AI-Based Teat Shape and Skin Condition Prediction for Dairy Management

TL;DR

This paper develops AI-based computer vision models to predict teat shape and skin condition in dairy cows, aiming to improve health monitoring and management efficiency in dairy farming.

Contribution

It introduces a novel ML pipeline for teat localization, shape, and skin condition classification, achieving high accuracy in a farming environment.

Findings

Teat shape prediction model achieves mAP of 0.783.

Teat skin condition model achieves mAP of 0.828.

Leverages existing ML vision models for dairy health assessment.

Abstract

Dairy owners spend significant effort to keep their animals healthy. There is good reason to hope that technologies such as computer vision and artificial intelligence (AI) could reduce these costs, yet obstacles arise when adapting advanced tools to farming environments. In this work, we adapt AI tools to dairy cow teat localization, teat shape, and teat skin condition classifications. We also curate a data collection and analysis methodology for a Machine Learning (ML) pipeline. The resulting teat shape prediction model achieves a mean Average Precision (mAP) of 0.783, and the teat skin condition model achieves a mean average precision of 0.828. Our work leverages existing ML vision models to facilitate the individualized identification of teat health and skin conditions, applying AI to the dairy management industry.