[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2774":3,"related-2774":102},{"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":71,"slug":74,"view_count":75,"doi":76,"paper":77,"created_at":101},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。",null,"Frontiers in Plant Science","2026-09-16T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,18,13,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","边缘计算","番茄","病虫害检测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":48},[45,54,60,66],{"code":46,"score":47,"supported":48,"title":49,"abstract":50,"tags":53},"I1",7,true,[],[51,52],"精准农业","precision agriculture",[27],{"code":33,"score":55,"supported":48,"title":56,"abstract":57,"tags":59},4,[],[58],"tomato",[30],{"code":61,"score":62,"supported":14,"title":63,"abstract":64,"tags":65},"I2",2,[],[],[28,31],{"code":67,"score":22,"supported":14,"title":68,"abstract":69,"tags":70},"I5",[],[],[29],[72,73],"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",3,"10.3389\u002Ffpls.2026.1925867",{"doi":76,"openalex_id":78,"authors":79,"venue":10,"cited_by_count":91,"oa_url":92,"card":93,"direction":99,"ingested_from":100},"W7213437661",[80,82,85,87,89],{"name":81,"orcid":9},"Wenbo Ma",{"name":83,"orcid":84},"Hao Sun","https:\u002F\u002Forcid.org\u002F0000-0002-6983-8149",{"name":86,"orcid":9},"Kun Zhou",{"name":88,"orcid":9},"Meichun Wang",{"name":90,"orcid":9},"Rui Fu",0,"https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1925867\u002Fpdf",{"tldr":94,"method":95,"finding":96,"direction":97,"opportunity":98},"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。","农业人工智能与决策模型","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。","智慧农业 \u002F 农业物联网","openalex","2026-09-17T23:30:14.148727Z",{"total":103,"page":22,"page_size":103,"items":104},6,[105,166,238,303,348,414],{"id":106,"title":107,"url":108,"summary":109,"summary_zh":9,"content":9,"source_name":110,"source_url":9,"published_at":111,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":112,"score_detail":113,"sources":117,"tags":119,"primary_topic":122,"secondary_topics":123,"classification":124,"search_phrases":152,"slug":155,"view_count":62,"doi":156,"paper":157,"created_at":165},5301,"MambaSeed:基于Mamba的轻量级混合视觉网络用于番茄叶病识别(PLOS ONE)","https:\u002F\u002Fjournals.plos.org\u002Fplosone\u002Farticle?id=10.1371\u002Fjournal.pone.0359350","PLOS ONE于2026-10-15发表论文MambaSeed,由Wang X, Zhang H, Chen Y, Li Y, Qin Y, Lv Z等团队提出基于MambaVisionMixer的轻量级混合视觉网络,配套局部连续分支注入(LCBI)机制;在番茄、玉米、马铃薯数据集上识别准确率分别达98.97%、98.06%和95.87%,模型仅1.13M参数、0.240G MACs,在RTX 4090上延迟3.39ms\u002F图像,为资源受限农田环境提供紧凑、高效、鲁棒的病害识别方案。","PLOS ONE(2026-10-15)","2026-10-15T00:00:00Z",79,{"impact":19,"substance":114,"depth":19,"authority":20,"freshness":115,"relevant":22,"comment":116},22,8,"方法新颖、指标扎实的轻量级病害识别论文，对资源受限农田场景有实用价值，值得进入每日精选。",[118],{"name":110,"url":108},[27,28,30,120,121],"病害识别","轻量级模型",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":125},{"candidates":126,"agricultural_evidence":48},[127,132,138,144,148],{"code":33,"score":128,"supported":48,"title":129,"abstract":130,"tags":131},12,[30],[30],[30],{"code":133,"score":75,"supported":48,"title":134,"abstract":135,"tags":137},"A3",[],[136],"玉米",[],{"code":139,"score":75,"supported":48,"title":140,"abstract":141,"tags":143},"A5",[],[142],"马铃薯",[],{"code":61,"score":62,"supported":14,"title":145,"abstract":146,"tags":147},[],[],[28,120],{"code":46,"score":22,"supported":14,"title":149,"abstract":150,"tags":151},[],[],[27],[153,154],"MambaSeed 番茄叶病","MambaVisionMixer 病害识别","MambaSeed番茄叶病-5301","10.1371\u002Fjournal.pone.0359350",{"doi":156,"openalex_id":9,"authors":158,"venue":9,"cited_by_count":91,"oa_url":9,"card":159,"direction":97,"ingested_from":164},[],{"tldr":160,"method":161,"finding":162,"direction":97,"opportunity":163},"提出轻量级混合视觉网络MambaSeed，实现番茄等作物叶病高效识别。","基于MambaVisionMixer与局部连续分支注入机制，在三种作物病害数据集","番茄、玉米、马铃薯识别准确率达98.97%、98.06%、95.87%，仅1.13M参数、3.39m","可探索该轻量模型在移动端与边缘设备的田间部署，及多作物跨域泛化与病害早期识别。","agent","2026-10-19T00:07:48.589614Z",{"id":167,"title":168,"url":169,"summary":170,"summary_zh":171,"content":9,"source_name":172,"source_url":169,"published_at":173,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":174,"score_detail":175,"sources":179,"tags":181,"primary_topic":184,"secondary_topics":185,"classification":191,"search_phrases":209,"slug":212,"view_count":22,"doi":213,"paper":214,"created_at":237},4494,"Tomato ripeness classification with weight-based and texture-adaptive pooling in CNNs","https:\u002F\u002Fdoi.org\u002F10.64960\u002Feasr.2026.266223","This research introduces a weight-based mixed pooling and adaptive fuzzy pooling mechanism based on feature map texture analysis for attention mechanism Squeeze-and-Excitation Networks (SE) and Convolutional Neural Networks (CNNs) in tomato ripeness classification. Traditional CNNs and SE networks rely on deterministic pooling strategies that can be suboptimal for capturing diverse visual features such as texture and color gradients in fruit. A novel fuzzy pooling system is proposed that dynamically adjusts the fusion ratio of min and max pooling based on real-time texture analysis of feature maps via a Laplacian filter. This mechanism was integrated into Darknet53, ResNet34, and ResNet101 architectures, both directly and within the SE attention block. The method was evaluated on a five-class tomato ripeness dataset using 10-fold cross-validation against traditional and fixed-weight pooling methods. The fuzzy and mixed pooling approach demonstrated superior performance on certain models which include ESEnet: Darknet53 + SE (Fuzzy pool) achieved an average F1 score across folds of 0.8429, outperforming Darknet53 + baseline SE (Average pool) at 0.8350; Extended Net: ResNet34 + Fuzzy pool achieved an average F1 score across folds of 0.8305, outperforming baseline ResNet34 at 0.8130 and ResNet101 + Fuzzy pool achieved an average F1 score across folds of 0.8284, outperforming baseline ResNet101 at 0.8267. This improvement was realized with minimal increase in computational overhead. Specifically, measurements of model parameters (M) and FLOPs (GMACs) showed only a slight increase across all architectures after integrating SEnet into the system, and zero increase after integrating the Laplacian filter and fuzzy logic. The study concludes that logic-based adaptive pooling is a robust strategy that improves feature representation in CNNs, achieving a balance between classification accuracy and computational cost. These findings offer practical value for developing more reliable automated harvesting systems and provide a generalizable enhancement for a wide range of computer vision classification tasks.","本研究提出了一种基于特征图纹理分析的权重混合池化与自适应模糊池化机制，用于番茄成熟度分类中的注意力机制挤压激励网络（Squeeze-and-Excitation Networks, SE）和卷积神经网络（Convolutional Neural Networks, CNNs）。传统的CNN和SE网络依赖确定性池化策略，在捕捉果实纹理和颜色梯度等多样化视觉特征时可能并非最优。本文提出了一种新颖的模糊池化系统，通过拉普拉斯滤波器对特征图进行实时纹理分析，动态调整最小池化和最大池化的融合比例。该机制被集成到Darknet53、ResNet34和ResNet101架构中，既可直接集成，也可嵌入SE注意力模块内。该方法在五类番茄成熟度数据集上采用10折交叉验证，与传统池化及固定权重池化方法进行了对比评估。模糊池化和混合池化方法在部分模型上表现出优越性能，包括：ESEnet：Darknet53 + SE（模糊池化）在各折上的平均F1得分为0.8429，优于Darknet53 + 基线SE（平均池化）的0.8350；扩展网络：ResNet34 + 模糊池化在各折上的平均F1得分为0.8305，优于基线ResNet34的0.8130；ResNet101 + 模糊池化在各折上的平均F1得分为0.8284，优于基线ResNet101的0.8267。这一改进仅带来了极小的计算开销增加。具体而言，模型参数量（M）和浮点运算次数（GMACs）的测量结果表明，将SEnet集成到系统后所有架构仅有轻微增加，而集成拉普拉斯滤波器和模糊逻辑后则无任何增加。研究结论认为，基于逻辑的自适应池化是一种鲁棒策略，能够改善CNN中的特征表示，在分类精度和计算成本之间实现平衡。这些发现为开发更可靠的自动化收获系统提供了实用价值，并为广泛的计算机视觉分类任务提供了一种可推广的增强方法。","Engineering and Applied Science Research","2026-10-07T00:00:00Z",66,{"impact":115,"substance":176,"depth":177,"authority":128,"freshness":21,"relevant":22,"comment":178},20,17,"提出基于纹理自适应模糊池化的CNN改进方法，在番茄成熟度分类上取得小幅但稳定的F1提升且算力开销几乎不增，对自动采摘与农业视觉识别有参考价值，但属细分方法类论文，影响面有限。",[180],{"name":172,"url":169},[27,28,30,182,183],"图像识别","自动采摘",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[186],{"code":61,"slug":187,"name":188,"path":189,"parent_code":190},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":192},{"candidates":193,"agricultural_evidence":48},[194,199,205],{"code":33,"score":195,"supported":48,"title":196,"abstract":197,"tags":198},15,[58],[30,58],[30],{"code":61,"score":115,"supported":48,"title":200,"abstract":201,"tags":204},[],[202,203],"计算机视觉","computer vision",[28,182],{"code":46,"score":22,"supported":14,"title":206,"abstract":207,"tags":208},[],[],[27],[210,211],"番茄 成熟度 分类 卷积神经网络","Darknet53 ResNet 番茄 成熟度","番茄成熟度分类卷积神经网络-4494","10.64960\u002Feasr.2026.266223",{"doi":213,"openalex_id":215,"authors":216,"venue":172,"cited_by_count":91,"oa_url":231,"card":232,"direction":99,"ingested_from":100},"W7220840158",[217,219,221,223,226,228],{"name":218,"orcid":9},"Rut Yatigul",{"name":220,"orcid":9},"Wisanu Jitviriya",{"name":222,"orcid":9},"Eiji Hayashi",{"name":224,"orcid":225},"Aran Blattler","https:\u002F\u002Forcid.org\u002F0000-0002-8684-6239",{"name":227,"orcid":9},"Watcharin Tangsuksant",{"name":229,"orcid":230},"Teppakorn Sittiwanchai","https:\u002F\u002Forcid.org\u002F0000-0001-9653-8750","https:\u002F\u002Fph01.tci-thaijo.org\u002Findex.php\u002Feasr\u002Farticle\u002Fdownload\u002F266223\u002F176569",{"tldr":233,"method":234,"finding":235,"direction":97,"opportunity":236},"提出基于纹理自适应模糊池化机制，提升CNN番茄成熟度分类性能。","用Laplacian滤波分析特征图纹理，动态调整最大最小池化融合比，集成到Dar","模糊池化在多个模型上F1提升，且计算开销几乎不增加。","可探索自适应池化在其它果蔬成熟度或田间复杂光照下的泛化能力。","2026-10-09T23:30:09.993787Z",{"id":239,"title":240,"url":241,"summary":242,"summary_zh":9,"content":9,"source_name":243,"source_url":9,"published_at":244,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":245,"score_detail":246,"sources":249,"tags":251,"primary_topic":254,"secondary_topics":255,"classification":256,"search_phrases":291,"slug":294,"view_count":115,"doi":9,"paper":295,"created_at":302},3960,"Groundbreaking AI Robot Picks Tomatoes Autonomously——YOLOv8n-BiFPN-WIoU模型驱动采摘新范式","https:\u002F\u002Fagritechinsights.com\u002Findex.php\u002F2026\u002F10\u002F02\u002Fgroundbreaking-ai-robot-picks-tomatoes-autonomously","山东理工大学孟凡钊团队开发出基于YOLOv8n-BiFPN-WIoU轻量级深度学习模型的温室番茄自主采摘机器人，可在边缘设备NVIDIA Jetson Nano上运行，优化后达21.1 FPS实时运行；定位误差3.5±0.32mm；mAP@0.5达92.4%。系统采用六轴机械臂+双爪末端执行器，针对温室遮挡与光照变化优化，适用于商业化串番茄采收；系统基于开源ROS模块化设计，未来可适配黄瓜、辣椒、草莓等。","Agritech Insights·2026年10月02日（Frontiers in Plant Science）","2026-10-02T00:00:00Z",86,{"impact":114,"substance":247,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":248},23,"轻量模型+边缘部署的番茄自主采摘机器人，方法新颖、指标扎实，具备细分行业参考价值。",[250],{"name":243,"url":241},[27,28,252,29,253],"农业机器人","番茄采摘",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":257},{"candidates":258,"agricultural_evidence":48},[259,266,272,277,283,287],{"code":33,"score":21,"supported":48,"title":260,"abstract":261,"tags":265},[],[30,262,263,264],"辣椒","黄瓜","草莓",[],{"code":267,"score":55,"supported":48,"title":268,"abstract":269,"tags":271},"F6",[],[270],"采摘机器人",[252],{"code":61,"score":55,"supported":48,"title":273,"abstract":274,"tags":276},[],[275],"深度学习",[28],{"code":278,"score":75,"supported":48,"title":279,"abstract":280,"tags":282},"B7",[],[281],"温室",[],{"code":46,"score":22,"supported":14,"title":284,"abstract":285,"tags":286},[],[],[27],{"code":67,"score":22,"supported":14,"title":288,"abstract":289,"tags":290},[],[],[29],[292,293],"山东理工大学 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FPS实时运行，定位误差3.5±0.32mm，可商业化串番茄采","可延伸至多作物泛化、复杂遮挡与光照鲁棒性提升，以及边缘端模型轻量化与多机协同采摘。","2026-10-02T11:53:16.408068Z",{"id":304,"title":305,"url":306,"summary":307,"summary_zh":9,"content":308,"source_name":309,"source_url":9,"published_at":310,"category":311,"cover_url":9,"hotness":13,"is_selected":14,"score":312,"score_detail":313,"sources":316,"tags":318,"primary_topic":322,"secondary_topics":323,"classification":324,"search_phrases":343,"slug":346,"view_count":55,"doi":9,"paper":9,"created_at":347},3751,"设施技术国产化让每公斤番茄综合生产成本降至5元","https:\u002F\u002Fnync.yn.gov.cn\u002Fhtml\u002F2026\u002Fyunnongkuanxun-new_0923\u002F1428950.html","云南首个AI+农业全链路示范基地位于昆明市宜良县匡远街道温泉社区。嘉措（云南）农业科技有限公司联合云南指月科技有限公司打造国产化AI种植模式，通过核心算法本地化适配与全链条国产化硬件深度协同，基地里每公斤番茄综合生产成本降至5元，仅为海外高端温室模式的三分之一。","位于昆明市宜良县匡远街道温泉社区的番茄示范基地即将在国庆假期进入采收期。记者近日来到该基地，探访这座云南首个“AI+农业全链路”示范基地。\n\n清晨，基地里一串串番茄在简易薄膜温室内泛着诱人的青红光泽。没有泥泞的土地，没有汗流浃背的弯腰劳作，眼前这座“番茄工厂”安静得只听见蜜蜂授粉时的嗡鸣声和水肥滴灌的微响。\n\n上午8时，有工人陆续进场，偌大的基地只需三五位工人就能打理完毕。物联网感知终端静静矗立，实时采集光照强度、空气温湿度、水肥浓度和作物长势。种苗定植、枝蔓生长、开花坐果到成熟采收，每一个环节都有“数字管家”全程值守。嘉措（云南）农业科技有限公司农场场长李良坐在棚外，掏出手机轻点屏幕，棚内温湿度曲线、土壤含水量、水肥配比参数一目了然。指尖滑动间，灌溉阀自动开启，水肥顺着滴灌带精准送达番茄根部。\n\n“过去种番茄靠经验，现在靠算法。”云南指月科技有限公司项目经理尹文鑫指着棚内的国产化智慧灌溉设备说。这套系统由嘉措（云南）农业科技有限公司联合云南指月科技有限公司打造，创新构建“全域监测+AI算法+智慧灌溉”三位一体的数字化种植模式。\n\n“基地里的全套设备都是国产的。”尹文鑫提高音量信心十足地介绍。长期以来，国内高端设施农业赛道被海外技术路线主导。参照国外植物工厂及高端玻璃温室模式，依托全套进口水肥设备与闭环控制系统虽能实现稳定高产，但每公斤番茄综合生产成本高达13元至15元，加之进口设备造价高、配件周期长、操作门槛高等痛点，技术落地难度极大。而该基地探索的国产化AI种植模式，正在彻底改写这一成本结构。\n\n国产化的意义，最终落在基地每一颗番茄的身价上。通过核心算法本地化适配与全链条国产化硬件深度协同，基地里每公斤番茄综合生产成本降至5元，仅为海外高端温室模式的三分之一。同时，国产化硬件的使用，进一步压缩了采购、部署与运维成本，凸显整套方案规模化落地的性价比。\n\n“云”上管棚，“数”里种田。从传感器、灌溉阀到核心控制系统，基地核心硬件已实现全部国产配套；从数据采集、模型训练到智能决策输出，AI技术真正扎根田间。对种植户而言，AI不再是遥远的科技概念，而是手机里能调水肥、棚里能提产量、年底能多增收的实用“新农具”。（记者：王淑娟）","云南省农业农村厅","2026-09-23T12:00:00Z","报道",78,{"impact":114,"substance":114,"depth":177,"authority":314,"freshness":103,"relevant":22,"comment":315},11,"云南首个AI+农业全链路示范基地，以国产化硬件与算法将番茄综合成本从13-15元\u002F公斤降至5元，数据具体、多方信源，具备行业示范价值。",[317],{"name":309,"url":306},[27,28,319,320,30,321],"设施农业","云南","国产化替代",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":325},{"candidates":326,"agricultural_evidence":48},[327,331,335,339],{"code":33,"score":128,"supported":48,"title":328,"abstract":329,"tags":330},[30],[30],[30],{"code":278,"score":75,"supported":48,"title":332,"abstract":333,"tags":334},[],[281],[],{"code":46,"score":22,"supported":14,"title":336,"abstract":337,"tags":338},[],[],[27],{"code":61,"score":22,"supported":14,"title":340,"abstract":341,"tags":342},[],[],[28],[344,345],"云南 宜良 AI番茄 示范基地","嘉措农业 指月科技 智慧灌溉","云南宜良AI番茄示范基地-3751","2026-09-29T00:08:25.735852Z",{"id":349,"title":350,"url":351,"summary":352,"summary_zh":353,"content":9,"source_name":354,"source_url":351,"published_at":355,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":356,"sources":358,"tags":360,"primary_topic":363,"secondary_topics":364,"classification":365,"search_phrases":386,"slug":389,"view_count":62,"doi":390,"paper":391,"created_at":413},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。","Horticulturae","2026-09-23T00:00:00Z",{"impact":195,"substance":114,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":357},"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。",[359],{"name":354,"url":351},[27,28,361,30,362],"目标检测","产量估测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":366},{"candidates":367,"agricultural_evidence":48},[368,372,377,381],{"code":33,"score":47,"supported":48,"title":369,"abstract":370,"tags":371},[],[30,58],[30],{"code":46,"score":47,"supported":48,"title":373,"abstract":374,"tags":376},[],[27,375],"smart agriculture",[27],{"code":61,"score":62,"supported":14,"title":378,"abstract":379,"tags":380},[],[],[28,361],{"code":382,"score":22,"supported":14,"title":383,"abstract":384,"tags":385},"I3",[],[],[362],[387,388],"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","10.3390\u002Fhorticulturae12101199",{"doi":390,"openalex_id":392,"authors":393,"venue":354,"cited_by_count":91,"oa_url":351,"card":408,"direction":99,"ingested_from":100},"W7214123905",[394,396,399,401,403,405],{"name":395,"orcid":9},"Liying Shi",{"name":397,"orcid":398},"Sen Lin","https:\u002F\u002Forcid.org\u002F0000-0001-6521-3152",{"name":400,"orcid":9},"Haihang Zhao",{"name":402,"orcid":9},"Tianlong Sun",{"name":404,"orcid":9},"Dongdong Sun",{"name":406,"orcid":407},"Yuchen Yang","https:\u002F\u002Forcid.org\u002F0000-0001-5977-1617",{"tldr":409,"method":410,"finding":411,"direction":97,"opportunity":412},"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。","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。","2026-09-24T23:30:10.615639Z",{"id":415,"title":416,"url":417,"summary":418,"summary_zh":9,"content":9,"source_name":419,"source_url":9,"published_at":420,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":312,"score_detail":421,"sources":423,"tags":425,"primary_topic":426,"secondary_topics":427,"classification":428,"search_phrases":447,"slug":450,"view_count":62,"doi":9,"paper":451,"created_at":458},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":17,"substance":114,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":422},"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。",[424],{"name":419,"url":417},[27,28,319,30,120],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":429},{"candidates":430,"agricultural_evidence":48},[431,435,439,443],{"code":33,"score":128,"supported":48,"title":432,"abstract":433,"tags":434},[30],[30],[30],{"code":278,"score":75,"supported":48,"title":436,"abstract":437,"tags":438},[],[281],[],{"code":61,"score":62,"supported":14,"title":440,"abstract":441,"tags":442},[],[],[28,120],{"code":46,"score":22,"supported":14,"title":444,"abstract":445,"tags":446},[],[],[27],[448,449],"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":452,"venue":9,"cited_by_count":91,"oa_url":9,"card":453,"direction":97,"ingested_from":164},[],{"tldr":454,"method":455,"finding":456,"direction":97,"opportunity":457},"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"]