Artificial intelligence-based dairy cattle behavior recognition for estrus detection via ensemble fusion of two camera views

TL;DR

This paper introduces a system using AI and two camera views to detect estrus in dairy cattle by analyzing their behavior.

Contribution

The novel contribution is an ensemble fusion system combining top-view and front-view CCTV data for accurate estrus detection.

Findings

The system uses YOLOv8 models to detect and classify six key cattle behaviors.

Estrus-related behaviors like riding and chin resting are accurately identified through synchronized camera views.

Decision-level ensemble improves the accuracy of behavior recognition by integrating multi-view data.

Abstract

Monitoring cattle behavior plays an important role in improving farm productivity, maintaining animal welfare, and supporting efficient management practices. This study presents a multi-view behavior recognition system that uses synchronized top-view and front-view CCTV footage, combined with deep learning techniques. The system includes four main components: cow identification, behavior classification, identity-behavior association using Intersection-over-Union (IoU), and a decision-level ensemble to combine information from both views. YOLOv8 models are applied separately to each camera angle to detect individual cows and classify six key behaviors: drinking, eating, standing, lying, riding, and chin resting, with the latter two being relevant for estrus detection. The system matches cow identities to their behaviors within each view and then integrates the results to produce a final activity label for each cow.

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