Automatic Cow Location Tracking System Using Ear Tag Visual Analysis
Automatic Cow Location Tracking System Using Ear Tag Visual Analysis
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DOI：
10.3390/s20123564
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发表时间：
2020-06-01
期刊：
影响因子：
3.9
通讯作者：
Yoshida, Kyohiro
中科院分区：
文献类型：
--
作者：
Zin, Thi Thi;Pwint, Moe Zet;Yoshida, Kyohiro
如今，出于多种原因，智能农业系统专注于图像处理技术和5G通信的使用。在本文中，我们提出了一种基于耳标视觉分析的奶牛个体跟踪系统。通过使用耳标，农民可以跟踪每头奶牛的特定数据，如身体状况评分、遗传异常等。具体而言，使用了四位数的识别号，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，我们开发了一个个体奶牛跟踪器，以提供有效的管理和实时的升级执行。为此，首先执行头部检测，以确定奶牛在其相关摄像机视图中的位置。头部检测过程结合了名为You Only Look Once(YOLO)的对象检测器，然后是耳标检测。在耳标识别中涉及的步骤是(1)找到四位数区域，(2)使用图像处理技术进行数字分割，以及(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号来建立单个奶牛的位置搜索系统。通过在日本北海道一个农场的饲养站进行实时实验，证实了所提出的搜索系统。结合我们的决策过程，该系统对头部检测的准确率为100%，对耳标数字识别的准确率为92.5%。使用我们的系统的结果是非常有希望的有效性。
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.