[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2264":3,"related-2264":86},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":71,"slug":74,"view_count":75,"doi":8,"paper":76,"created_at":85},2264,"南京农业大学等单位3MSP2多簇番茄序贯采摘规划在Computers and Electronics in Agriculture发表","https:\u002F\u002Fpubscholar.cn\u002Fheadlines\u002F1fe11dc4f9cf2a7db5fd265751febd525e0c4ea77d8edb8ce4d6232ff27da05120c65eab10d9c0dbda65b8d76671d51f","山东农业大学学者在Computers and Electronics in Agriculture发表3MSP2方法,结合\"眼在臂上\"和\"眼在手上\"双RGB-D相机视角,从簇级排序到单果姿态估计的完整决策链,实现温室大果型番茄\"低损伤高效率\"连续采收。",null,"PubScholar 智慧设施种养研究专题","2026-09-08T16:00:00Z","论文",10,false,80,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,14,8,1,"双RGB-D视角簇级序贯采摘规划方法发表于农业工程核心期刊，方法新颖、决策链完整，对温室番茄自动化采收有实质参考价值。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","采摘机器人","番茄","设施园艺",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,49,56,61,66],{"code":31,"score":44,"supported":45,"title":46,"abstract":47,"tags":48},12,true,[28],[28],[28],{"code":50,"score":51,"supported":45,"title":52,"abstract":53,"tags":55},"B7",4,[],[54],"温室",[29],{"code":57,"score":20,"supported":13,"title":58,"abstract":59,"tags":60},"F6",[],[],[27],{"code":62,"score":20,"supported":13,"title":63,"abstract":64,"tags":65},"I1",[],[],[25],{"code":67,"score":20,"supported":13,"title":68,"abstract":69,"tags":70},"I2",[],[],[26],[72,73],"农业人工智能 采摘机器人 智慧农业 设施园艺","农业人工智能 采摘机器人","农业人工智能采摘机器人智慧农业设施园艺-2264",0,{"doi":8,"openalex_id":8,"authors":77,"venue":8,"cited_by_count":75,"oa_url":8,"card":78,"direction":82,"ingested_from":84},[],{"tldr":79,"method":80,"finding":81,"direction":82,"opportunity":83},"提出3MSP2方法，实现温室大果番茄从簇级排序到单果姿态估计的序贯采摘规划。","双RGB-D相机（眼在臂上+眼在手上）与簇级排序、单果姿态估计决策链。","该方法可实现温室大果型番茄低损伤、高效率的连续采收。","农业人工智能与决策模型","可探索多臂协同采摘与动态障碍物下的实时重规划，提升复杂温室环境适应性。","agent","2026-09-13T00:04:03.523312Z",{"total":87,"page":20,"page_size":87,"items":88},6,[89,149,225,274,318,385],{"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":100,"tags":102,"primary_topic":105,"secondary_topics":106,"classification":107,"search_phrases":136,"slug":139,"view_count":128,"doi":140,"paper":141,"created_at":148},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":16,"substance":17,"depth":16,"authority":98,"freshness":19,"relevant":20,"comment":99},13,"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。",[101],{"name":94,"url":92},[25,26,28,103,104],"病害识别","轻量级模型",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":108},{"candidates":109,"agricultural_evidence":45},[110,114,121,127,132],{"code":31,"score":44,"supported":45,"title":111,"abstract":112,"tags":113},[28],[28],[28],{"code":115,"score":116,"supported":45,"title":117,"abstract":118,"tags":120},"A3",3,[],[119],"玉米",[],{"code":122,"score":116,"supported":45,"title":123,"abstract":124,"tags":126},"A5",[],[125],"马铃薯",[],{"code":67,"score":128,"supported":13,"title":129,"abstract":130,"tags":131},2,[],[],[26,103],{"code":62,"score":20,"supported":13,"title":133,"abstract":134,"tags":135},[],[],[25],[137,138],"MambaSeed 番茄叶病","MambaVisionMixer 病害识别","MambaSeed番茄叶病-5301","10.1371\u002Fjournal.pone.0359350",{"doi":140,"openalex_id":8,"authors":142,"venue":8,"cited_by_count":75,"oa_url":8,"card":143,"direction":82,"ingested_from":84},[],{"tldr":144,"method":145,"finding":146,"direction":82,"opportunity":147},"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。","2026-10-19T00:07:48.589614Z",{"id":150,"title":151,"url":152,"summary":153,"summary_zh":154,"content":8,"source_name":155,"source_url":152,"published_at":156,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":157,"score_detail":158,"sources":163,"tags":165,"primary_topic":168,"secondary_topics":169,"classification":175,"search_phrases":194,"slug":197,"view_count":20,"doi":198,"paper":199,"created_at":224},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":19,"substance":159,"depth":160,"authority":44,"freshness":161,"relevant":20,"comment":162},20,17,9,"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。",[164],{"name":155,"url":152},[25,26,28,166,167],"图像识别","自动采摘",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[170],{"code":67,"slug":171,"name":172,"path":173,"parent_code":174},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":38,"locked":13,"version":39,"method":40,"evidence":176},{"candidates":177,"agricultural_evidence":45},[178,184,190],{"code":31,"score":179,"supported":45,"title":180,"abstract":182,"tags":183},15,[181],"tomato",[28,181],[28],{"code":67,"score":19,"supported":45,"title":185,"abstract":186,"tags":189},[],[187,188],"计算机视觉","computer vision",[26,166],{"code":62,"score":20,"supported":13,"title":191,"abstract":192,"tags":193},[],[],[25],[195,196],"番茄 成熟度 分类 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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",77,{"impact":179,"substance":17,"depth":16,"authority":98,"freshness":161,"relevant":20,"comment":328},"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。",[330],{"name":324,"url":321},[25,26,238,28,332],"产量估测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":336},{"candidates":337,"agricultural_evidence":45},[338,343,348,352],{"code":31,"score":339,"supported":45,"title":340,"abstract":341,"tags":342},7,[],[28,181],[28],{"code":62,"score":339,"supported":45,"title":344,"abstract":345,"tags":347},[],[25,346],"smart agriculture",[25],{"code":67,"score":128,"supported":13,"title":349,"abstract":350,"tags":351},[],[],[26,238],{"code":353,"score":20,"supported":13,"title":354,"abstract":355,"tags":356},"I3",[],[],[332],[358,359],"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":361,"openalex_id":363,"authors":364,"venue":324,"cited_by_count":75,"oa_url":321,"card":379,"direction":222,"ingested_from":223},"W7214123905",[365,367,370,372,374,376],{"name":366,"orcid":8},"Liying Shi",{"name":368,"orcid":369},"Sen Lin","https:\u002F\u002Forcid.org\u002F0000-0001-6521-3152",{"name":371,"orcid":8},"Haihang Zhao",{"name":373,"orcid":8},"Tianlong Sun",{"name":375,"orcid":8},"Dongdong Sun",{"name":377,"orcid":378},"Yuchen Yang","https:\u002F\u002Forcid.org\u002F0000-0001-5977-1617",{"tldr":380,"method":381,"finding":382,"direction":82,"opportunity":383},"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":386,"title":387,"url":388,"summary":389,"summary_zh":8,"content":8,"source_name":390,"source_url":8,"published_at":391,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":392,"score_detail":393,"sources":395,"tags":397,"primary_topic":400,"secondary_topics":401,"classification":408,"search_phrases":428,"slug":431,"view_count":432,"doi":8,"paper":433,"created_at":440},3249,"设施番茄采摘机器人识别定位与采摘方法研究——江苏大学雷志龙等 基于改进YOLO v5-HSV融合算法识别准确率95.01%","http:\u002F\u002Fwww.qikanzj.com\u002Fhek\u002Fnyjxxb\u002Fmulu\u002F455758.html","智慧农业(中英文)期刊发表江苏大学雷志龙、刘畅、王权团队研究：针对设施单果番茄采摘需求，设计一款设施番茄智能采摘平台，主要由升降机构、采摘机构、识别与定位系统等部分组成。平台整体结构由低压一体化伺服滚珠丝杠副升降机构、六轴协作机械臂和力控末端执行器组成。基于改进YOLO v5-HSV融合算法来识别检测，通过对H分量进行图像阈值分割，提高对成熟目标果实识别的准确率，有效排除未成熟番茄和枝叶背景的干扰；通过眼在手外的标定方法，使用ZED双目相机进行定位。搭建的设施番茄采摘机样机平台在现场采摘试验中识别准确率达到95.01%，采摘成功率为87.96%，单果平均采摘时间为14.56s。","智慧农业(中英文)·江苏大学","2026-09-22T00:00:00Z",82,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":12,"relevant":20,"comment":394},"核心期刊论文，方法有改进、数据完整，识别准确率与采摘成功率等指标明确，对设施农业智能装备研发有参考价值。",[396],{"name":390,"url":388},[25,26,27,398,399],"机器视觉","设施番茄",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[402,403],{"code":67,"slug":171,"name":172,"path":173,"parent_code":174},{"code":57,"slug":404,"name":405,"path":406,"parent_code":407},"agricultural-robots","机器人与无人农场","\u002Ftopic\u002Finputs-equipment\u002Fagricultural-robots","F",{"status":38,"locked":13,"version":39,"method":40,"evidence":409},{"candidates":410,"agricultural_evidence":45},[411,416,420,424],{"code":67,"score":98,"supported":45,"title":412,"abstract":414,"tags":415},[413],"YOLO",[413],[26,398],{"code":31,"score":44,"supported":45,"title":417,"abstract":418,"tags":419},[28],[28],[399],{"code":57,"score":161,"supported":45,"title":421,"abstract":422,"tags":423},[27],[],[27],{"code":62,"score":51,"supported":45,"title":425,"abstract":426,"tags":427},[],[25],[25],[429,430],"江苏大学 设施番茄 采摘机器人","YOLO v5 番茄 识别定位","江苏大学设施番茄采摘机器人-3249",5,{"doi":8,"openalex_id":8,"authors":434,"venue":8,"cited_by_count":75,"oa_url":8,"card":435,"direction":82,"ingested_from":84},[],{"tldr":436,"method":437,"finding":438,"direction":82,"opportunity":439},"设计设施番茄智能采摘平台，融合改进YOLO v5与HSV实现识别定位与采摘。","改进YOLO v5-HSV融合算法、ZED双目相机眼在手外标定、六轴机械臂力控末","识别准确率95.01%，采摘成功率87.96%，单果平均采摘时间14.56秒。","可探索多果簇、遮挡与弱光环境下识别定位鲁棒性，并优化采摘效率与末端力控。","2026-09-23T00:04:33.401768Z"]