Machine Learning Techniques Associated With Infrared Thermography to Optimize the Diagnosis of Bovine Subclinical Mastitis.

ABSTRACT: Bovine subclinical mastitis (SCM) is the costliest disease for the dairy industry. Technologies aimed at the early diagnosis of this condition, such as infrared thermography (IRT), can be used to generate large amounts of data that provide valuable information when analyzed using learning techniques. The objective of this study was to evaluate and optimize the use of machine learning by applying the Extreme Gradient Boosting (XGBoost) algorithm in the diagnosis of bovine SCM, based on udder thermogram analysis. Over 14 months, a total of 1035 milk samples were collected from 97 dairy cows subjected to an automatic milking system. Somatic cell counts were performed by flow cytometry, and the health status of the mammary gland was determined based on a cutoff of 200,000 cells/mL of milk. The

SUBMITTER: Santana RCM 

PROVIDER: S-EPMC11830110 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature