Exploring Cluster Analysis in Nelore Cattle Visual Score Attribution
This work addresses precision cattle breeding by providing data-driven methods for cattle assessment, but it is incremental as it applies existing techniques to this domain.
The paper tackled the problem of assessing Nelore cattle biotype by analyzing correlations between human visual scores and measurable data, and used k-means clustering to group cattle based on body weight and visual scores.
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.