The integration of Machine Learning (ML) and Artificial Intelligence (AI) into animal biotechnology is revolutionizing the field, particularly in developing countries where agriculture and livestock play significant roles in the economy. AI and ML enable more efficient data analysis in areas such as genetic optimization, disease prediction, and livestock management, improving both productivity and sustainability. With the growing availability of data, AI-driven models can process large volumes of information from diverse sources such as environmental conditions, genetic markers, and health records, offering more precise insights than traditional methods. Recent advancements include AI-powered diagnostic systems for detecting and managing disease outbreaks, which allow for faster response times and more targeted interventions, ultimately reducing economic losses. Enhanced breeding techniques, now, leverage machine learning algorithms to predict desirable genetic traits, enabling farmers to make data-driven breeding choices. Feed efficiency improvements, another critical area, benefit from AI's ability to analyze nutrient requirements and optimize feeding schedules based on individual animal needs, reducing waste and costs. Additionally, AI is increasingly applied to animal health monitoring, using tools such as sound-based systems and piezoelectric sensors embedded in smart collars to track behaviors indicative of health issues. In the dairy sector, AI models assess health risks like nitrate contamination in milk, contributing to safer food production and improving public health. In genetic studies, AI enhances selective breeding, improving traits such as growth and disease resistance. This manuscript reviews the transformative role of AI and ML in animal biotechnology, focusing on developing regions where resource optimization is crucial. By simplifying complex techniques and providing step-by-step tutorials, this work aims to equip researchers and practitioners with practical tools to harness AI in animal biotechnology.
Kiran et al. (Fri,) studied this question.