AI-powered visual E-monitoring system for cattle health and wealth

The livestock industry is experiencing a major transformation through the integration of artificial intelligence (AI) and advanced visual e-monitoring technologies. This study presents an AI-powered cattle health monitoring system that combines real-time computer vision, edge computing, and mobile applications to enhance animal welfare and farm productivity. The system employs a multi-camera setup, comprising RGB, RGB-D, and ToF depth cameras, strategically deployed across four functional zones of a cattle barn: the milking parlor, return lane, feeding area, and resting space. Through integrated deep learning algorithms, the platform performs key health-related tasks, including ear-tag, body-based, and face-based cattle identification, body condition scoring (BCS), lameness detection, feeding time estimation, and real-time localization. A farm-side desktop application processes live video streams from 22 cameras using multiprocessing, maintaining an average latency of 0.62 s per frame per camera. Captured data are stored in a structured MySQL database and accessed via a RESTful API by a user-side mobile application developed using Flutter and Clean Architecture. Experimental evaluation under continuous 24-hour operation demonstrated the system’s stability and effectiveness in delivering actionable insights. Cattle identification achieved high accuracies: ear-tag 94.00 %, face-based 93.66 %, body-based 92.80 %, and body-color point cloud 99.55 %. The BCS prediction and lameness detection modules achieved average accuracies of 86.21 % and 88.88 %, respectively. Feedback from veterinarians and farm personnel during pilot testing confirmed its usability and practical relevance. While current limitations include computational demands and the need for improved model robustness, the proposed system establishes a scalable, non-invasive framework for intelligent livestock monitoring. It aligns with broader Green and Digital Transformation (GX and DX) initiatives toward sustainable smart farming practices.