Schmidt Ty B, Lancaster Jessica M, Psota Eric, Mote Benny E, Hulbert Lindsey E, Holliday Aaron, Woiwode Ruth, Pérez Lance C
Department of Animal Science, University of Nebraska - Lincoln, Lincoln, NE 68583, USA.
Electrical and Computer Engineering, University of Nebraska - Lincoln, Lincoln, NE 68583, USA.
Transl Anim Sci. 2022 Jun 16;6(3):txac082. doi: 10.1093/tas/txac082. eCollection 2022 Jul.
Animal behavior is indicative of health status and changes in behavior can indicate health issues (i.e., illness, stress, or injury). Currently, human observation (HO) is the only method for detecting behavior changes that may indicate problems in group-housed pigs. While HO is effective, limitations exist. Limitations include HO being time consuming, HO obfuscates natural behaviors, and it is not possible to maintain continuous HO. To address these limitations, a computer vision platform (NU was developed to identify (ID) and continuously monitor specific behaviors of group-housed pigs on an individual basis. The objectives of this study were to evaluate the capabilities of the NUystem and evaluate changes in behavior patterns over time of group-housed nursery pigs. The NU system was installed above four nursery pens to monitor the behavior of 28 newly weaned pigs during a 42-d nursery period. Pigs were stratified by sex, litter, and randomly assigned to one of two pens (14 pigs/pen) for the first 22 d. On day 23, pigs were split into four pens (7 pigs/pen). To evaluate the NU system's capabilities, 800 video frames containing 11,200 individual observations were randomly selected across the nursery period. Each frame was visually evaluated to verify the NU system's accuracy for ID and classification of behavior. The NU system achieved an overall accuracy for ID of 95.6%. This accuracy for ID was 93.5% during the first 22 d and increased ( < 0.001) to 98.2% for the final 20 d. Of the ID errors, 72.2% were due to mislabeled ID and 27.8% were due to loss of ID. The NU system classified lying, standing, walking, at the feeder (ATF), and at the waterer (ATW) behaviors accurately at a rate of 98.7%, 89.7%, 88.5%, 95.6%, and 79.9%, respectively. Behavior data indicated that the time budget for lying, standing, and walking in nursery pigs was 77.7% ± 1.6%, 8.5% ± 1.1%, and 2.9% ± 0.4%, respectively. In addition, behavior data indicated that nursery pigs spent 9.9% ± 1.7% and 1.0% ± 0.3% time ATF and ATW, respectively. Results suggest that the NU system can detect, identify, maintain ID, and classify specific behavior of group-housed nursery pigs for the duration of the 42-d nursery period. Overall, results suggest that, with continued research, the NU system may provide a viable real-time precision livestock tool with the ability to assist producers in monitoring behaviors and potential changes in the behavior of group-housed pigs.
动物行为是健康状况的指标,行为变化可能表明存在健康问题(如疾病、应激或损伤)。目前,人工观察(HO)是检测群养猪中可能表明问题的行为变化的唯一方法。虽然人工观察是有效的,但也存在局限性。局限性包括人工观察耗时、会干扰自然行为,且无法持续进行人工观察。为了解决这些局限性,开发了一个计算机视觉平台(NU),以个体方式识别(ID)并持续监测群养猪的特定行为。本研究的目的是评估NU系统的能力,并评估群养保育猪随时间的行为模式变化。NU系统安装在四个保育栏上方,以监测28头新断奶仔猪在42天保育期内的行为。在前22天,根据性别、窝别对仔猪进行分层,并随机分配到两个栏中的一个(每栏14头猪)。在第23天,仔猪被分成四个栏(每栏7头猪)。为了评估NU系统的能力,在整个保育期内随机选择了800个包含11200次个体观察的视频帧。对每个帧进行视觉评估,以验证NU系统对行为识别和分类的准确性。NU系统的总体识别准确率为95.6%。前22天的识别准确率为93.5%,在最后20天提高到98.2%(P<0.001)。在识别错误中,72.2%是由于识别标签错误,27.8%是由于识别丢失。NU系统对躺卧、站立、行走、在采食器处(ATF)和在饮水器处(ATW)行为的分类准确率分别为98.7%、89.7%、88.5%、95.6%和79.9%。行为数据表明,保育猪躺卧、站立和行走的时间分配分别为77.7%±1.6%、8.5%±1.1%和2.9%±0.4%。此外,行为数据表明,保育猪在采食器处和饮水器处分别花费9.9%±1.7%和1.0%±0.3%的时间。结果表明,NU系统能够在42天的保育期内检测、识别、维持识别并分类群养保育猪的特定行为。总体而言,结果表明,随着持续研究,NU系统可能会提供一种可行的实时精准畜牧工具,能够帮助生产者监测群养猪的行为及行为的潜在变化。