International Journal of Scientific Research and Engineering Development

The intensification of poultry production necessitates advanced welfare monitoring systems that minimize animal stress while providing accurate behavioral assessments. This study investigates the application of artificial intelligence-based non-invasive techniques for continuous poultry behavior analysis in commercial farming environments. We implemented a multimodal monitoring system combining computer vision, acoustic analysis, and thermal imaging to classify behaviors and detect welfare indicators in a cohort of 500 broiler chickens over eight weeks. Deep learning models achieved 94.3% accuracy in behavior classification, with particularly high sensitivity (96.7%) for stress-related behaviors. Results demonstrate that AI-based non-invasive monitoring significantly outperforms manual observation in detecting early welfare concerns, offering scalable solutions for improving animal welfare standards and optimizing farm management practices in the poultry industry.