Mind the Step: An Artificial Intelligence-Based Monitoring Platform for Animal Welfare - Andrea Michielon; Paolo Litta; Francesca Bonelli; Gregorio Don; Stefano Farisè; Diana Giannuzzi; Marco Milanesi; Daniele Pietrucci; Angelica Vezzoli; Alessio Cecchinato; Giovanni Chillemi; Luigi Gallo; et al. (Sensors)
We present an artificial intelligence (AI)-enhanced monitoring framework designed to assist personnel in evaluating and maintaining animal welfare using a modular architecture. This framework integrates multiple deep learning models to automatically compute metrics relevant to assessing animal well-being. Using deep learning for AI-based vision adapted from industrial applications and human behavioral analysis, the framework includes modules for markerless animal identification and health status assessment (e.g., locomotion score and body condition score). Methods for behavioral analysis are also included to evaluate how nutritional and rearing conditions impact behaviors. These models are initially trained on public datasets and then fine-tuned on original data. We demonstrate the approach through two use cases: a health monitoring system for dairy cattle and a piglet behavior analysis system. The results indicate that scalable deep learning and edge computing solutions can support precision livestock farming by automating welfare assessments and enabling timely, data-driven interventions.