Abstract:
"The growing problem of food waste, which affects both the environment and resource
management, is a growing concern. This is often due to the fact that people don't know how to
use ingredients creatively in their homes, leading to unnecessary waste of resources. In order
to reduce food waste and promote healthy eating habits, the thesis addresses the need for a new
solution.
To address this challenge, a system is being developed that uses advanced deep learning
technology to identify food items from images or videos provided by users. The YOLOv8
action, which can correctly identify the various food components. To find and retrieve
matching recipes for ingredients, this is connected to a backend that interfaces with the
Spoonacular API. This approach is not only a way to reduce food waste by suggesting
immediate uses for available ingredients, but also supports healthier choices of foods through
the provision of nutritional information on recipes.
The performance of this system, in particular the accuracy of the YOLOv8 model for
identification of ingredients, has been thoroughly evaluated. The model showed very good
accuracy, with a ratio of 0.854. The system has also been positively received by users, who
have highlighted its usefulness in reducing food waste and the possibility of healthy eating.
These results show that the system is technically suitable and useful for users who wish to use
kitchen ingredients in a way which minimises waste.
"
