Content available at:
Indonesia (Indonesian) Melayu (Malay) ไทย (Thai) Tiếng Việt (Vietnamese) Philipino
BACKGROUND ON POULTRY WELFARE
Poultry production plays a critical role in feeding the increasing world’s population with affordable protein (i.e., chicken and eggs). The United States is currently the world’s largest broiler producer and 2nd largest egg producer due to continuous innovation in animal breeding, nutrition management, environmental control, and disease prevention, etc.
However, US poultry and egg farms are facing several production challenges such as animal welfare concerns.
For instance, the fast growing broiler chickens were reported with leg issues or lameness.
The caged egg production systems were pushed to shift for cage-free operation, which cannot guarantee a better hen welfare due to high mortality, injury rate, and poor air quality.
To address those issues, researchers at the University of Georgia (UGA, Dr. Lilong Chai’s precision poultry farming lab) developed several precision farming technologies for monitoring welfare and behaviors of broilers and cage-free layers.
BROILERS’ FLOOR DISTRIBUTION AND BEHAVIORS
The spatial distribution of broiler chickens is an indication of a healthy flock or not. Routine inspections of broiler chickens’ floor distribution are done manually in commercial houses daily or multiple times a day, which is labor intensive, time consuming, and subject to farm staff’s errors.
This task requires a precision system that can monitor the chicken’s floor distributions automatically.
A machine vision-based method was developed and tested in an experimental broiler house at UGA. To track individual birds’ distribution, the pen floor was virtually defined/divided into drinking, feeding, and rest/exercise zones (Figure 1).
About 7000 chicken areas/profiles were used to build a neural network model (BP – backward propagation) for floor distribution analysis.
The results showed that the identification accuracies of bird distribution in the drinking and feeding zones were 0.9419 and 0.9544, respectively.
The team further innovated CNN (convolutional neural network) based deep learning models to detect birds’ behaviors of feeding, drinking, resting, and standing at different ages (Figure 2 & Figure 3).
After image augmentation processing, over 10,000 images were generated for each day and the model reached the accuracy rates of 88.5%, 97%, 94.5%, and 90% when birds were 2, 9, 16 and 23 d...
