Automatic Monitoring of Chicken Movement and Drinking Time Using Convolutional Neural Networks

Highlights A customized embedded system was built to acquire images of a chicken coop. Faster R-CNN was used to localize the chickens in the images. The accuracies in chicken detection and tracking were 98.16% and 98.94%, respectively. Movement and drinking time of chickens were quantified. Abstract . Poultry and eggs are major sources of dietary protein worldwide. Because Taiwan is located in tropical and subtropical regions, heat stress in chickens is one of the most challenging concerns of the poultry industry in Taiwan. Typical heat stress symptoms in chickens are reduced movement and increased drinking time. The level of heat stress is conventionally evaluated using the temperature-humidity index (THI) or through manual observation. However, THI is indirect, and manual observation is subjective and time-consuming. This study proposes to directly monitor the movement and drinking time of chickens using time-lapse images and deep learning algorithms. In this study, an experimental coop was constructed to house ten chickens. An embedded system was then designed to acquire images of the chickens at a rate of 1 frame s-1 and to measure the temperature and humidity of the coop. A faster region-based convolutional neural network was then trained on a personal computer to detect and localize the chickens in the images. The movement and drinking time of the chickens under various THI values were then analyzed. The proposed method provided 98.16% chicken detection accuracy and 98.94% chicken tracking accuracy. Keywords: Chicken activities, Embedded system, Faster region-based convolutional neural network, Faster R-CNN, Heat stress, Temperature-humidity index (THI).