Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan.
Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan.
Poult Sci. 2023 Dec;102(12):103040. doi: 10.1016/j.psj.2023.103040. Epub 2023 Aug 23.
Chicken is a major source of dietary protein worldwide. The dispersion and movement of chickens constitute vital indicators of their health and status. This is especially evident in Taiwanese native chickens (TNCs), a local variety which is high in physical activity when healthy. Conventionally, the dispersion and movement of chicken flocks are observed in patrols. However, manual patrolling is laborious and time-consuming. Moreover, frequent patrols increase the risk of carrying pathogens into chicken farms. To address these issues, this study proposes an approach to develop an automatic warning system for anomalous dispersion and movement of chicken flocks in commercial chicken farms. Embendded systems were developed to acquire videos of chickens from overhead view in a chicken house, in which approximately 20,000 TNCs were raised for a period of 10 wk. Each video was 5-min in length. The videos were transmitted to a remote cloud server and were converted into images. A You Only Look Once-version 7 tiny (YOLOv7-tiny) object detection model was trained to detect chickens in the images. The dispersion of the chicken flocks in a 5-min long video was calculated using nearest neighbor index (NNI). The movement of the chicken flocks in a 5-min long video was quantified using simple online and real-time tracking algorithm (SORT). The normal ranges (i.e., 95% confidence intervals) of chicken dispersion and movement were established using an autoregressive integrated moving average (ARIMA) model and a seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model, respectively. The system allows farmers to check up on the chicken farm only when the dispersion or movement values were not in the normal ranges. Thus, labor time can be saved and the risk of carrying pathogens into chicken farms can be reduced. The trained YOLOv7-tiny model achieved an average precision of 98.2% in chicken detection. SORT achieved a multiple object tracking accuracy of 95.3%. The ARIMA and SARIMAX achieved a mean absolute percentage error 3.71% and 13.39%, respectively, in forecasting dispersion and movement. The proposed approach can serve as a solution for automatic monitoring of anomalous chicken dispersion and movement in chicken farming, alerting farmers of potential health risks and environmental hazards in chicken farms.
鸡肉是全球主要的膳食蛋白质来源。鸡的分散和移动是其健康和状态的重要指标。这在台湾土鸡（TNC）中尤为明显，健康的 TNC 活动量较高。传统上，鸡群的分散和移动是通过巡逻来观察的。然而，人工巡逻既费力又费时。此外，频繁的巡逻会增加将病原体带入养鸡场的风险。为了解决这些问题，本研究提出了一种方法，用于开发商业养鸡场鸡群异常分散和移动的自动报警系统。嵌入式系统被开发用于从鸡舍的高空获取鸡的视频，大约有 20000 只 TNC 在鸡舍中饲养了 10 周。每个视频时长 5 分钟。视频被传输到远程云服务器并转换为图像。使用 You Only Look Once 版本 7 微小（YOLOv7-tiny）对象检测模型来检测图像中的鸡。使用最近邻指数（NNI）计算 5 分钟长视频中鸡群的分散度。使用简单在线实时跟踪算法（SORT）量化 5 分钟长视频中鸡群的移动。使用自回归综合移动平均（ARIMA）模型和带外生因素的季节性自回归综合移动平均（SARIMAX）模型分别建立鸡群分散和移动的正常范围（即 95%置信区间）。该系统允许农民仅在分散或移动值不在正常范围内时检查养鸡场。因此，可以节省劳动力，降低将病原体带入养鸡场的风险。训练有素的 YOLOv7-tiny 模型在鸡检测方面的平均精度为 98.2%。SORT 实现了 95.3%的多目标跟踪精度。ARIMA 和 SARIMAX 在预测分散和移动方面的平均绝对百分比误差分别为 3.71%和 13.39%。本研究提出的方法可以作为鸡养殖中鸡异常分散和移动自动监测的解决方案，提醒农民养鸡场潜在的健康风险和环境危害。
