Exploring Cluster Analysis in Nelore Cattle Visual Score Attribution

TL;DR

This paper investigates the use of cluster analysis and correlation techniques to improve cattle biotype assessment through visual scores and image-derived measurements, aiming to enhance precision breeding practices.

Contribution

It introduces a novel application of k-means clustering to categorize Nelore cattle based on correlated measurements and visual scores, providing new insights into cattle biotype evaluation.

Findings

Correlation between visual scores and measurements identified

K-means clustering effectively grouped cattle by biotype

Potential for improved cattle selection methods

Abstract

Assessing the biotype of cattle through human visual inspection is a very common and important practice in precision cattle breeding. This paper presents the results of a correlation analysis between scores produced by humans for Nelore cattle and a variety of measurements that can be derived from images or other instruments. It also presents a study using the k-means algorithm to generate new ways of clustering a batch of cattle using the measurements that most correlate with the animal's body weight and visual scores.