Developing an automatic warning system for anomalous chicken dispersion and movement using deep learning and machine learning

Abstract

ABSTRACT: 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.

Keywords

• Convolutional neural network (CNN)
• Embedded system
• Simple online and real-time tracking (SORT)
• Taiwanese native chickens (TNCs)
• You only look once (YOLO)