Year of Graduation
Developing Methods for Recognition of the Shelf Life of Food Based on Machine Learning in Smart Device Technology
Big Data Systems
Food management is a new opportunity that provided by home appliances manufacturers with the help of IoT solutions. Several companies that produce fridges allow using internal or additional modules for remote controlling and checking the content inside the refrigerator. One of the further perspective destinations of developing food management is an automatic recognition the approximate shelf life of the foodstuff. There is a hypothesis that Machine Learning methods could detect it in case of analysing the changes of appearance of particular food item during the time. The goal of this research is to prove it with the example of creating methods of detecting approximate shelf life of food items that can be used as an additional function to SmartDevice technology from Liebherr.