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注意台阶:一个基于人工智能的动物福利监测平台。

Mind the Step: An Artificial Intelligence-Based Monitoring Platform for Animal Welfare.

作者信息

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

机构信息

Orobix Life, 24121 Bergamo, Italy.

Antares Vision, 25039 Travagliato, Italy.

出版信息

Sensors (Basel). 2024 Dec 17;24(24):8042. doi: 10.3390/s24248042.

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.

摘要

我们提出了一种人工智能(AI)增强的监测框架,该框架采用模块化架构设计,旨在协助人员评估和维护动物福利。此框架集成了多个深度学习模型,以自动计算与评估动物福祉相关的指标。通过改编自工业应用和人类行为分析的基于AI的视觉深度学习,该框架包括用于无标记动物识别和健康状况评估的模块(例如,运动评分和身体状况评分)。还包括行为分析方法,以评估营养和饲养条件如何影响行为。这些模型最初在公共数据集上进行训练,然后在原始数据上进行微调。我们通过两个用例展示了该方法:一个奶牛健康监测系统和一个仔猪行为分析系统。结果表明,可扩展的深度学习和边缘计算解决方案可以通过自动化福利评估并实现及时的数据驱动干预来支持精准畜牧业。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f66/11679443/8bc2c3669144/sensors-24-08042-g001.jpg

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