Applications of Artificial Intelligence and Machine Learning in Animal Production and Biotechnology: An Evaluation and Future Perspective Based on the Research of Prof. Dr. Uğur Şen

Abstract

In modern animal production and agricultural biotechnology disciplines, artificial intelligence (AI) and machine learning (ML) algorithms have become crucial for analyzing complex data, predicting productivity, and modeling physiological and phenotypic traits. In this study, the articles contributed to the literature by Prof. Dr. Uğur Şen and colleagues were systematically evaluated from an AI and data mining perspective. The reviewed studies demonstrate that advanced machine learning algorithms such as XGBoost, Random Forest (RF), Multivariate Adaptive Regression Splines (MARS), Support Vector Machines (SVM), and LightGBM have been successfully applied across various livestock species (sheep, goats, cattle, buffaloes, pigs, rabbits, Pekin ducks) for body weight prediction, classification of biometric measurements, FAMACHA© scoring, and genomic predictions. Furthermore, the future integration of biotechnological data—focused on molecular biology, muscle fiber phenotyping, placental morphology, and in vitro embryo development—with deep learning and computer vision techniques is discussed. This evaluation aims to provide an interdisciplinary vision for researchers working in the field of Precision Livestock Farming (PLF).