Chicken Tracking and Individual Bird Activity Monitoring Using the BoT-SORT Algorithm

Abstract

The analysis of chicken movement on the farm has several applications in evaluating the well-being and health of birds. Low locomotion may be associated with locomotor problems, and undesirable bird movement patterns may be related to environmental discomfort or fear. Our objective was to test the BoT-SORT object tracking architecture embedded in Yolo v8 to monitor the movement of cage-free chickens and extract measures to classify running, exploring, and resting behaviors, the latter of which includes all other behaviors that do not involve displacement. We trained a new model with a dataset of 3623 images obtained with a camera installed on the ceiling (top images) from an experiment with layers raised cage-free in small-scale aviaries and housed in groups of 20 individuals. The model presented a mAP of 98.5%, being efficient in detecting and tracking the chickens in the video. From the tracking, it was possible to record the movements and directions of individual birds, and we later classified the movement. The results obtained for a group of 20 chickens demonstrated that approximately 84% of the time, the birds remained resting, 10% of the time exploring, and 6% of the time running. The BoT-SORT algorithm was efficient in maintaining the identification of the chickens, and our tracking algorithm was efficient in classifying the movement, allowing us to quantify the time of each movement class. Our algorithm and the measurements we extract to classify bird movements can be used to assess the welfare and health of chickens and contribute to establishing standards for comparisons between individuals and groups raised in different environmental conditions.

Chicken tracking for location mapping of lameness chickens using YOLOv8 and deep learning-based tracking algorithm

“…Assessing the health and well-being of chickens on the farm involves tracking their movements and categorizing various behaviors, including activity and relaxation. The Bot-sort architecture in YOLOv8 was used for testing to find chicken movements and turned out to be effective in detecting and tracking chickens [37]. A bird's behavior indicates its health and well-being.…”

“…Some of these algorithms have limitations, therefore there is a new algorithm, namely the ADAMW optimizer, to overcome the ADAM optimizer problem with overfitting and a sharp decrease in learning rate [35]. Meanwhile, to improve multi-object tracking performance in deep learning, there is the Bot-sort algorithm [36] which is employed to extract movement characteristics from objects, such as displacement, acceleration, and speed; these attributes are then utilized to preserve modifications and ignore failures to identify the desired behavior in the behavior of the chickens [37]. Another object tracking technique, ByteTrack improves its speed on difficult data sets with an easy yet powerful data augmentation method.…”

A Video Mosaicing-Based Sensing Method for Chicken Behavior Recognition on Edge Computing Devices

“…Understanding chicken behavior in the context of animal welfare monitoring provides crucial information for evaluating the flock’s health. This involves observing activities such as eating [ 1 ], drinking [ 2 ], resting [ 3 ], stretching [ 4 ], walking [ 5 ], running [ 6 ], preening [ 7 ] and others, like chicken being unhealthy [ 8 ]. The importance of deviations from the standard behavior of chickens cannot be overstated, as they serve as crucial indicators of possible problems or illnesses within the flock.…”

“…By closely observing bird species and their activities using automatic recognition technologies [ 17 ], valuable insights can be gained regarding the overall health and happiness of the flock, contributing to the understanding of diverse emergent collective behaviors [ 18 ]. This knowledge enables early detection of health issues and diseases [ 6 , 19 ], leading to timely intervention and improved flock health [ 3 ].…”