[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3065":3,"related-3065":104},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":76,"slug":79,"view_count":80,"doi":81,"paper":82,"created_at":103},3065,"A MULTIMODAL FRAMEWORK FOR LIVESTOCK DETECTION AND EPIZOOTIC MONITORING","https:\u002F\u002Fdoi.org\u002F10.67048\u002Fqxju2026ai128iss7m1055","This study proposes an automated computer vision framework to detect and monitor mixed herds of cattle and sheep. By integrating unmanned aerial vehicle (UAV) imagery with ground-level perspectives, a multimodal dataset was curated. Using this data, a lightweight, single-stage object detector (YOLOv8n), tailored for resource-constrained edge devices, was trained and optimised. The integration of computer vision and UAV technology offers agricultural agencies a scalable tool for proactive epizootic surveillance.","本研究提出了一种自动化计算机视觉框架，用于检测和监测牛与羊的混合畜群。通过将无人机（UAV）影像与地面视角相结合，构建了一个多模态数据集。利用该数据，训练并优化了一个轻量级单阶段目标检测器（YOLOv8n），专为资源受限的边缘设备而设计。计算机视觉与无人机技术的融合为农业机构提供了一种可扩展的工具，用于主动进行动物流行病监测。",null,"Agro ilm","2026-09-18T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":20,"relevant":21,"comment":22},18,20,17,8,1,"多模态无人机视觉框架用于牛羊混群检测与疫病预警，方法具体、面向边缘设备，对智慧畜牧有实用参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","无人机遥感","畜牧养殖","疫病监测",{"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":45},[44,52,58,66,71],{"code":32,"score":13,"supported":45,"title":46,"abstract":47,"tags":51},true,[],[48,49,50],"目标检测","计算机视觉","computer vision",[27],{"code":53,"score":20,"supported":45,"title":54,"abstract":56,"tags":57},"C",[55],"livestock",[],[],{"code":59,"score":60,"supported":45,"title":61,"abstract":62,"tags":65},"C2",6,[],[63,64],"cattle","sheep",[],{"code":67,"score":21,"supported":14,"title":68,"abstract":69,"tags":70},"I1",[],[],[26],{"code":72,"score":21,"supported":14,"title":73,"abstract":74,"tags":75},"I3",[],[],[28],[77,78],"YOLOv8n 牲畜检测","无人机 畜牧 疫病监测","YOLOv8n牲畜检测-3065",2,"10.67048\u002Fqxju2026ai128iss7m1055",{"doi":81,"openalex_id":83,"authors":84,"venue":10,"cited_by_count":94,"oa_url":95,"card":96,"direction":100,"ingested_from":102},"W7213668410",[85,88,91],{"name":86,"orcid":87},"R. N. Khadzhaev","https:\u002F\u002Forcid.org\u002F0009-0001-8124-2399",{"name":89,"orcid":90},"Sa’dulla Avezbayev","https:\u002F\u002Forcid.org\u002F0009-0008-8896-7078",{"name":92,"orcid":93},"Sayfuddin Sharipov","https:\u002F\u002Forcid.org\u002F0000-0002-4232-1369",0,"https:\u002F\u002Fqxjurnal.uz\u002Findex.php\u002Fai\u002Farticle\u002Fdownload\u002F1055\u002F936",{"tldr":97,"method":98,"finding":99,"direction":100,"opportunity":101},"提出多模态计算机视觉框架，用无人机和地面图像检测牛羊并监测疫情。","融合无人机与地面视角构建多模态数据集，训练轻量级YOLOv8n边缘检测器。","轻量级模型可在资源受限设备上实现混合畜群检测，支持主动疫情监测。","智慧农业 \u002F 农业物联网","可探索多物种、复杂地形下的实时边缘检测与疫情早期预警模型优化。","openalex","2026-09-21T23:30:13.271856Z",{"total":60,"page":21,"page_size":60,"items":105},[106,169,238,291,378,442],{"id":107,"title":108,"url":109,"summary":110,"summary_zh":9,"content":9,"source_name":111,"source_url":9,"published_at":112,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":113,"score_detail":114,"sources":119,"tags":121,"primary_topic":123,"secondary_topics":129,"classification":131,"search_phrases":155,"slug":158,"view_count":94,"doi":9,"paper":159,"created_at":168},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",69,{"impact":115,"substance":116,"depth":19,"authority":117,"freshness":60,"relevant":21,"comment":118},16,21,9,"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。",[120],{"name":111,"url":109},[26,27,48,28,122],"棉花病害",{"code":124,"slug":125,"name":126,"path":127,"parent_code":128},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[130],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":132},{"candidates":133,"agricultural_evidence":45},[134,140,147,151],{"code":32,"score":17,"supported":45,"title":135,"abstract":138,"tags":139},[136,137],"深度学习","YOLO",[],[27,48],{"code":124,"score":141,"supported":45,"title":142,"abstract":144,"tags":146},11,[143],"棉花",[145],"cotton",[],{"code":67,"score":21,"supported":14,"title":148,"abstract":149,"tags":150},[],[],[26],{"code":72,"score":21,"supported":14,"title":152,"abstract":153,"tags":154},[],[],[28],[156,157],"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":160,"venue":9,"cited_by_count":94,"oa_url":9,"card":161,"direction":165,"ingested_from":167},[],{"tldr":162,"method":163,"finding":164,"direction":165,"opportunity":166},"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":170,"title":171,"url":172,"summary":173,"summary_zh":174,"content":9,"source_name":175,"source_url":172,"published_at":176,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":177,"score_detail":178,"sources":182,"tags":184,"primary_topic":186,"secondary_topics":187,"classification":192,"search_phrases":210,"slug":213,"view_count":21,"doi":214,"paper":215,"created_at":237},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":17,"substance":179,"depth":17,"authority":180,"freshness":117,"relevant":21,"comment":181},22,14,"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。",[183],{"name":175,"url":172},[26,27,48,29,185],"羊只发情监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[188],{"code":59,"slug":189,"name":190,"path":191,"parent_code":53},"ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants",{"status":39,"locked":14,"version":40,"method":41,"evidence":193},{"candidates":194,"agricultural_evidence":45},[195,200,206],{"code":32,"score":196,"supported":45,"title":197,"abstract":198,"tags":199},13,[137],[137],[27,48],{"code":59,"score":117,"supported":45,"title":201,"abstract":202,"tags":205},[],[203,204,64],"湖羊","绵羊",[],{"code":67,"score":21,"supported":14,"title":207,"abstract":208,"tags":209},[],[],[26],[211,212],"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":214,"openalex_id":216,"authors":217,"venue":175,"cited_by_count":94,"oa_url":172,"card":231,"direction":235,"ingested_from":102},"W7221573347",[218,220,222,224,227,229],{"name":219,"orcid":9},"Yanchao Wang",{"name":221,"orcid":9},"Ming Gao",{"name":223,"orcid":9},"Zhixin Hua",{"name":225,"orcid":226},"Asim Shakeel","https:\u002F\u002Forcid.org\u002F0000-0003-2942-9081",{"name":228,"orcid":9},"Chengyu Ren",{"name":230,"orcid":9},"Kaiying Wang",{"tldr":232,"method":233,"finding":234,"direction":235,"opportunity":236},"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。","农业人工智能与决策模型","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":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":248,"tags":250,"primary_topic":253,"secondary_topics":254,"classification":255,"search_phrases":278,"slug":281,"view_count":21,"doi":282,"paper":283,"created_at":290},4968,"A real-time deep learning method for detection and localization of non-emerged cabbage seedlings","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1984989\u002Ffull","Chen, Han, Liu, Kong, Maity, Peng, Di, Yu and Yu(《Frontiers in Plant Science》, 2026-10-14发表)提出基于无人机的精确、快速、实时未出苗甘蓝幼苗检测与定位方法。Enhanced YOLOv8n通过更高效骨干网络和双向特征融合颈部结构,达到81.4% mAP50-95,较基线提升8%,同时保持实时推理能力。整合Enhanced YOLOv8n、YOLO跟踪算法和图像处理算法,实现鲁棒的实时未出苗幼苗检测与定位方法,未出苗甘蓝幼苗识别准确率达96.9%,且可区分连续未出苗幼苗数量,人工成本降至原1\u002F5。","Frontiers in Plant Science 2026 年 10 月 14 日","2026-10-14T00:00:00Z",82,{"impact":17,"substance":179,"depth":17,"authority":180,"freshness":13,"relevant":21,"comment":247},"方法新颖、数据扎实且落地价值明确，但属细分作物技术进展，未达重大突破层级。",[249],{"name":243,"url":241},[26,27,251,28,252],"甘蓝","幼苗检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":256},{"candidates":257,"agricultural_evidence":45},[258,264,270,274],{"code":32,"score":259,"supported":45,"title":260,"abstract":262,"tags":263},12,[261],"deep learning",[137],[27],{"code":265,"score":266,"supported":45,"title":267,"abstract":268,"tags":269},"B1",4,[],[251],[251],{"code":67,"score":21,"supported":14,"title":271,"abstract":272,"tags":273},[],[],[26],{"code":72,"score":21,"supported":14,"title":275,"abstract":276,"tags":277},[],[],[28],[279,280],"甘蓝 未出苗 幼苗检测","无人机 甘蓝 实时检测","甘蓝未出苗幼苗检测-4968","10.3389\u002Ffpls.2026.1984989\u002Ffull",{"doi":282,"openalex_id":9,"authors":284,"venue":9,"cited_by_count":94,"oa_url":9,"card":285,"direction":165,"ingested_from":167},[],{"tldr":286,"method":287,"finding":288,"direction":165,"opportunity":289},"提出无人机实时检测定位未出苗甘蓝幼苗的深度学习方法，准确率96.9%。","改进YOLOv8n骨干与双向特征融合颈部，结合YOLO跟踪和图像处理。","Enhanced YOLOv8n的mAP50-95达81.4%，未出苗识别准确率96.9%，人工成本","可迁移到其他作物出苗监测，并探索多光谱融合与边缘端轻量化实时部署。","2026-10-15T00:06:08.716058Z",{"id":292,"title":293,"url":294,"summary":295,"summary_zh":296,"content":9,"source_name":297,"source_url":294,"published_at":298,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":299,"score_detail":300,"sources":302,"tags":304,"primary_topic":307,"secondary_topics":308,"classification":309,"search_phrases":337,"slug":340,"view_count":266,"doi":341,"paper":342,"created_at":377},4010,"Intelligent Automated Crop Monitoring System Based on Unmanned Aerial Vehicles and Deep Learning for Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fapp16199747","The rapid development of precision agriculture technologies requires intelligent systems capable of automatically analyzing unmanned aerial vehicle (UAV) imagery and transforming image-processing results into structured information for crop-monitoring decision support. This study develops and computationally validates an integrated framework combining deep learning-based semantic segmentation, RGB-based spatial interpretation, image-space vectorization of candidate RGB heterogeneity zones, and monitoring-priority assessment. Experimental validation was performed using the open-access dataset “High-Resolution RGB Images and Corresponding Masks of Agricultural Fields”, comprising mixed agricultural vegetation scenes. Following data-integrity verification, 326 valid RGB image–reference mask pairs were retained and divided into 21,324 non-overlapping 512 × 512-pixel patches. U-Net-ResNet50, DeepLabV3-ResNet50, and FCN-ResNet50 were evaluated in three repeated source-image-grouped holdout experiments using the full dataset. U-Net-ResNet50 achieved the highest held-out performance, with mean Precision = 0.9946 ± 0.0008, Recall = 0.9931 ± 0.0021, vegetation IoU = 0.9877 ± 0.0020, and Dice = 0.9938 ± 0.0010, and was therefore selected for subsequent spatial analysis. Its segmentation outputs were combined with VARI, ExG, and GLI indices to identify candidate RGB heterogeneity zones, which were converted into image-space vector objects in source-image pixel coordinates. The revised full-data analysis generated 58,206 candidate RGB heterogeneity zones. These regions represent visible canopy heterogeneity rather than physiologically confirmed crop stress and are intended to support targeted field verification and monitoring prioritization. The results demonstrate the feasibility of an end-to-end UAV RGB processing workflow that transforms pixel-level segmentation outputs into structured spatial information for precision-agriculture monitoring and decision support.","精准农业技术的快速发展需要智能系统能够自动分析无人机（UAV）影像，并将图像处理结果转化为结构化信息，以支持作物监测决策。本研究开发并通过计算验证了一个集成框架，该框架结合了基于深度学习的语义分割、基于RGB的空间解译、候选RGB异质性区域的图像空间矢量化以及监测优先级评估。实验验证使用开放获取数据集“High-Resolution RGB Images and Corresponding Masks of Agricultural Fields”进行，该数据集包含混合农业植被场景。经数据完整性验证后，保留了326对有效RGB图像-参考掩膜对，并划分为21,324个不重叠的512 × 512像素图像块。使用完整数据集，在三次重复的以源图像分组的留出实验中评估了U-Net-ResNet50、DeepLabV3-ResNet50和FCN-ResNet50。U-Net-ResNet50取得了最高的留出性能，平均精确率 = 0.9946 ± 0.0008，召回率 = 0.9931 ± 0.0021，植被IoU = 0.9877 ± 0.0020，Dice = 0.9938 ± 0.0010，因此被选用于后续空间分析。其分割输出与VARI、ExG和GLI指数相结合，用于识别候选RGB异质性区域，并将其转换为源图像像素坐标下的图像空间矢量对象。修订后的全数据分析生成了58,206个候选RGB异质性区域。这些区域代表可见的冠层异质性，而非经生理学确认的作物胁迫，旨在支持有针对性的田间验证和监测优先级排序。结果表明，端到端无人机RGB处理工作流程具有可行性，该流程可将像素级分割输出转化为结构化空间信息，用于精准农业监测与决策支持。","Applied Sciences","2026-10-01T00:00:00Z",80,{"impact":17,"substance":179,"depth":17,"authority":196,"freshness":117,"relevant":21,"comment":301},"基于无人机RGB影像与深度学习语义分割的端到端作物监测框架，数据规模大、指标可靠，对精准农业决策支持有实质参考价值。",[303],{"name":297,"url":294},[26,27,28,305,306],"作物监测","图像分割",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":310},{"candidates":311,"agricultural_evidence":45},[312,318,326,333],{"code":32,"score":313,"supported":45,"title":314,"abstract":315,"tags":317},19,[261],[136,261,316],"decision support",[27,306],{"code":67,"score":319,"supported":45,"title":320,"abstract":322,"tags":325},15,[321],"smart agriculture",[323,324],"精准农业","precision agriculture",[26],{"code":327,"score":60,"supported":45,"title":328,"abstract":329,"tags":332},"I4",[],[330,331],"空间分析","spatial analysis",[],{"code":72,"score":80,"supported":14,"title":334,"abstract":335,"tags":336},[],[],[305,28],[338,339],"UAV 作物监测 深度学习","U-Net 植被分割 无人机","UAV作物监测深度学习-4010","10.3390\u002Fapp16199747",{"doi":341,"openalex_id":343,"authors":344,"venue":297,"cited_by_count":94,"oa_url":294,"card":372,"direction":100,"ingested_from":102},"W7215074401",[345,348,351,354,356,358,361,363,366,369],{"name":346,"orcid":347},"Аnatoliy M. Тryhuba","https:\u002F\u002Forcid.org\u002F0000-0001-8014-5661",{"name":349,"orcid":350},"Paweł Kiełbasa","https:\u002F\u002Forcid.org\u002F0000-0003-0249-8626",{"name":352,"orcid":353},"Nazarii Ya. Koval","https:\u002F\u002Forcid.org\u002F0000-0001-7846-2924",{"name":355,"orcid":9},"Inna Tryhuba",{"name":357,"orcid":9},"Oleh Andrushkiv",{"name":359,"orcid":360},"Vitalij Grabovets","https:\u002F\u002Forcid.org\u002F0000-0002-0340-185X",{"name":362,"orcid":9},"Nataliia Kozak",{"name":364,"orcid":365},"Akinniyi Akinsunmade","https:\u002F\u002Forcid.org\u002F0000-0002-1602-1741",{"name":367,"orcid":368},"Anna Miernik","https:\u002F\u002Forcid.org\u002F0000-0003-0394-9185",{"name":370,"orcid":371},"Paweł Pysz","https:\u002F\u002Forcid.org\u002F0000-0001-7083-1078",{"tldr":373,"method":374,"finding":375,"direction":165,"opportunity":376},"构建无人机RGB影像深度学习分割与空间矢量化框架，实现作物监测优先级评估。","U-Net-ResNet50等模型在326张影像21,324个512×512块上","U-Net-ResNet50分割最优（IoU 0.9877），生成58,206个候选异质区用于田间验","可延伸研究：将RGB异质区与多光谱\u002F热红外数据融合，验证生理胁迫并构建实时决策模型。","2026-10-02T23:30:11.747839Z",{"id":379,"title":380,"url":381,"summary":382,"summary_zh":383,"content":9,"source_name":384,"source_url":381,"published_at":298,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":385,"score_detail":386,"sources":388,"tags":390,"primary_topic":393,"secondary_topics":394,"classification":395,"search_phrases":412,"slug":415,"view_count":60,"doi":416,"paper":417,"created_at":441},3970,"PhySinc-Net: A physics-informed interpretable deep learning architecture for non-contact respiratory rate monitoring in dairy goats","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112472","PhySinc-Net: A physics-informed interpretable deep learning architecture for non-contact respiratory rate monitoring in dairy goats。Computers and Electronics in Agriculture","PhySinc-Net：一种用于奶山羊非接触式呼吸频率监测的物理信息可解释深度学习架构。","Computers and Electronics in Agriculture",78,{"impact":115,"substance":116,"depth":17,"authority":180,"freshness":117,"relevant":21,"comment":387},"物理信息驱动的可解释深度学习用于奶山羊非接触呼吸率监测，方法新颖、发表于农业信息领域核心期刊，对智慧畜牧有参考价值。",[389],{"name":384,"url":381},[26,27,29,391,392],"呼吸监测","非接触感知",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":396},{"candidates":397,"agricultural_evidence":45},[398,402,408],{"code":32,"score":319,"supported":45,"title":399,"abstract":400,"tags":401},[261],[136,261],[27],{"code":59,"score":403,"supported":45,"title":404,"abstract":405,"tags":407},3,[],[406],"山羊",[],{"code":67,"score":21,"supported":14,"title":409,"abstract":410,"tags":411},[],[],[26],[413,414],"PhySinc-Net 奶山羊 呼吸率","非接触呼吸监测 奶牛","PhySinc-Net奶山羊呼吸率-3970","10.1016\u002Fj.compag.2026.112472",{"doi":416,"openalex_id":418,"authors":419,"venue":384,"cited_by_count":94,"oa_url":9,"card":436,"direction":100,"ingested_from":102},"W7215068338",[420,422,425,427,429,431,433],{"name":421,"orcid":9},"Wenxuan Lu",{"name":423,"orcid":424},"Rui Mao","https:\u002F\u002Forcid.org\u002F0000-0001-5345-8128",{"name":426,"orcid":9},"Bohan Jiang",{"name":428,"orcid":9},"Jiayi Tian",{"name":430,"orcid":9},"Hanrui Li",{"name":432,"orcid":9},"Xianyao Zhu",{"name":434,"orcid":435},"Mei Li","https:\u002F\u002Forcid.org\u002F0000-0003-2962-8945",{"tldr":437,"method":438,"finding":439,"direction":100,"opportunity":440},"提出物理信息可解释深度学习架构PhySinc-Net，实现奶山羊非接触呼吸率监测。","融合物理先验与深度学习的可解释网络，用于奶山羊呼吸信号分析。","该架构可非接触准确监测奶山羊呼吸率，并具备物理可解释性。","可将物理信息可解释模型迁移至其他畜禽生理监测，解决数据稀缺与黑箱问题。","2026-10-02T23:30:02.169669Z",{"id":443,"title":444,"url":445,"summary":446,"summary_zh":447,"content":9,"source_name":448,"source_url":445,"published_at":449,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":385,"score_detail":450,"sources":452,"tags":454,"primary_topic":457,"secondary_topics":458,"classification":464,"search_phrases":489,"slug":492,"view_count":493,"doi":494,"paper":495,"created_at":520},3478,"Closed-loop artificial intelligence agents for animal epidemic prediction and decision support in livestock farming: a review","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffvets.2026.1968632","Animal diseases continuously threaten livestock production and public health, yet current surveillance approaches remain largely passive and fragmented. Conventional artificial intelligence models typically function as open-loop predictors, delivering static outputs that fail to accommodate the dynamic operational needs of veterinarians in real-world farm settings. This review explores an emerging paradigm of AI agents for animal disease monitoring, emphasizing how such agents can transition from passive observation to active intervention through a closed-loop perception–decision–feedback architecture. We first classify multi-scale sensing technologies: at the micro-scale, automated molecular diagnostics and biosensors; at the macro-scale, computer-vision-based phenotyping. These technologies collectively provide the data streams required for continuous surveillance. The review then discusses how multi-modal inputs, time-series forecasting, resource-aware decision-making, and reinforcement learning can be integrated into an agent-based workflow. Importantly, most current veterinary research evidence supports only the individual components of this framework, rather than the deployment of fully autonomous, iteratively evolving closed-loop agents in actual field environments. Translating closed-loop AI from a conceptual framework into a reliable veterinary decision-support tool will therefore require further advances in validation, standardization, governance, and human oversight.","动物疫病持续威胁着畜牧生产与公共卫生，然而当前的监测手段在很大程度上仍是被动且碎片化的。传统人工智能模型通常作为开环预测器运行，输出静态结果，无法满足兽医在真实养殖场景中动态的操作需求。本文综述了动物疫病监测中新兴的人工智能代理（AI agents）范式，重点探讨此类代理如何通过“感知—决策—反馈”的闭环架构，从被动观察转向主动干预。我们首先对多尺度传感技术进行分类：在微观尺度上，包括自动化分子诊断与生物传感器；在宏观尺度上，包括基于计算机视觉的表型分析。这些技术共同提供了持续监测所需的数据流。随后，本文讨论了多模态输入、时间序列预测、资源感知决策以及强化学习如何整合到基于代理的工作流程中。重要的是，当前大多数兽医研究证据仅支持该框架的各个独立组件，而非在实际现场环境中部署完全自主、迭代演化的闭环代理。因此，要将闭环人工智能从概念框架转化为可靠的兽医决策支持工具，还需在验证、标准化、治理和人工监督方面取得进一步进展。","Frontiers in Veterinary Science","2026-09-23T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":180,"freshness":20,"relevant":21,"comment":451},"系统梳理闭环AI智能体在动物疫病监测与决策中的应用框架，指出落地瓶颈，对智慧畜牧有参考价值。",[453],{"name":448,"url":445},[26,27,455,29,456],"动物疫病防控","智能决策",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[459],{"code":460,"slug":461,"name":462,"path":463,"parent_code":53},"C5","animal-health","疫病防控与兽药","\u002Ftopic\u002Flivestock\u002Fanimal-health",{"status":39,"locked":14,"version":40,"method":41,"evidence":465},{"candidates":466,"agricultural_evidence":45},[467,473,478,485],{"code":32,"score":468,"supported":45,"title":469,"abstract":471,"tags":472},23,[470,316],"artificial intelligence",[49,470],[27],{"code":53,"score":180,"supported":45,"title":474,"abstract":475,"tags":477},[55],[476,55],"畜牧",[],{"code":460,"score":13,"supported":45,"title":479,"abstract":480,"tags":484},[],[481,482,483],"动物疫病","veterinary","animal disease",[455],{"code":67,"score":21,"supported":14,"title":486,"abstract":487,"tags":488},[],[],[26],[490,491],"动物疫病 AI 预测","闭环智能体 畜牧","动物疫病AI预测-3478",5,"10.3389\u002Ffvets.2026.1968632",{"doi":494,"openalex_id":496,"authors":497,"venue":448,"cited_by_count":94,"oa_url":514,"card":515,"direction":100,"ingested_from":102},"W7214118204",[498,501,503,505,508,511],{"name":499,"orcid":500},"Yuzhi Wang","https:\u002F\u002Forcid.org\u002F0009-0001-8566-7810",{"name":502,"orcid":9},"Yingtong Zhou",{"name":504,"orcid":9},"Liyu Li",{"name":506,"orcid":507},"Huiling Xu","https:\u002F\u002Forcid.org\u002F0000-0001-8195-8648",{"name":509,"orcid":510},"Wangze Ni","https:\u002F\u002Forcid.org\u002F0000-0003-1438-1345",{"name":512,"orcid":513},"Xiaoliang Li","https:\u002F\u002Forcid.org\u002F0000-0002-2410-7545","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fveterinary-science\u002Farticles\u002F10.3389\u002Ffvets.2026.1968632\u002Fpdf",{"tldr":516,"method":517,"finding":518,"direction":235,"opportunity":519},"综述闭环AI智能体用于畜禽疫病预测与决策支持，提出感知-决策-反馈架构。","综述多尺度传感、多模态输入、时序预测与强化学习集成。","现有证据仅支持框架组件，闭环智能体尚未在真实农场部署。","可研究闭环智能体在真实农场的验证、标准化与人机协同治理。","2026-09-25T23:30:12.980548Z"]