# Development of a Prototype Solution for Reducing Soup Waste in an Institutional Canteen
## 4 Conclusions  
Reducing food waste is not just an option, but an ethical responsibility with a temporal urgency. Every meal recovered helps to tackle important challenges, with significant consequences in the economic, environmental, and social spheres. Understanding the urgent need to solve these problems is fundamental, especially considering the large amount of food lost along the supply chain, accentuated by the habits of retailers and consumers. This work has explored the use of computer vision and artificial intelligence to identify and analyze patterns that can guide strategies to effectively reduce food losses. The main aim of this paper was to propose, implement, test, and validate a prototype that makes it possible to identify and quantify soup waste in an institutional canteen, contributing to the development of strategies that lead to a reduction in its waste. The main contributions of the work presented in this paper are as follows: the creation of a dataset adapted to the scenario and food in question [23]; the training and validation of the ResNet-50 model combined with the Faster R-CNN model for the detection; the study of the extraction of characteristics from images; the definition of an architecture, as well as the hardware and software components of a low-cost prototype, based on Internet of Things and computer vision (CNNs); the description of the prototype's implementation and configuration process; the development of a web application based on microservices to visualize the data collected; and finally, the validation and proof-of-concept tests carried out on the prototype. The prototype proved to be effective, as it achieved the objectives proposed in its design. It is considered an economically viable and functional option. Together with the  
Figure 34: Detecting the bowl in the image and calculating the volume of wasted soup. application, it can be used to support decision making and contribute to more efficient management, with the aim of reducing food waste. Based on our results, we identify several opportunities for future work. To replicate this solution, it is necessary to adapt it to the environment in which it will be used. This includes creating a dataset suitable for the dishes that are sold, training the CNN with the dataset created, and adapting the hardware to the space in which it is used, for example, by adjusting the location of the sensor and camera. As for feature extraction, this also needs to be adapted to the implementation context. In the case of this solution, it was developed to detect circles and the color of soup to obtain an estimate of the soup's volume. It is important to recognize its limitations and consider possible error factors, such as inaccuracies in the detection of circles and the conversion of pixels to centimeters. This form of feature extraction can easily be replicated for other foods served in bowls, such as desserts, but would need to be adapted to other types of food served in other formats, such as plates. When training the ResNet-50 and Faster R-CNN model, the performance metrics used to evaluate it show extremely positive results since the model is working in a very controlled environment, where all the images in the dataset come from a single canteen, resulting in great similarity between them. To improve the model's performance, it would be advantageous to diversify the training environment by including images from several different canteens or food environments, which would help the model to generalize better to different scenarios. Additionally, increasing the diversity of the dataset, by incorporating a variety of lighting conditions, viewing angles, and food arrangements on the trays, would also be beneficial. Furthermore, the web application developed to visualize the data collected could be adapted to the needs of users, offering better support for decision making.