评价基于摄像机的奶牛运动和身体状况评分系统的奶牛识别可靠性

D. Swartz, E. Shepley, G. Cramer
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引用次数: 0

摘要

奶牛场的规模在不断扩大,而奶牛场的总数却在减少。随着牛群规模的增长,越来越多地采用技术来监测和管理奶牛。然而,技术要取代主观的、劳动密集型的任务,需要精确的技术。人们正在探索基于摄像头的系统,以改善管理,特别是在跛行和身体状况评分方面。基于相机的技术的一个关键要求是它能够准确地识别被观察的动物。本研究的主要目的是评估基于摄像机的奶牛运动评分和身体状况评分技术的奶牛识别(ID)准确性。次要目标是确定初始识别所需的天数,并评估动物识别的频率。对明尼苏达州的两个奶牛场进行了两次访问,从a点(n = 40)和B点(n = 65)共招募了105头奶牛。每头奶牛都记录了它们的奶牛ID和射频识别。所有在围栏中待了5天或更短时间的奶牛都参加了这项研究,并在它们的臀部上画上了颜色、字母和数字的组合,作为唯一标识符(油漆ID;PID)。每天从公司网站上观察基于摄像头的视频馈送,持续7天。如果网站上上传的奶牛ID视频与PID相匹配,则记录为正确识别奶牛。成功识别的定义是在研究开始后7天内将视频上传到用户平台的奶牛ID的比例,而不考虑准确性。正确的识别随后被计算为这些成功识别的奶牛中具有与其PID相对应的PID的比例。摄像机下的天数是通过包括奶牛在我们7天观察期之前暴露在摄像机下的时间来计算的。此外,识别的天数反映了在观察期间奶牛被识别(正确或不正确)和评分的总天数。在入选的103头奶牛中,87头(84.5%;95% CI: 76%-91%),在7 d的研究期间成功鉴定。这87头牛中有一头被错误识别,导致98.9%的正确识别(95% CI: 94%-100%)。在正确识别的86头奶牛中,所有奶牛都在摄像机下观察了第4天至第11天。在被识别的奶牛中,它们被识别了1到7次。该技术准确地识别了奶牛,但16头奶牛最初没有被识别出来,在摄像机下的最小和最大时间分别为7天和11天。为了在哺乳期早期或新奶牛进入牛群时做出管理决策,该技术需要在接触摄像机的第一周内准确识别所有奶牛。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating cow identification reliability of a camera-based locomotion and body condition scoring system in dairy cows
Dairy farms are growing in size, while the total number of farms is decreasing. As herd sizes grow, technology is increasingly adopted for monitoring and managing cows. However, for technology to replace subjective and labor-intensive tasks, accurate technologies are needed. Camera-based systems are being explored for improving management, particularly around lameness and body condition scoring. A key requirement a camera-based technology is its ability to accurately identify the animal being observed. The primary objective of this study was to evaluate the cow identification (ID) accuracy of a camera-based technology that locomotion scores and body condition scores dairy cattle. Secondary objectives were to determine the number of days required for initial identification and evaluate the frequency of animal recognition. Two dairy sites in Minnesota were visited twice and a total of 105 cows were enrolled from site A (n = 40) and site B (n = 65). Each cow had their cow ID and radio frequency identification recorded. All cows that had been in the pen for 5 or less days were enrolled in the study and had a combination of colors, letters, and numbers painted on their rumps as unique identifiers (paint ID; PID). The video feed from the camera-based technology was observed daily from the company website for 7 d. A cow was recorded as correctly identified if the website had an uploaded video for a cow's ID that matched its PID. Successful identification was defined as the proportion of cow ID for which video was uploaded to the user platform within 7 d from the start of the study, regardless of accuracy. Correct identification was subsequently calculated as the proportion of these successfully identified cows that had a PID corresponding to their PID. The days under the camera were calculated by including the time cows would have been exposed to the camera before our 7-d observation period. Additionally, days identified reflect the total number of days that cows were identified (correctly or incorrectly) and scored during the observation period. Of the 103 cows enrolled, 87 (84.5%; 95% CI: 76%–91%) of cows were successfully identified during the 7-d study period. One cow from those 87 was incorrectly identified, resulting in a correct identification of 98.9% (95% CI: 94%–100%). Of the 86 correctly identified cows, all cows were observed between days 4 and 11 under the camera. Of the cows identified, they were identified 1 to 7 times. This technology accurately identifies cows, but 16 cows were not initially identified and ended with a minimum and maximum of 7 and 11 d under the camera, respectively. To allow management decisions to be made early in lactation or for new cows entering the herd, the technology will need to accurately identify all cows within the first week of being exposed to the camera.
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JDS communications
JDS communications Animal Science and Zoology
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