# Food Waste Detection in Canteen Plates Using YOLOv11
## 3 Methods and Methodology
### Proposed System  
We propose a platform for automatic monitoring of FW in a canteen, based on robust CV techniques for the automatic recognition of food items and the calculation of the waste percentage. One of the main advantages of the platform lies in the controlled environment of a canteen. Here, a weekly menu is provided, and both the trays and the plates have the same standardized format, simplifying the detection process. The platform is able to identify and categorize food items and estimate the percentage of FW on each plate. Following a machine learning methodology, the Figure 9 illustrates the user interaction and data processing involved in the platform. Specifically, there are five key steps in a canteen context environment:  
Figure 8: An example of CNN architecture for image classification. After finishing their meal, the users proceeds to the designated area where they place their tray. The tray is placed within the detection zone, clearly marked in the space. 2. Once the tray reaches the detection station, an image of the tray is captured using a camera positioned above it. The image is then sent to the server application. 3. The image is processed and the food recognition phase begins. First, the system detects the plate in the image to isolate the area of interest. Then, the segmentation module separates the food items from one another. After segmentation, a pixelation step refines the detection by analyzing food areas at a detailed pixel level, improving measurement accuracy. Finally, the identified regions are classified using the YOLOv11 model, which assigns each object a label and confidence score. 4. After processing the image, the calculation of FW begins, and then the percentage of waste for the meal is displayed. We consider the FoodArea refers to the area occupied by leftover food, PlateArea is the total area of the plate, and WasteArea represents the space occupied by inedible or discarded items. 5. Calculation: The final step involves calculating the percentage of FW, using the following formula: \[\text{Waste }(\%)=\frac{\text{FoodArea}}{\text{PlateArea}-\text{GarbageArea}} \times 100\] (6)
6. Finally, the user can remove the tray from the detection area, allowing the process to be initiated for another user. The Figure 9 illustrates the flow of image processing in our FW monitoring system, demonstrating the step-by-step process from image acquisition to waste calculation. Below is a description of the stages shown in the diagram:  
1. Input image: The process begins with the input image of a tray containing food. This image is captured by a camera positioned above the plate to provide a clear view of the meal, including the plate and its contents. 2. Plate detection: The second step focuses on detecting the plate. The system automatically identifies the boundaries of the plate within the input image. This is a crucial step in isolating the food items from the surrounding elements, such as the tray or other objects on the table. Recognizing the plate, the system narrows the area of interest for subsequent processing. Figure 9: Flow to calculate the waste percentage using the YOLO model. Segmentation, pixelation, and identification with YOLOv11: After detecting the plate, the system uses the YOLOv11 model to perform an integrated process of food segmentation, pixel-level analysis, and item identification. First, segmentation visually separates the different components of the meal. Then, the pixelation step refines this separation by analyzing the food areas with a higher level of precision, which is essential for reliable area measurement. Finally, the identification stage classifies the detected objects (such as rice, fork, meat), assigning each one a category and a confidence score. This integrated process allows for accurate distinction between consumed and discarded food, providing a solid foundation for the prediction and waste calculation stages. 4.