Application for cow lameness detection using computer vision

Kong, Zi Lin (2025) Application for cow lameness detection using computer vision. Final Year Project, UTAR.

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Abstract

Lameness is a major welfare and economic issue in dairy farming, causing reduced milk yield, reproductive failure, and premature culling. Small and medium-sized farms often lack affordable tools for early detection, leading to delayed treatment. This project develops a cost effective mobile application that uses computer vision to detect lameness in cows and provides basic herd management functions. The lameness detection model applies YOLOv8-based pose estimation to extract anatomical key points and calculate back arching through Root Mean Squared Error (RMSE), identifying abnormal postures linked to lameness. A cow recognition system, based on ResNet18 and ArcFace embeddings, allows farmers to register cows by coat patterns and maintain health records. The system is supported by a FastAPI backend with Firebase integration for storage, authentication, and data management. Through a simple Flutter-based mobile app interface, farmers can upload images, receive predictions, confirm cow identities, and update records. This approach enables early diagnosis, improves animal welfare, and reduces economic losses.

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