Miller Ruales

Guinea pigs have significant economic and cultural importance in Latin America. However, their breeding is affected by various diseases that reduce productivity and animal welfare. Early detection of these diseases is crucial, but traditional methods such as palpation and visual observation can be imprecise and subjective. Additionally, guinea pigs are very sensitive to human contact, and improper handling can negatively affect their health. In this context, the question arises: Is it possible to develop a non-invasive monitoring system that combines temperature and weight measurements in guinea pigs to determine their health status more accurately than traditional methods? To answer this question, a comprehensive review of the existing literature is proposed. This review aims to identify and analyze the most relevant non-invasive technologies for measuring the temperature and weight of these animals. Furthermore, methodologies for combining these measurements to assess the health status of guinea pigs will be explored. The review will also consider current trends in artificial intelligence, specifically in machine learning, to analyze the data collected by sensors. The review methodology follows a systematic approach, establishing inclusion and exclusion criteria to ensure the quality and relevance of the information collected. Keywords are defined, and relevant research databases are selected. The article selection process is carried out in three stages: elimination of duplicates, elimination of articles with access restrictions, and filtering according to the research topic. Finally, the quality of the selected articles is evaluated to deepen the proposed discussion and draw conclusions on the feasibility and potential benefits of implementing a non-invasive monitoring system to improve guinea pig health and welfare in production settings.