Stress in cattle is one of the main factors that generate economic losses in the livestock sector (e.g., reduction in the quality of milk or meat). In this field, heat stress has been considered as one of the main types of stress that negatively affects cattle. In addition, thanks to the arising of the Internet of Things in Animal Health, some researchers have proposed systems and models for the detection of this type of stress in an automated way, collecting and using data from meteorological variables (e.g., temperature, humidity), heart rate and others. However, the proposed models are mainly focused on heat stress detection that uses threshold-based estimation to determine the presence of stress; but, the level of stress experienced by cows can vary depending on their breed, or their ability to adapt to the environment where they are located. Therefore, in this project we propose an IoT platform for automatic detection of stress in cattle based on physiological signals; which is divided into three parts: i) implement a sensing device to collect physiological data, ii) a new method for automatic detection of stress based on physiological signals, and iii) an intuitive visualizer for monitoring cattle in individually way. The future research project, named PhyDac, is going to be carried out for two years with the participation of farmers from Peruvian regions (Arequipa, Cusco).