[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2618":3},{"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,"view_count":31,"doi":32,"paper":33,"created_at":64},2618,"Mastitis detection in dairy cows with varying postures using infrared thermography based on YOLOv8n-improved and random forest models","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112430","Current automated mastitis detection methods typically identify dairy cows only in a fixed standing posture, which limits their scope and practical flexibility. To address potential occlusion issues across different postures, we divided the key anatomical regions into three areas (the eye, the back surface of the udder (BSU) and the lower surface of the udder (LSU)) and developed an infrared thermography (IRT)-based automated diagnostic system for cows in various postures within a lactation barn. First, a three-stage image enhancement method was applied to extract contour and texture features from the IRT images. Next, the You Only Look Once v8 Nano (YOLOv8n) model was improved by integrating Dynamic Snake Convolution to better capture subtle and complex texture patterns. We further optimised the weight distribution of contour and texture features using an efficient multi-scale attention module to reduce the loss of critical information in the deep network. Structural improvements included adding a P2 detection head to focus on contour features in the target regions. Finally, we built three machine learning models to diagnose mastitis using the maximum body temperatures of these critical regions. Results showed that the three-stage image enhancement effectively enriched IRT details, strengthened contour and texture features and improved detection confidence for the eye, BSU and LSU by 0.025, 0.05 and 0.04, respectively. The improved YOLOv8n model achieved top performance, with precision (P) of 94.2%, 97.8% and 96.1%; recall (R) of 96.6%, 94.1% and 89.7%; and average precision at an intersection-over-union of 50% (AP@0.5) of 94.3%, 93.7% and 94.2% for the eye, BSU and LSU, respectively. Compared with the baseline YOLOv8n model, the enhanced version improved P, R and AP@0.5 metrics by 2%∼5.4% across the three regions of interest. Among the diagnostic models, random forest achieved the highest accuracy at 92.31%. This method broadens the application of automated mastitis detection and provides a reference framework for building automatic monitoring systems for mastitis in feeder barns.","目前的自动乳腺炎检测方法通常仅能在固定站立姿势下识别奶牛，这限制了其适用范围和实践灵活性。为解决不同姿势下可能出现的遮挡问题，我们将关键解剖区域划分为三个区域（眼部、乳房背面（BSU）和乳房下面（LSU）），并开发了一套基于红外热成像（IRT）的自动化诊断系统，用于泌乳牛舍中不同姿势的奶牛。首先，应用三阶段图像增强方法从IRT图像中提取轮廓和纹理特征。接着，通过集成动态蛇形卷积对YOLOv8n模型进行改进，以更好地捕捉细微复杂的纹理模式。我们进一步利用高效多尺度注意力模块优化轮廓和纹理特征的权重分配，以减少深层网络中关键信息的损失。结构改进包括添加P2检测头以聚焦目标区域的轮廓特征。最后，我们构建了三个机器学习模型，利用这些关键区域的最大体表温度来诊断乳腺炎。结果表明，三阶段图像增强有效丰富了IRT细节，增强了轮廓和纹理特征，并将眼部、BSU和LSU的检测置信度分别提高了0.025、0.05和0.04。改进后的YOLOv8n模型取得了最佳性能，眼部、BSU和LSU的精确率（P）分别为94.2%、97.8%和96.1%；召回率（R）分别为96.6%、94.1%和89.7%；交并比为50%时的平均精度（AP@0.5）分别为94.3%、93.7%和94.2%。与基线YOLOv8n模型相比，增强版本在三个感兴趣区域的P、R和AP@0.5指标上提升了2%∼5.4%。在诊断模型中，随机森林达到了最高准确率92.31%。该方法拓宽了自动乳腺炎检测的应用范围，并为构建饲养牛舍乳腺炎自动监测系统提供了参考框架。",null,"Computers and Electronics in Agriculture","2026-09-15T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"该论文提出基于改进YOLOv8n与随机森林的红外热成像奶牛乳腺炎检测方法，支持多姿态识别，方法新颖、数据详实，对智慧牧场疫病监测有参考价值，值得进入每日精选。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","奶牛养殖","红外热成像","疫病智能诊断",0,"10.1016\u002Fj.compag.2026.112430",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":6,"card":57,"direction":61,"ingested_from":63},"W7213340298",[36,39,41,43,45,48,50,52,54],{"name":37,"orcid":38},"Hang Song","https:\u002F\u002Forcid.org\u002F0009-0008-7352-0855",{"name":40,"orcid":9},"Longwei Guo",{"name":42,"orcid":9},"Hang Shi",{"name":44,"orcid":9},"Miao Wu",{"name":46,"orcid":47},"Hang Xue","https:\u002F\u002Forcid.org\u002F0009-0008-4953-8748",{"name":49,"orcid":9},"Hui Zhang",{"name":51,"orcid":9},"Huize Lv",{"name":53,"orcid":9},"Qiuju Xie",{"name":55,"orcid":56},"Jun Hu","https:\u002F\u002Forcid.org\u002F0009-0003-2743-0641",{"tldr":58,"method":59,"finding":60,"direction":61,"opportunity":62},"用改进YOLOv8n与随机森林，通过红外热成像实现奶牛不同姿态下的乳腺炎自动检测。","三阶段图像增强、动态蛇形卷积改进YOLOv8n、多尺度注意力与P2检测头，随机森","改进模型对眼、乳房后表面、乳房下表面检测AP@0.5达94%左右，随机森林诊断准确率92.31%。","农业人工智能与决策模型","可探索多模态融合与轻量化边缘部署，实现牛舍内多姿态、多疾病的实时连续监测。","openalex","2026-09-16T23:30:02.203663Z"]