# Food Waste Detection in Canteen Plates Using YOLOv11
###### Abstract  
This work presents a Computer Vision (CV) platform for Food Waste (FW) detection in canteen plates exploring a research gap in automated FW detection using CV models. A machine learning methodology was followed, starting with the creation of a custom dataset of canteen plates images before and after lunch or dinner, and data augmentation techniques were applied to enhance the model's robustness. Subsequently, a CV model was developed using YOLOv11 to classify the percentage of FW on a plate, distinguishing between edible food items and non-edible discarded material. To evaluate the performance of the model, we used a real dataset as well as three benchmarking datasets with food plates, in which it could be detected waste. For the real dataset, the system achieved a mean average precision (mAP) of 0.343, a precision of 0.62, and a recall of 0.322 on the test set as well as demonstrating high accuracy in classifying waste considering the traditional evaluation metrics on the benchmarking datasets. Given these promising results and the provision of open-source code on a GitHub repository, the platform can be readily utilized by the research community and educational institutions to monitor FW in student meals and proactively implement reduction strategies. artificial intelligence; food waste; food waste detection; instance segmentation; computer vision; YOLOv11 +
Footnote †: journal: Applied sciences  
Article  
Food Waste (FW) constitutes one of humanity's most significant unsolved problems, impacting both developed and developing countries. Most of the FW in developed countries is generated during the consumption stage, at which it has already reached the final consumer, and most of the FW in developing countries is generated at the production and post-harvest stages, due to the lack of modern technology [1]. On the other hand, following the orientation of sustainability and health, schools have a very important role at their core in educating people in being responsible future consumers. FW can also be a major problem in schools by the fact that it can mean that the students (especially children) could be losing important nutritional benefits from the wasted food [2]. These nutritional benefits are especially concerning when we think about the current major problems regarding obesity and overweightness, whose rates are rapidly increasing in every developed country [3]. Schools can also benefit from reducing FW because it will consequently reduce the costs of making that food and serving it to their consumers [4]. However, accurately and efficiently measuring FW in a school canteen can be challenging. In recent years, there has been a significant rise in Artificial Intelligence (AI), particularly in Computer Vision (CV) through open-source frameworks for object detection. YOLO [5] is one such framework and has been applied and explored in numerous fooddetection studies. However, a review of recent research papers and CV implementations reveals a notable gap concerning the practical applicability of CV (e.g., through YOLO [5]) in FW detection in canteen plates. In this context, the objective is to create and use a CV approach, namely using YOLO version 11 for Food Waste detection in canteen plates. A machine learning methodology was followed, starting with the creation of a custom dataset of canteen plates images before and after lunch or dinner. Data Augmentation (DA) techniques were applied to enhance the model's robustness. Then, a model was created in order to classify the percentage of Food Waste in a plate based on the categories of what is food and garbage. To evaluate the performance of the model, we used a real dataset as well as three benchmarking datasets with food plates in which waste could be detected. The model was evaluated with a real dataset and three benchmark datasets, and promising results were achieved in the calculation of Food Waste as well as the food items in canteen plates.