Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/52731
Title: Mind the Step: An Artificial Intelligence-Based Monitoring Platform for Animal Welfare
Authors: Michielon, Andrea
Litta, Paolo
Bonelli, Francesca
Don, Gregorio
Farisè, Stefano
Giannuzzi, Diana
Milanesi, Marco 
Pietrucci Daniele 
Vezzoli, Angelica
Cecchinato, Alessio
Chillemi, Giovanni
Gallo, Luigi
Mele, Marcello
Furlanello, Cesare
Journal: SENSORS 
Issue Date: 2024
Abstract: 
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.
URI: http://hdl.handle.net/2067/52731
ISSN: 1424-8220
DOI: 10.3390/s24248042
Rights: Attribution 4.0 International
Appears in Collections:A1. Articolo in rivista

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