Related Experiment Videos
Artificial neural network prediction of ascites in broilers
W B Roush1, Y K Kirby, T L Cravener
1Department of Poultry Science, Pennsylvania State University, University Park 16802, USA.
Poultry Science
|December 1, 1996
Summary
Artificial neural networks accurately predict ascites in broiler chickens. This technology aids in early detection, even before fluid accumulation, improving flock health management.
Area of Science:
- Veterinary Medicine
- Animal Science
- Machine Learning
Background:
- Ascites, a condition characterized by fluid accumulation, significantly impacts broiler chicken health and welfare.
- Early and accurate diagnosis of ascites is crucial for effective disease management in poultry farming.
Purpose of the Study:
- To develop and validate an artificial neural network (ANN) model for predicting the presence or absence of ascites in broiler chickens.
- To assess the efficacy of ANNs in identifying ascites, including subclinical cases.
Main Methods:
- A three-layer back-propagation neural network was designed with 15 physiological variables as input.
- The network was trained using data from broiler chickens subjected to cool temperatures to induce ascites.
- Model performance was validated using independent datasets, including those with induced ascites and simulated physiological conditions.
Main Results:
- The ANN model demonstrated high accuracy in identifying ascites in the training dataset.
- Validation datasets showed a low rate of false positives (two and one, respectively).
- False positives were identified as birds in the early developmental stages of ascites.
Conclusions:
- Artificial neural networks are effective tools for accurately identifying ascites in broiler chickens.
- ANNs can detect ascites even in its early developmental stages, offering potential for proactive intervention.
- This technology can enhance disease surveillance and management strategies in commercial poultry production.