# Food Waste Detection in Canteen Plates Using YOLOv11
## 5 Conclusions and Future Work  
This work focuses on the development of an innovative system for the automatic detection of food waste on school canteen plates, using Computer Vision CV techniques based on the YOLOv11 model. Through the creation of a custom dataset and three benchmarking datasets, the application of DA techniques, and benchmarking across different YOLO versions, the effectiveness of YOLOv11 was demonstrated in accurately identifying food and waste in controlled environments such as school canteens. The promising performance metrics obtained--mAP of 0.343, precision of 0.62, and recall of 0.322--as well as other evaluation metrics extracted from the model, confirm the system's potential. It can serve not only to reduce food waste but also as an educational tool and a decision-support resource for nutritionists and school administrators. The open-source availability of the code and trained models reinforces the project's commitment to sustainability, scientific transparency, and the encouragement of future research. This work presents a CV platform for FW detection in canteen plates, exploring a research gap in automated FW detection using CV models. The contributions are the following: it provides a recent literature review of the YOLO framework applications for food detection, food in plates detection, and food waste detection, and it provides an open-source GitHub repository, with an implementation of YOLOv11 for food waste detection in canteen plates, which can be freely utilized and explored for research purposes. Beyond its contribution to food waste mitigation, the system aims to generate valuable insights into consumption and rejection patterns of specific food items, providing a foundation for concrete actions to improve the nutritional quality of school meals. As future work, we propose expanding the dataset with a larger and more diverse set of images, including different types of plates, food items, and lighting conditions, in order to improve the model's generalization, as well as to explore images with noise. Additionally, we aim to optimize the system's accuracy through fine-tuning of the model's hyperparameters. It will be also interesting to explore other CV pretrained architectures (e.g., MobileNet, ResNet, EfficientNet among other) and compare with the implemented in this work. Further, we will complement the study with large language models in order to compare the performance of the percentage of waste calculation in canteen plates and to promote statements for sustainable food promoting such as the consumption of vegetables. A practical application to explore involves implementing the system in a real school environment using low-cost devices such as the Raspberry Pi. This solution would enable the automatic capture of images at the end of meals and their subsequent processing, providing an accessible and scalable alternative for continuous food waste monitoring. The use of a Raspberry Pi also facilitates integration with existing canteen systems, promoting automated analysis. Finally, it would be valuable to conduct longitudinal studies in schools to evaluate the impact of the system on reducing food waste and improving students' eating habits. Conceptualization, methodology, supervision, investigation, formal analysis and writing original draft preparation, J.R.; software development and visualization, J.F. and P.C. ; project administration, J.R., J.F. and P.C. ; resources and validation, J.R., all authors contributed equally to the formal analysis, investigation, writing review, and editing. All authors have read and agreed to the published version of the manuscript. This work has been financed by National Funds through the Portuguese funding agency, FCT--Fundacao para a Ciencia e a Tecnologia within project: UID/06121/2023. Not applicable. Data Availability Statement: Data are contained within the article.