759Z",{"id":218,"title":219,"url":220,"summary":221,"summary_zh":222,"content":9,"source_name":223,"source_url":220,"published_at":74,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":224,"sources":227,"tags":229,"search_phrases":232,"slug":235,"view_count":36,"doi":236,"paper":237,"created_at":257},2774,"SpatioFormer: spatial perception enhancement for lightweight agricultural pest and disease detection","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1925867","Introduction In precision agriculture, accurate and efficient detection of crop pests and diseases is crucial. However, existing models in complex environments are prone to insufficient spatial perception and attenuation of disease texture features, making it difficult to balance recognition accuracy and lightweighting. Methods To address this, this study proposes a lightweight spatial perception enhancement hybrid architecture, SpatioFormer. First, a Pixel-level Detail Retrieval (PDR) mechanism is designed. This mechanism leverages cross-layer dynamic routing to facilitate the fusion of deep semantic features with shallow texture features, significantly enhancing the capability to capture disease features. Second, we design a Spatially Adaptive Modulation Attention (SA-SHMA) mechanism, which utilizes large-kernel depthwise convolution to capture contextual information and combines dynamic modulation maps for fine-grained focusing, efficiently recovering spatial details, and suppressing background noise. Furthermore, this paper introduces a Context-Guided Asymmetric Gated Linear Unit (CGA-GLU), which utilizes an asymmetric design focusing on the gating branch and incorporates contextual information for guidance, enhancing the inter-channel representation capability with minimal computational overhead. Results Finally, extensive experiments on the PDDD and Tomato-Village datasets validated the effectiveness of the proposed model. The proposed model achieves a Top-1 accuracy of 81.05% on the PDDD dataset and an AP 50 of 61.53% on the Tomato-Village dataset, with testing latency on edge devices being highly competitive among existing models. Discussion Compared to existing lightweight hybrid models, SpatioFormer effectively recovers shallow spatial details and precisely suppresses complex background noise under an extremely low parameter budget. Consequently, it achieves a superior balance between practical disease localization capability and inference latency on resource-constrained edge devices.","引言 在精准农业中，准确高效地检测作物病虫害至关重要。然而，复杂环境下的现有模型容易出现空间感知不足和病害纹理特征衰减的问题，难以兼顾识别精度与轻量化。方法 为解决这一问题，本研究提出了一种轻量级空间感知增强混合架构——SpatioFormer。首先，设计了像素级细节检索（Pixel-level Detail Retrieval，PDR）机制。该机制利用跨层动态路由，促进深层语义特征与浅层纹理特征的融合，显著增强了对病害特征的捕捉能力。其次，设计了空间自适应调制注意力（Spatially Adaptive Modulation Attention，SA-SHMA）机制，该机制利用大核深度卷积捕获上下文信息，并结合动态调制图进行细粒度聚焦，高效恢复空间细节并抑制背景噪声。此外，本文引入了上下文引导非对称门控线性单元（Context-Guided Asymmetric Gated Linear Unit，CGA-GLU），其采用聚焦门控分支的非对称设计，并融入上下文信息进行引导，以极小的计算开销增强了通道间表征能力。结果 最后，在PDDD和Tomato-Village数据集上的大量实验验证了所提模型的有效性。所提模型在PDDD数据集上取得了81.05%的Top-1准确率，在Tomato-Village数据集上取得了61.53%的AP 50，其在边缘设备上的测试延迟在现有模型中极具竞争力。讨论 与现有轻量级混合模型相比，SpatioFormer在极低的参数预算下有效恢复了浅层空间细节，并精确抑制了复杂背景噪声。因此，它在实际病害定位能力与资源受限边缘设备上的推理延迟之间实现了更优的平衡。","Frontiers in Plant Science",{"impact":78,"substance":225,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":226},21,"提出轻量化空间感知增强架构，在边缘设备上兼顾检测精度与推理延迟，方法新颖、实验扎实，对农业病虫害智能识别有参考价值。",[228],{"name":223,"url":220},[27,28,230,30,231],"边缘计算","病虫害检测",[233,234],"农业人工智能 病虫害检测 智慧农业 边缘计算","农业人工智能 病虫害检测","农业人工智能病虫害检测智慧农业边缘计算-2774","10.3389\u002Ffpls.2026.1925867",{"doi":236,"openalex_id":238,"authors":239,"venue":223,"cited_by_count":36,"oa_url":251,"card":252,"direction":61,"ingested_from":62},"W7213437661",[240,242,245,247,249],{"name":241,"orcid":9},"Wenbo Ma",{"name":243,"orcid":244},"Hao Sun","https:\u002F\u002Forcid.org\u002F0000-0002-6983-8149",{"name":246,"orcid":9},"Kun Zhou",{"name":248,"orcid":9},"Meichun Wang",{"name":250,"orcid":9},"Rui Fu","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1925867\u002Fpdf",{"tldr":253,"method":254,"finding":255,"direction":59,"opportunity":256},"提出轻量混合架构SpatioFormer，提升复杂环境下农作物病虫害检测的空间感知能力。","设计PDR跨层动态路由、SA-SHMA大核注意力与CGA-GLU门控，在PDDD","在极低参数量下恢复浅层空间细节并抑制背景噪声，边缘设备延迟具竞争力。","可探索将空间感知增强机制迁移至多作物多病害场景，并研究边缘端实时部署的能效优化。","2026-09-17T23:30:14.148727Z",{"id":259,"title":260,"url":261,"summary":262,"summary_zh":263,"content":9,"source_name":223,"source_url":261,"published_at":74,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":181,"score_detail":264,"sources":267,"tags":269,"search_phrases":271,"slug":274,"view_count":22,"doi":275,"paper":276,"created_at":294},2638,"Full shuffle and p-rectified semi-inner powerful IoU-based chili pepper flower recognition with environment-aware YOLO11","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1935123","As a common greenhouse-grown commercial crop, chili pepper yields have always been a focus of attention. Effective management is essential for both environmental sustainability and high productivity in greenhouses. Whether for growth monitoring, yield estimates, and automated production, precise identification of chili flowers is critical. To this goal, this paper works upon the YOLO11 object detection model, focusing on the precise detection of chili flowers. The diversity of samples across various scenarios is increased by utilizing a self-built dataset of chili flowers in greenhouses and integrating data augmentation. To address YOLO11’s limitations in this task, this paper proposes a unified framework that integrates three modules: (1) full shuffle to enhance information exchange between model channels and optimize weights; (2) Since YOLO11 cannot incorporate external environmental data during training, the environment-aware C3k2 is introduced; and (3) The proposed p-rectified Semi-inner Powerful IoU accelerates model convergence and flexibility. Experiments show that our method obtains 84.8% mAP50, 50.2% mAP50-95, 81.1% Precision, and 75.6% Recall, outperforming the baseline YOLO11’s 77.3% by 7.5 percentage points, as well as YOLOv8, YOLO12, RTDETR, and Faster R-CNN under the same experimental conditions. Therefore, in terms of performance, our method generally surpasses existing algorithms for chili flower detection. Research on detecting chili flowers in greenhouses remains limited. The study offers valuable insights for the advancement of smart agriculture. Future work will further explore the model’s generalization capabilities across multiple varieties and growth stages, as well as deploy it on embedded devices for practical application.","作为常见的温室商业化种植作物，辣椒的产量一直备受关注。有效的管理对于温室的環境可持续性和高生产力都至关重要。无论是生长监测、产量估算还是自动化生产，辣椒花朵的精准识别都至关重要。为实现这一目标，本文基于YOLO11目标检测模型，聚焦辣椒花朵的精准检测。通过利用自建的温室辣椒花朵数据集并结合数据增强，增加了不同场景下样本的多样性。针对YOLO11在此任务中的局限性，本文提出了一个集成三个模块的统一框架：（1）完全混洗（full shuffle），以增强模型通道间的信息交换并优化权重；（2）由于YOLO11在训练过程中无法纳入外部环境数据，引入了环境感知C3k2（environment-aware C3k2）；（3）提出的p-rectified Semi-inner Powerful IoU加速了模型收敛并提升了灵活性。实验表明，本方法取得了84.8%的mAP50、50.2%的mAP50-95、81.1%的精确率和75.6%的召回率，较基线YOLO11的77.3%提升了7.5个百分点，并在相同实验条件下优于YOLOv8、YOLO12、RTDETR和Faster R-CNN。因此，在性能方面，本方法总体上超越了现有的辣椒花朵检测算法。目前关于温室辣椒花朵检测的研究仍然有限。本研究为智慧农业的发展提供了有价值的见解。未来工作将进一步探索模型在多个品种和生长阶段上的泛化能力，并将其部署到嵌入式设备上以实现实际应用。",{"impact":17,"substance":225,"depth":265,"authority":20,"freshness":13,"relevant":22,"comment":266},17,"基于YOLO11的辣椒花检测新方法，mAP50提升7.5个百分点，对设施农业智能监测有参考价值，但属细分技术进展，影响范围有限。",[268],{"name":223,"url":261},[27,28,160,29,270],"辣椒",[272,273],"农业人工智能 智慧农业 目标检测 设施农业","农业人工智能 智慧农业","农业人工智能智慧农业目标检测设施农业-2638","10.3389\u002Ffpls.2026.1935123",{"doi":275,"openalex_id":277,"authors":278,"venue":223,"cited_by_count":36,"oa_url":261,"card":289,"direction":61,"ingested_from":62},"W7213351973",[279,281,283,286],{"name":280,"orcid":9},"Cui-Ping Zhang",{"name":282,"orcid":9},"Zhi-Yong Wang",{"name":284,"orcid":285},"Xuewei Wang","https:\u002F\u002Forcid.org\u002F0000-0001-9604-3045",{"name":287,"orcid":288},"Zhi Li","https:\u002F\u002Forcid.org\u002F0000-0001-5571-0518",{"tldr":290,"method":291,"finding":292,"direction":59,"opportunity":293},"基于YOLO11改进，实现温室辣椒花精准检测。","自建数据集+数据增强，引入全混洗、环境感知C3k2和p校正IoU。","mAP50达84.8%，比基线YOLO11提升7.5个百分点，优于多个对比模型。","温室辣椒花检测研究少，可探索多品种、多生长期泛化及嵌入式部署。","2026-09-16T23:30:09.868848Z"]