# RecipeIS--Recipe Recommendation System Based on Recognition of Food Ingredients
## 2 Related Works
Figure 5: % of the architectures used in the articles. Figure 6: Quantity of datasets used in the articles. After the analysis of the scientific articles and verification of their results, it is concluded that, besides the implementation method being one of the most important, it is fundamental to make a good choice of the software to be used, the architecture, and the most accurate models for recognizing a given food and recommending recipes. It is also possible to see that different types of models and architectures can be used to carry out the recognition and recommendation of recipes, and this implementation requires programming that is too complex to be solved using traditional programming since it is sometimes necessary to make changes in the architectures or models. Based on the above information, the answer to the research questions is presented:  
1. Question 1: Are there digital solutions that offer recipe proposals based on ingredient recognition or ingredient images? As the proposed topic is comprehensive and image recognition is becoming increasingly common in the food industry, many people use mobile applications to simplify tasks. Among the scientific articles studied, various solutions for ingredient recognition using different implementation methods were found, as referenced in articles [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. Although no article specifically mentions the recognition of food packages, some articles tested image datasets that included images of packaged food, such as the VireoFood dataset. This dataset contains pictures of recipes, foods, ingredients, and some packaged foods; it is mentioned in articles [9; 15; 16; 17; 18]. 2. Question 2: What are the frameworks for ingredient recognition solutions? Frameworks are interfaces that contain a set of generic codes, enabling the development and implementation of models in a fast and effective way, thus reducing the time required to reproduce a certain function. Concerning the intended topic of food or ingredient recognition, several available frameworks can perform this function, such as PyTorch, as referenced in the article [13], MXNet in the article [16], and Keras in the article [19]. 3. Question 3: Are there digital solutions for recipe proposals based on leftover meals to fight food waste? Food waste is a growing concern in today's world due to high consumption and variety, which has serious social, economic, and environmental consequences. Despite analyzing several articles, none of them directly address the problem of food waste. However, combating food waste can involve actions such as buying food that can be consumed within its shelf life, and recommending recipes based on the recognition of purchased foods is one way to do so. The articles that focus on recipe recommendations based on food or ingredient recognition are [12; 18; 19; 23]. After a comprehensive study, it was concluded that the ResNet-50 model is the most frequently used and mentioned in the scientific articles analyzed. Additionally, the Food-101 dataset is the most cited and utilized for tests and results, despite being extensive and primarily consisting of images with several ingredients and fewer images of simple ingredients.