商业育肥猪场扫描取样行为观察与自动监测图像系统的比较研究

IF 2.1 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE
Queralt Allueva Molina, H. Ko, Y. Gómez, X. Manteca, P. Llonch
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引用次数: 0

摘要

自动化是现代畜牧业的重要组成部分。图像计算机分析是一种自动化技术,旨在通过记录连续图像来监控农场动物。通过进一步分析,可以更精确、更有效地评估农场动物的福利。本研究的目的是确定法国 Copeeks SAS 公司开发的商用多传感器设备(Peek Analytics)的适用性,并与用于评估猪的行为(包括姿势(站立/休息)、栏内区域(喂食/饮水/休息/充实)和活动水平(活动猪的数量))的人工观察进行比较。西班牙穆尔西亚的一家商业育肥猪场安装了两台 Peek 分析仪(Peek 3 和 Peek 4)。每个设备记录两个猪圈(共四个猪圈 39 头猪)的数据。本研究采用的行为观察方法是扫描取样。数据收集时间为连续五天,时间间隔为:09:00-11:00、13:00-15:00 和 16:00-18:00(共观察 30 小时)。每头猪每小时被观察六次,因此分析的信息包括 7020 次观察(每头猪 180 次观察)。人类观察数据与 Peek Analytics 数据之间的比较是通过皮尔逊相关性检验进行的。分别分析了姿势、感兴趣的区域和活动水平,以及 Peek 3 和 4 记录的数据。结果表明,在记录姿势(r=0.77,P<0.01)和笔内区域(r=0.77,P<0.01)时,Peek Analytics 与人类观察结果的一致性要好于记录活动水平(r=0.35,P<0.01)时的一致性。总体而言,两种设备的表现不同,无论姿势、笔内面积和活动水平如何,Peek 3 与人类观察的一致性都优于 Peek 4。Peek 3 的一致性更好可能是因为 Peek 3 的猪只数量(18 头)少于 Peek 4(22 头)。我们可以从这项研究中得出结论,图像计算机分析在评估猪的姿势和栏内面积方面可能是可靠的。另一方面,人类观察和计算机视觉在活动水平上的适度一致可能是由于记录活动的方法不同,而不是由于 Peek 分析的准确性低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative study between scan sampling behavioral observations and an automatic monitoring image system on a commercial fattening pig farm
Automation is an important element in modern livestock farming. Image computer analysis is the automation technology aiming to monitor farm animals by recording continuous images. Further analysis can be carried out to assess more precisely and effectively farm animals’ welfare. The aim of this study was to determine the applicability of the commercial multi-sensor device (Peek Analytics) developed by Copeeks SAS (France), in comparison to human-based observations used to assess behaviors in pigs, including posture (standing/resting), area within the pen (feeding/drinking/resting/enrichment), and activity level (number of active pigs). Two Peek Analytics (Peek 3 and Peek 4) were installed on a commercial fattening pig farm in Murcia (Spain). Each device recorded data of two pens (39 pigs in four pens in total). Scan sampling was the human-based behavioral observation method used in this study. Data was collected for five consecutive days, in the following intervals: 09:00-11:00, 13:00-15:00, and 16:00-18:00 (30 hours of observation in total). Every pig was observed six times per hour and hence the information analyzed includes 7020 observations (180 observations/pig). The comparison between data from human observation and Peek Analytics was performed by using Pearson correlation tests. Posture, areas of interest, and activity level were analyzed separately, as well as data recorded by Peek 3 and 4. Results indicated that Peek Analytics showed a better agreement with human observation, when recording posture(r=0.77, P<0.01) and area within the pen (r=0.77, P<0.01), than when recording activity level (r=0.35, P<0.01). Two devices performed differently in general, with Peek 3 having better agreement than Peek 4 with human observation, regardless of posture, area within the pen, and activity level. The better agreement in Peek 3 may be attributed to the smaller number of pigs in Peek 3 (18) compared to Peek 4 (22). We can conclude from the study that image computer analysis may be reliable in assessing posture and area within the pen of pigs. On the other hand, a moderate agreement in activity level between human observation and computer vision can be due to different methodologies of recording the activity, rather than due to low accuracy of Peek Analytics.
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CiteScore
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