[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3963":3,"related-3963":89},{"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":31,"secondary_topics":37,"classification":38,"search_phrases":60,"slug":63,"view_count":64,"doi":65,"paper":66,"created_at":88},3963,"MSO-Det: A multi-level synergistic optimization framework for robust spike object detection in field scenes","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112473","MSO-Det: A multi-level synergistic optimization framework for robust spike object detection in field scenes。Computers and Electronics in Agriculture","MSO-Det：一种用于田间场景中鲁棒脉冲目标检测的多级协同优化框架。《计算机与电子农业》",null,"Computers and Electronics in Agriculture","2026-10-01T00:00:00Z","论文",10,false,69,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,18,16,14,9,1,"发表于农业信息领域核心期刊的穗部目标检测新方法，方法有创新但属细分技术进展，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30],"智慧农业","农业人工智能","目标检测","作物表型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,50,55],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},5,true,[],[29],[28,29],{"code":51,"score":22,"supported":14,"title":52,"abstract":53,"tags":54},"I1",[],[],[27],{"code":56,"score":22,"supported":14,"title":57,"abstract":58,"tags":59},"I3",[],[],[30],[61,62],"MSO-Det 穗部检测","田间场景 穗目标检测","MSO-Det穗部检测-3963",3,"10.1016\u002Fj.compag.2026.112473",{"doi":65,"openalex_id":67,"authors":68,"venue":10,"cited_by_count":80,"oa_url":9,"card":81,"direction":85,"ingested_from":87},"W7215045657",[69,71,73,75,77],{"name":70,"orcid":9},"Yiqun Wang",{"name":72,"orcid":9},"Shuo Zhang",{"name":74,"orcid":9},"Xiaolong Liu",{"name":76,"orcid":9},"Ze Ji",{"name":78,"orcid":79},"Weikuan Jia","https:\u002F\u002Forcid.org\u002F0000-0001-6242-3269",0,{"tldr":82,"method":83,"finding":84,"direction":85,"opportunity":86},"提出MSO-Det多级协同优化框架，用于田间场景下鲁棒的穗部目标检测。","多级协同优化框架，结合田间场景图像进行穗目标检测。","该框架提升了田间穗检测的鲁棒性，但摘要未给出具体指标。","农业人工智能与决策模型","可探索多级优化在复杂田间光照与遮挡下的泛化能力及轻量化部署。","openalex","2026-10-02T23:30:01.492947Z",{"total":90,"page":22,"page_size":90,"items":91},6,[92,154,222,293,369,443],{"id":93,"title":94,"url":95,"summary":96,"summary_zh":9,"content":9,"source_name":97,"source_url":9,"published_at":98,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":99,"sources":103,"tags":105,"primary_topic":108,"secondary_topics":114,"classification":116,"search_phrases":140,"slug":143,"view_count":80,"doi":9,"paper":144,"created_at":153},5532,"精准棉花病害管理：融合无人机影像与定制YOLO深度学习框架","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.24182","研究提出单阶段全卷积检测器Cotton-Vision，融合幽灵瓶颈通道注意力与跨阶段部分融合三项轻量化设计，参数减少20%。在巴基斯坦旁遮普省7个棉田采集的2700余幅高分辨率无人机影像上测试，精确率97.7%、召回率77.7%、mAP@50为85.6%，较主流基线提升10至20个百分点。预印本，仅在巴基斯坦单一区域验证。","arXiv 预印本 2026-10-16","2026-10-16T00:00:00Z",{"impact":19,"substance":100,"depth":101,"authority":21,"freshness":90,"relevant":22,"comment":102},21,17,"方法轻量化与实测数据有增量，但仅单区域预印本验证，属细分技术进展，可入精选备选。",[104],{"name":97,"url":95},[27,28,29,106,107],"无人机遥感","棉花病害",{"code":109,"slug":110,"name":111,"path":112,"parent_code":113},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[115],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":117},{"candidates":118,"agricultural_evidence":46},[119,125,132,136],{"code":32,"score":18,"supported":46,"title":120,"abstract":123,"tags":124},[121,122],"深度学习","YOLO",[],[28,29],{"code":109,"score":126,"supported":46,"title":127,"abstract":129,"tags":131},11,[128],"棉花",[130],"cotton",[],{"code":51,"score":22,"supported":14,"title":133,"abstract":134,"tags":135},[],[],[27],{"code":56,"score":22,"supported":14,"title":137,"abstract":138,"tags":139},[],[],[106],[141,142],"Cotton-Vision 棉花病害","无人机影像 棉花 检测","Cotton-Vision棉花病害-5532",{"doi":9,"openalex_id":9,"authors":145,"venue":9,"cited_by_count":80,"oa_url":9,"card":146,"direction":150,"ingested_from":152},[],{"tldr":147,"method":148,"finding":149,"direction":150,"opportunity":151},"提出轻量化检测器Cotton-Vision，用无人机影像精准识别棉花病害。","基于YOLO改进，融合幽灵瓶颈通道注意力与跨阶段部分融合，用2700余幅无人机影","精确率97.7%、mAP@50达85.6%，较主流基线提升10至20个百分点。","农业遥感与作物表型","仅在巴基斯坦单一区域验证，可扩展至多区域多品种，并探索边缘设备实时部署。","agent","2026-10-22T00:07:09.816790Z",{"id":155,"title":156,"url":157,"summary":158,"summary_zh":159,"content":9,"source_name":160,"source_url":157,"published_at":161,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":162,"score_detail":163,"sources":167,"tags":169,"primary_topic":171,"secondary_topics":172,"classification":173,"search_phrases":193,"slug":196,"view_count":80,"doi":197,"paper":198,"created_at":221},5393,"IMPLEMENTATION OF META-HEURISTIC OPTIMIZATION ALGORITHMS IN CONVOLUTIONAL NEURAL NETWORKS FOR OBJECT DETECTION AND CLASSIFICATION","https:\u002F\u002Fdoi.org\u002F10.22190\u002Ffuacr251214006i","CNNs have grown to form the backbone of modern computer vision systems, producing outstanding object detection and classification performance within diverse application domains. However, the manual development of suitable CNN architectures and, correspondingly, the selection of optimal hyperparameter sets are still inconvenient and time-consuming processes. The aim of this survey is to contribute a comprehensive review regarding how meta-heuristic optimization algorithms such as Genetic Algorithms, Particle Swarm Optimization, and other swarm intelligence-based methods are embedded in the CNN-based pipelines with the intent to achieve better detection and classification results. We gather recent progress regarding automated architecture search, hyperparameter tuning, model compression, and ensemble learning. We identify how hybrid optimization approaches consistently yield superior accuracy with computational efficiency by systematically reviewing applications in medical imaging, agriculture, vehicular recognition, and remote sensing. Optimized CNN models outperform their baseline versions many times, especially in health diagnostics, where several fine-tuned architectures reach accuracies greater than 99%. This survey provides a structured and updated overview of methodological developments, implementation practices, and emerging research directions for those practitioners and researchers who will apply meta-heuristic optimization within deep learning-based computer vision systems.","卷积神经网络（CNNs）已发展成为现代计算机视觉系统的核心骨干，在多个应用领域中展现出卓越的目标检测与分类性能。然而，人工设计合适的CNN架构并相应地选择最优超参数集仍然是繁琐且耗时的过程。本综述旨在全面回顾遗传算法、粒子群优化及其他群体智能方法等元启发式优化算法如何嵌入基于CNN的流程中以获得更优的检测与分类结果。我们汇集了自动化架构搜索、超参数调优、模型压缩和集成学习方面的最新进展。通过系统梳理在医学影像、农业、车辆识别和遥感领域的应用，我们揭示了混合优化方法如何以计算效率持续实现更优的精度。优化后的CNN模型在多数情况下远超其基线版本，尤其在健康诊断领域，若干精细调优的架构达到了超过99%的准确率。本综述为将在基于深度学习的计算机视觉系统中应用元启发式优化的从业者和研究者，提供了关于方法学进展、实施实践和新兴研究方向的结构化且更新的概述。","Facta Universitatis Series Automatic Control and Robotics","2026-10-18T00:00:00Z",61,{"impact":164,"substance":18,"depth":165,"authority":17,"freshness":164,"relevant":22,"comment":166},8,15,"综述系统梳理元启发式优化在CNN视觉任务中的嵌入方法，含农业应用，但属通用方法综述，非农业专属突破。",[168],{"name":160,"url":157},[27,28,29,170],"遥感",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":174},{"candidates":175,"agricultural_evidence":46},[176,183,189],{"code":32,"score":126,"supported":46,"title":177,"abstract":178,"tags":182},[],[29,121,179,180,181],"计算机视觉","deep learning","computer vision",[28,29],{"code":56,"score":184,"supported":46,"title":185,"abstract":186,"tags":188},7,[],[170,187],"remote sensing",[170],{"code":51,"score":22,"supported":14,"title":190,"abstract":191,"tags":192},[],[],[27],[194,195],"元启发式优化 卷积神经网络","CNN 目标检测","元启发式优化卷积神经网络-5393","10.22190\u002Ffuacr251214006i",{"doi":197,"openalex_id":199,"authors":200,"venue":160,"cited_by_count":80,"oa_url":157,"card":216,"direction":150,"ingested_from":87},"W7222920697",[201,204,207,210,213],{"name":202,"orcid":203},"Nikola Ivačko","https:\u002F\u002Forcid.org\u002F0000-0003-4367-119X",{"name":205,"orcid":206},"Ivan S Ciric","https:\u002F\u002Forcid.org\u002F0000-0003-0430-8937",{"name":208,"orcid":209},"Milica Tasić","https:\u002F\u002Forcid.org\u002F0009-0001-9160-6379",{"name":211,"orcid":212},"Ljiljana Radović","https:\u002F\u002Forcid.org\u002F0000-0002-4099-1289",{"name":214,"orcid":215},"Žarko Ćojbašić","https:\u002F\u002Forcid.org\u002F0000-0002-4581-1048",{"tldr":217,"method":218,"finding":219,"direction":85,"opportunity":220},"综述元启发式算法嵌入CNN流程以优化目标检测与分类的研究进展。","系统综述遗传算法、粒子群等群智能方法用于CNN架构搜索与超参调优。","混合优化方法在医学影像、农业等领域兼顾精度与效率，部分模型准确率超99%。","农业场景下元启发式优化CNN的架构搜索与轻量化部署仍缺乏系统基准与田间验证。","2026-10-20T23:30:19.233167Z",{"id":223,"title":224,"url":225,"summary":226,"summary_zh":227,"content":9,"source_name":228,"source_url":225,"published_at":229,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":230,"score_detail":231,"sources":234,"tags":236,"primary_topic":239,"secondary_topics":240,"classification":247,"search_phrases":266,"slug":269,"view_count":22,"doi":270,"paper":271,"created_at":292},4975,"Detection of mounting behavior and mounted ewes in group-housed environments using HSM-YOLO algorithm","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102639","The detection of mounting behavior and the mounted ewe is critical for estrus monitoring in sheep. However, existing methods suffer from low detection efficiency and high false detection rates due to the similarity between non-sexual and sexual mounting behaviors and the complexity of group-housed environments. To address these challenges, this study introduced a fine-grained classification of mounting behaviors into sexual and non-sexual categories, and further proposed an improved detection algorithm named Hu Sheep Mounting-YOLO (HSM-YOLO) based on the YOLOv11n architecture for detecting sheep mounting behavior and the corresponding mounted ewes. The HSM-YOLO algorithm employed depthwise separable convolution (DWSConv) to reduce the model complexity. To improve behavioral feature extraction and pose discrimination capabilities, it further integrated the C3k2_RepViTBlock-RepBottleneck (C3k2_BiRep) module, an efficient multi-scale attention (EMA) module, and the Inner-SIoU loss function. To evaluate the performance of the proposed algorithm, comparative experiments were conducted on a multiple scenarios dataset. The experimental results showed that the HSM-YOLO model achieved an F1 score of 88.1%, a mean Average Precision (mAP) of 90.2%, with 2.2M parameters and 5.4G floating-point operations (FLOPs). Compared to the baseline YOLOv11n model, HSM-YOLO improved the F1 score by 5.8% and the mAP by 5.7%, while reducing both model parameters and computational complexity. When deployed and tested on an RTX 4090, the system delivered an average detection speed of 47.9 frames per second (FPS). Moreover, despite considerable variation in detection performance across the four different imaging scenarios, HSM-YOLO consistently demonstrated superior detection accuracy. These results confirm that the HSM-YOLO provides a robust and efficient solution for detecting sheep mounting behaviors and mounted ewes in complex and variable scenarios, paving the way for the development of automated estrus monitoring systems.","检测羊只的爬跨行为及被爬跨母羊对于绵羊发情监测至关重要。然而，由于非性爬跨行为与性爬跨行为之间的相似性以及群养环境的复杂性，现有方法存在检测效率低和误检率高的问题。为解决这些挑战，本研究将爬跨行为细粒度分类为性爬跨和非性爬跨两类，并进一步提出了一种基于YOLOv11n架构的改进检测算法——湖羊爬跨-YOLO（HSM-YOLO），用于检测绵羊爬跨行为及相应的被爬跨母羊。HSM-YOLO算法采用深度可分离卷积（DWSConv）以降低模型复杂度。为提高行为特征提取和姿态判别能力，该算法进一步集成了C3k2_RepViTBlock-RepBottleneck（C3k2_BiRep）模块、高效多尺度注意力（EMA）模块以及Inner-SIoU损失函数。为评估所提算法的性能，在多场景数据集上进行了对比实验。实验结果表明，HSM-YOLO模型的F1分数达到88.1%，平均精度均值（mAP）达到90.2%，参数量为2.2M，浮点运算量为5.4G。与基线YOLOv11n模型相比，HSM-YOLO的F1分数提高了5.8%，mAP提高了5.7%，同时降低了模型参数量和计算复杂度。在RTX 4090上部署测试时，系统平均检测速度达到47.9帧每秒（FPS）。此外，尽管在四种不同成像场景下检测性能存在较大差异，HSM-YOLO始终展现出优越的检测精度。这些结果证实，HSM-YOLO为复杂多变场景下检测绵羊爬跨行为和被爬跨母羊提供了一种鲁棒且高效的解决方案，为自动化发情监测系统的开发奠定了基础。","Smart Agricultural Technology","2026-10-12T00:00:00Z",81,{"impact":18,"substance":232,"depth":18,"authority":20,"freshness":21,"relevant":22,"comment":233},22,"提出HSM-YOLO算法实现羊只爬跨行为与受体母羊检测，为自动化发情监测提供高效方案，方法新颖、指标扎实，具行业参考价值。",[235],{"name":228,"url":225},[27,28,29,237,238],"畜牧养殖","羊只发情监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[241],{"code":242,"slug":243,"name":244,"path":245,"parent_code":246},"C2","ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants","C",{"status":39,"locked":14,"version":40,"method":41,"evidence":248},{"candidates":249,"agricultural_evidence":46},[250,255,262],{"code":32,"score":251,"supported":46,"title":252,"abstract":253,"tags":254},13,[122],[122],[28,29],{"code":242,"score":21,"supported":46,"title":256,"abstract":257,"tags":261},[],[258,259,260],"湖羊","绵羊","sheep",[],{"code":51,"score":22,"supported":14,"title":263,"abstract":264,"tags":265},[],[],[27],[267,268],"HSM-YOLO 羊只爬跨行为 检测","绵羊 发情监测 目标检测","HSM-YOLO羊只爬跨行为检测-4975","10.1016\u002Fj.atech.2026.102639",{"doi":270,"openalex_id":272,"authors":273,"venue":228,"cited_by_count":80,"oa_url":225,"card":287,"direction":85,"ingested_from":87},"W7221573347",[274,276,278,280,283,285],{"name":275,"orcid":9},"Yanchao Wang",{"name":277,"orcid":9},"Ming Gao",{"name":279,"orcid":9},"Zhixin Hua",{"name":281,"orcid":282},"Asim Shakeel","https:\u002F\u002Forcid.org\u002F0000-0003-2942-9081",{"name":284,"orcid":9},"Chengyu Ren",{"name":286,"orcid":9},"Kaiying Wang",{"tldr":288,"method":289,"finding":290,"direction":85,"opportunity":291},"提出HSM-YOLO算法，在群养环境下检测羊爬跨行为及被爬跨母羊，用于发情监测。","基于YOLOv11n改进，引入DWSConv、C3k2_BiRep、EMA和In","F1达88.1%、mAP达90.2%，参数量2.2M，较基线提升且更轻量，检测速度47.9 FPS。","可延伸至多物种发情行为细粒度识别与轻量化边缘部署，结合时序信息提升监测实时性。","2026-10-15T23:30:03.393878Z",{"id":294,"title":295,"url":296,"summary":297,"summary_zh":298,"content":9,"source_name":299,"source_url":296,"published_at":300,"category":12,"cover_url":9,"hotness":13,"is_selected":46,"score":301,"score_detail":302,"sources":304,"tags":306,"primary_topic":309,"secondary_topics":310,"classification":311,"search_phrases":336,"slug":339,"view_count":325,"doi":340,"paper":341,"created_at":368},4055,"A collaborative perception framework for green jujube: from lightweight key point detection to posture-aware harvest analysis","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12870-026-09842-7","Abstract Automated harvesting is pivotal to smart agriculture, yet precisely localizing picking points in complex orchard environments remains a key challenge for robotic harvesting systems. To this end, this study proposes a collaborative perception framework integrating a lightweight pose estimation model termed RepViT-DSConv-ARC-PoseHead enhanced YOLO (RDA-YOLO) with Segment Anything Model 2 (SAM2). RDA-YOLO is the lightweight component responsible for real-time fruit detection and keypoint localization, while SAM2 is adopted for fruit contour segmentation and occlusion assessment. Based on the YOLOv11n-POSE architecture, we introduce Distribution Shift Convolution (DSConv) to reduce computational overhead and design the C3k2_RepViT module to enhance multi-scale feature fusion. Crucially, a specialized Adaptive Regression Coordination Pose Head (ARC-PoseHead) is developed to replace traditional prediction structures, employing log-scale polar regression and subpixel refinement to significantly improve coordinate perception of jujubes and key points under complex backgrounds. RDA-YOLO performs real-time detection and locates key points such as the pedicel-fruit transition zone to calculate optimal harvesting angles, while SAM2 performs fine-grained contour segmentation based on detection boxes to determine occlusion status and establish harvesting priority. Experimental results show that RDA-YOLO achieves 93.9% precision and 94.9% mAP@50 for green jujube detection, while for key point detection, it attains a superior precision of 90.4% and a mAP@50 of 83.6%. Notably, the model achieves a lightweight design by reducing parameters to 2.490 million and computational load to 5.7 GFLOPs. Deployment feasibility was verified on the field robot control computer and a robotic platform using jujube fruit models, demonstrating the potential of the proposed framework for practical harvesting applications.","自动采摘是智慧农业的关键环节，但在复杂果园环境中精准定位采摘点仍是机器人采摘系统面临的核心挑战。为此，本研究提出了一种协同感知框架，将轻量级姿态估计模型RDA-YOLO（RepViT-DSConv-ARC-PoseHead增强型YOLO）与Segment Anything Model 2（SAM2）相结合。RDA-YOLO作为轻量级组件，负责实时果实检测与关键点定位；SAM2则用于果实轮廓分割与遮挡评估。基于YOLOv11n-POSE架构，我们引入分布偏移卷积（DSConv）以降低计算开销，并设计C3k2_RepViT模块以增强多尺度特征融合。关键在于，我们开发了专用的自适应回归协调姿态头（ARC-PoseHead）以替代传统预测结构，采用对数尺度极坐标回归和亚像素细化，显著提升了复杂背景下枣果及关键点的坐标感知能力。RDA-YOLO执行实时检测并定位果柄-果实过渡区等关键点以计算最优采摘角度，而SAM2基于检测框进行细粒度轮廓分割，以判定遮挡状态并建立采摘优先级。实验结果表明，RDA-YOLO对青枣检测的精确率达到93.9%，mAP@50为94.9%；在关键点检测方面，精确率达到90.4%，mAP@50为83.6%。值得注意的是，该模型通过将参数量降至249万、计算量降至5.7 GFLOPs，实现了轻量化设计。在田间机器人控制计算机及使用枣果模型的机器人平台上验证了部署可行性，展示了所提框架在实际采摘应用中的潜力。","BMC Plant Biology","2026-10-03T00:00:00Z",82,{"impact":18,"substance":232,"depth":18,"authority":20,"freshness":13,"relevant":22,"comment":303},"提出轻量化姿态估计与SAM2协同感知框架，实现青枣采摘点定位与遮挡评估，方法新颖、指标扎实，对智能采收有实用价值。",[305],{"name":299,"url":296},[27,28,29,307,308],"采摘机器人","青枣",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":312},{"candidates":313,"agricultural_evidence":46},[314,319,323,330],{"code":51,"score":184,"supported":46,"title":315,"abstract":316,"tags":318},[],[27,317],"smart agriculture",[27],{"code":32,"score":45,"supported":46,"title":320,"abstract":321,"tags":322},[],[122],[28,29],{"code":324,"score":325,"supported":46,"title":326,"abstract":327,"tags":329},"F6",4,[],[328],"robotic harvesting",[307],{"code":331,"score":64,"supported":46,"title":332,"abstract":333,"tags":335},"B2",[],[334],"orchard",[],[337,338],"RDA-YOLO SAM2 青枣 采摘","青枣 关键点检测 采摘机器人","RDA-YOLOSAM2青枣采摘-4055","10.1186\u002Fs12870-026-09842-7",{"doi":340,"openalex_id":342,"authors":343,"venue":299,"cited_by_count":80,"oa_url":296,"card":362,"direction":367,"ingested_from":87},"W7216260984",[344,347,350,352,354,356,358,360],{"name":345,"orcid":346},"Hongyan Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-8703-6289",{"name":348,"orcid":349},"Runlong Cao","https:\u002F\u002Forcid.org\u002F0009-0002-9535-4946",{"name":351,"orcid":9},"Shuai Qin",{"name":353,"orcid":9},"Chengzhi Lin",{"name":355,"orcid":9},"Weizhe Sun",{"name":357,"orcid":9},"Yanli Zou",{"name":359,"orcid":9},"Zhixian Liao",{"name":361,"orcid":9},"Yong He",{"tldr":363,"method":364,"finding":365,"direction":85,"opportunity":366},"提出RDA-YOLO与SAM2协同感知框架，实现青枣采摘点定位与姿态分析。","改进YOLOv11n-POSE，引入DSConv、RepViT与ARC-Pose","检测精度93.9%、关键点精度90.4%，模型仅2.49M参数、5.7 GFLOPs。","可探索多作物通用轻量姿态估计与遮挡感知采摘决策的迁移研究。","智慧农业 \u002F 农业物联网","2026-10-03T23:30:09.616335Z",{"id":370,"title":371,"url":372,"summary":373,"summary_zh":374,"content":9,"source_name":10,"source_url":372,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":375,"score_detail":376,"sources":379,"tags":381,"primary_topic":384,"secondary_topics":389,"classification":391,"search_phrases":409,"slug":412,"view_count":405,"doi":413,"paper":414,"created_at":442},3986,"YOLOv8-MST: a lightweight and efficient real-time detection and counting deep learning algorithm for double-grain peanut seeding","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112481","YOLOv8-MST: a lightweight and efficient real-time detection and counting deep learning algorithm for double-grain peanut seeding。Computers and Electronics in Agriculture","YOLOv8-MST：一种用于双粒花生播种的轻量高效实时检测与计数深度学习算法。《计算机与电子农业》",72,{"impact":17,"substance":377,"depth":101,"authority":20,"freshness":21,"relevant":22,"comment":378},20,"核心期刊发表的轻量化实时检测计数算法，对花生精量播种智能化有实用价值，但属细分技术进展，影响面有限。",[380],{"name":10,"url":372},[27,28,29,382,383],"精准农业","花生播种",{"code":385,"slug":386,"name":387,"path":388,"parent_code":113},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds",[390],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":392},{"candidates":393,"agricultural_evidence":46},[394,398,404],{"code":32,"score":19,"supported":46,"title":395,"abstract":396,"tags":397},[180],[121,180],[28,29],{"code":385,"score":20,"supported":46,"title":399,"abstract":401,"tags":403},[400],"peanut",[402,400],"花生",[],{"code":51,"score":405,"supported":14,"title":406,"abstract":407,"tags":408},2,[],[],[27,382],[410,411],"YOLOv8-MST 花生播种","双粒花生 实时检测计数","YOLOv8-MST花生播种-3986","10.1016\u002Fj.compag.2026.112481",{"doi":413,"openalex_id":415,"authors":416,"venue":10,"cited_by_count":80,"oa_url":9,"card":437,"direction":85,"ingested_from":87},"W7215133023",[417,420,422,424,426,428,430,432,435],{"name":418,"orcid":419},"Baogang Li","https:\u002F\u002Forcid.org\u002F0000-0002-3430-7508",{"name":421,"orcid":9},"Enxi Liu",{"name":423,"orcid":9},"Yijia Chen",{"name":425,"orcid":9},"Zhipeng Feng",{"name":427,"orcid":9},"Yuhuan Li",{"name":429,"orcid":9},"Zunmin Liu",{"name":431,"orcid":9},"Jingchun Tang",{"name":433,"orcid":434},"Jiang Liu","https:\u002F\u002Forcid.org\u002F0000-0001-6746-9987",{"name":436,"orcid":9},"Fazhan Yang",{"tldr":438,"method":439,"finding":440,"direction":85,"opportunity":441},"提出YOLOv8-MST轻量算法，实现双粒花生播种实时检测与计数。","基于YOLOv8改进的轻量深度学习模型，用于花生播种图像检测计数。","YOLOv8-MST在保持高精度的同时实现轻量化实时检测与计数。","可探索轻量模型在多种作物播种质量实时监测中的泛化与边缘部署。","2026-10-02T23:30:03.835851Z",{"id":444,"title":445,"url":446,"summary":447,"summary_zh":448,"content":9,"source_name":449,"source_url":446,"published_at":450,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":451,"score_detail":452,"sources":454,"tags":456,"primary_topic":459,"secondary_topics":460,"classification":461,"search_phrases":477,"slug":480,"view_count":45,"doi":481,"paper":482,"created_at":497},3667,"Real-Time Detection and Yield Estimation of Rosa damascena Using YOLOv10-N: A Case Study in the Dades Oasis, Drâa-Tafilalet, Southeastern Morocco","https:\u002F\u002Fdoi.org\u002F10.22194\u002Fjgias\u002F27.1790","Rosa damascena is an important economic resource in the Drâa Tafilalet region of southeast Morocco, particularly within the Dades Oasis in Kelaat M’Gouna. Detection and yield estimation of Rosa damascena flowers are important for increasing crop productivity and enhancing the local economy. In this work, we explored the application of YOLOv10-N, a deep learning model, for the real-time detection of Rosa damascena under varied environmental conditions. Utilizing a dataset of 2,807 annotated images captured under diverse lighting and terrain conditions, YOLOv10-N achieved an accuracy of 92.12%, recall of 77.22%, mAP50 of 90.65%, and mAP50-95 of 63.93%, demonstrating its robustness across varied environmental settings. In this study, we also present a novel method for estimating yield based on detected flower counts and average weight, providing a practical tool for better monitoring of rose fields, reducing labor costs, and optimizing resource management. These results demonstrate the importance of precision agriculture in addressing broader difficulties in agricultural productivity and economic sustainability. Keywords: YOLOv10, Rosa damascena, object detection, yield estimation, deep learning, computer vision in agriculture.","大马士革玫瑰（Rosa damascena）是摩洛哥东南部德拉-塔菲拉勒特地区的重要经济资源，尤其是在凯拉特姆古纳的达德斯绿洲。大马士革玫瑰花朵的检测与产量估测对于提高作物生产力和促进当地经济具有重要意义。在本研究中，我们探索了深度学习模型YOLOv10-N在不同环境条件下对大马士革玫瑰进行实时检测的应用。利用在多样光照和地形条件下采集的2,807张标注图像数据集，YOLOv10-N达到了92.12%的准确率、77.22%的召回率、90.65%的mAP50以及63.93%的mAP50-95，表明其在不同环境设置下具有稳健性。在本研究中，我们还提出了一种基于检测到的花朵数量和平均重量来估测产量的新方法，为更好地监测玫瑰田、降低劳动力成本和优化资源管理提供了实用工具。这些结果证明了精准农业在应对农业生产力和经济可持续性方面更广泛难题中的重要性。关键词：YOLOv10，大马士革玫瑰，目标检测，产量估测，深度学习，农业计算机视觉。","Journal of Global Innovations in Agricultural Sciences","2026-09-25T00:00:00Z",68,{"impact":17,"substance":377,"depth":19,"authority":17,"freshness":164,"relevant":22,"comment":453},"基于YOLOv10-N的玫瑰实时检测与产量估算研究，数据集规模可观、指标完整，但属区域性案例，产业影响有限。",[455],{"name":449,"url":446},[27,28,29,457,458],"产量估算","大马士革玫瑰",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":462},{"candidates":463,"agricultural_evidence":46},[464,468,473],{"code":32,"score":126,"supported":46,"title":465,"abstract":466,"tags":467},[],[29,121,179,180,181],[28,29],{"code":51,"score":184,"supported":46,"title":469,"abstract":470,"tags":472},[],[382,471],"precision agriculture",[27],{"code":56,"score":22,"supported":14,"title":474,"abstract":475,"tags":476},[],[],[457],[478,479],"YOLOv10 大马士革玫瑰 产量估算","摩洛哥 Dades 绿洲 玫瑰检测","YOLOv10大马士革玫瑰产量估算-3667","10.22194\u002Fjgias\u002F27.1790",{"doi":481,"openalex_id":483,"authors":484,"venue":449,"cited_by_count":80,"oa_url":491,"card":492,"direction":150,"ingested_from":87},"W7214280976",[485,488],{"name":486,"orcid":487},"Mohamed Ohamouddou","https:\u002F\u002Forcid.org\u002F0009-0000-9369-2891",{"name":489,"orcid":490},"Said Ohamouddou","https:\u002F\u002Forcid.org\u002F0009-0006-9175-9769","https:\u002F\u002Fjgiass.com\u002Fpdf-reader.php?file=Real-Time-Detection-and-Yield-Estimation-of-Rosa-damascena-Using-YOLOv10-N%3A-A-Case-Study-in-the-Dades-Oasis%2C-Dr%C3%A2a-Tafilalet%2C-Southeastern-Morocco.pdf&path=issue_papers",{"tldr":493,"method":494,"finding":495,"direction":85,"opportunity":496},"用YOLOv10-N实时检测大马士革玫瑰并基于花数估算产量。","2807张多光照地形标注图像训练YOLOv10-N，结合花数与均重估产。","检测精度92.12%、mAP50为90.65%，可跨环境稳健估产。","可扩展到多品种、多生长阶段及无人机边缘部署的实时估产研究。","2026-09-28T23:30:20.312517Z"]