Machine learning overview and its application in the livestock industry

Abstract

Machine learning (ML) algorithms have transformed data analysis across various sectors, providing powerful tools to derive insights, make predictions, and automate complex processes. This review explores the fundamental classifications of ML algorithms, including supervised, unsupervised, semi-supervised, reinforcement, and deep learning methods, each offering unique capabilities for addressing diverse data-driven challenges. In the field of livestock farming, ML applications are proving to be particularly impactful, helping to enhance productivity, optimize resource use, and improve animal health and welfare. By leveraging data from sensors, imaging, and environmental inputs, ML models can detect diseases, monitor animal behaviors, forecast production yields, and manage feeding schedules more precisely than ever before. Despite significant advancements, challenges remain, including data quality issues, model interpretability, and ethical considerations surrounding animal welfare. This paper provides an overview of key ML algorithms and examines current applications and future prospects of ML in the livestock sector, aiming to highlight its potential for innovation and sustainable development in modern agriculture.