PRECISION AGRICULTURE FOR GRAZING AND ANIMAL HEALTH MANAGEMENT: A CASE STUDY IN COLOMBIA

In this research, we focus on addressing fattening management and animal health in rotational grazing within the framework of precision farming. Our approach leverages advanced technologies like Industry 4.0 and artificial intelligence to optimize agricultural and livestock processes. Our objective was to develop methodologies, models, and approaches supporting decision-making in productivity management and animal health. We pursued sub-objectives including developing a precision livestock farming architecture, creating knowledge models for animal health and herding management, and crafting meta-intelligent models for autonomous grazing and animal health management. Through a series of research articles, we achieved significant milestones. These include autonomous data analysis cycles for beef production, weight identification models using machine learning systems for monitoring cattle fattening using fuzzy classification, and multi-objective optimization models for maximizing weight gain in rotational grazing. We also introduced autonomous data analysis for self-supervision of animal fattening, management systems for cattle fattening, and the use of meta-learning in cattle weight identification for anomaly detection. Our methodologies and models demonstrated strong decision-making capabilities in managing livestock production processes, particularly in fattening and animal health management in rotational grazing, encompassing monitoring, diagnosis, and process optimization.