Birds' Eye View: Measuring Behavior and Posture of Chickens as a Metric for Their Well-Being
This addresses chicken well-being for food security and nutrition, but is incremental as it applies existing methods to a new domain-specific setup.
The research tackled measuring chicken well-being by detecting posture and behavior using Mask R-CNN and YOLOv4 with ResNet50, achieving a weighted F1 score of 88.46% for posture and behavior detection and accuracies of 91% for behavior and 86.5% for posture.
Chicken well-being is important for ensuring food security and better nutrition for a growing global human population. In this research, we represent behavior and posture as a metric to measure chicken well-being. With the objective of detecting chicken posture and behavior in a pen, we employ two algorithms: Mask R-CNN for instance segmentation and YOLOv4 in combination with ResNet50 for classification. Our results indicate a weighted F1 score of 88.46% for posture and behavior detection using Mask R-CNN and an average of 91% accuracy in behavior detection and 86.5% average accuracy in posture detection using YOLOv4. These experiments are conducted under uncontrolled scenarios for both posture and behavior measurements. These metrics establish a strong foundation to obtain a decent indication of individual and group behaviors and postures. Such outcomes would help improve the overall well-being of the chickens. The dataset used in this research is collected in-house and will be made public after the publication as it would serve as a very useful resource for future research. To the best of our knowledge no other research work has been conducted in this specific setup used for this work involving multiple behaviors and postures simultaneously.