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基于耳标视觉分析的自动奶牛定位跟踪系统。

Automatic Cow Location Tracking System using Ear Tag Visual Analysis.

机构信息

Graduate School of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan.

Interdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki, Miyazaki 889-2192, Japan.

出版信息

Sensors (Basel). 2020 Jun 23;20(12):3564. doi: 10.3390/s20123564.

DOI:10.3390/s20123564
PMID:32586067
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7349613/
Abstract

Nowadays, for numerous reasons, smart farming systems focus on the use of image processing technologies and 5G communications. In this paper, we propose a tracking system for individual cows using an ear tag visual analysis. By using ear tags, the farmers can track specific data for individual cows such as body condition score, genetic abnormalities, etc. Specifically, a four-digit identification number is used, so that a farm can accommodate up to 9999 cows. In our proposed system, we develop an individual cow tracker to provide effective management with real-time upgrading enforcement. For this purpose, head detection is first carried out to determine the cow's position in its related camera view. The head detection process incorporates an object detector called You Only Look Once (YOLO) and is then followed by ear tag detection. The steps involved in ear tag recognition are (1) finding the four-digit area, (2) digit segmentation using an image processing technique, and (3) ear tag recognition using a convolutional neural network (CNN) classifier. Finally, a location searching system for an individual cow is established by entering the ID numbers through the application's user interface. The proposed searching system was confirmed by performing real-time experiments at a feeding station on a farm at Hokkaido prefecture, Japan. In combination with our decision-making process, the proposed system achieved an accuracy of 100% for head detection, and 92.5% for ear tag digit recognition. The results of using our system are very promising in terms of effectiveness.

摘要

如今,出于众多原因,智能农业系统侧重于使用图像处理技术和 5G 通信。在本文中,我们提出了一种使用耳标视觉分析来跟踪个体奶牛的系统。通过使用耳标,农民可以跟踪个体奶牛的特定数据,例如身体状况评分、遗传异常等。具体来说,使用四位识别码,因此一个农场可以容纳多达 9999 头奶牛。在我们提出的系统中,我们开发了一种个体奶牛跟踪器,以提供有效的管理,并实时升级执行。为此,首先进行头部检测,以确定奶牛在相关摄像机视图中的位置。头部检测过程结合了一种称为 You Only Look Once (YOLO) 的目标检测器,然后进行耳标检测。耳标识别涉及以下步骤:(1)找到四位数字区域,(2)使用图像处理技术进行数字分割,(3)使用卷积神经网络 (CNN) 分类器进行耳标识别。最后,通过应用程序的用户界面输入 ID 号,建立了个体奶牛的位置搜索系统。该搜索系统在日本北海道的一个农场的饲养站进行实时实验得到了验证。结合我们的决策过程,所提出的系统对头检测的准确率达到了 100%,对耳标数字识别的准确率达到了 92.5%。就有效性而言,使用我们的系统的结果非常有希望。

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本文引用的文献

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Research on the Vision-Based Dairy Cow Ear Tag Recognition Method.基于视觉的奶牛耳标识别方法研究。
Sensors (Basel). 2024 Mar 29;24(7):2194. doi: 10.3390/s24072194.
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Deep learning strategies with CReToNeXt-YOLOv5 for advanced pig face emotion detection.深度学习策略与 CReToNeXt-YOLOv5 用于高级猪脸情绪检测。
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Sci Rep. 2023 Dec 21;13(1):22843. doi: 10.1038/s41598-023-50343-6.
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