# Food Waste Detection in Canteen Plates Using YOLOv11
## 4 Experiments and Results
### Exploration and Results
Three images from each dataset are presented, organized in the same order as the datasets mentioned above and grouped by row. As an illustration, for the nine images presented in Figure 13, Table 6 presents the results regarding food waste percentage and the wasted food. The individual analysis of each image further highlights the model's performance in various scenarios. Figure 13a showed a low waste percentage, as it contained only small food remnants. Figure 13b,d,f detected 0% waste. In Figure 13b, the plate contained only a napkin, which was correctly classified as garbage. In Figure 13d, the plate showed no visible food leftovers. Finally, in Figure 13f the plate contained only bones, which were also accurately identified as inedible waste. These cases demonstrate the platform's ability to effectively distinguish between food, garbage, and non-food objects. In contrast, Figure 13c,e,g,i presented low to moderate waste percentages, reflecting the presence of small amounts of uneaten food, which the platform was able to detect with precision. Lastly, Figure 13h stood out with a 100% waste percentage, as the plate still contained the full meal, clearly indicating a case of complete non-consumption. Figure 12: Platform interface showing object detection (**a**) and waste calculation output (**b**). The platform output provides a visual representation of the platform's processing workflow. On the left side, the interface displays the detected image with annotated objects and their respective classifications, along with a clustered version of the same image to highlight the spatial distribution of the identified items. On the right side, the interface presents a dedicated section that explains the waste calculation formula used by the platform. This section outlines the core principles of the computation, including which elements are considered (e.g., food area, ined items such as garbage and utensils) and how the waste percentage is derived based on pixel areas. This combination of visual \begin{table}
\begin{tabular}{c c c} \hline \hline
**Image** & **Waste Percentage** & **Waste Food** \\ \hline Figure 13a & 1.70 & Baked Potatoes \\ Figure 13b & 0.00 & – \\ Figure 13c & 29.26 & Scrambled Eggs \\ Figure 13d & 0.00 & – \\ Figure 13e & 22.10 & Rice \\ Figure 13f & 0.00 & – \\ Figure 13g & 11.15 & Steak, French Fries \\ Figure 13h & 100.00 & Pasta, Beef \\ Figure 13i & 23.14 & Steak, French Fries \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Waste percentage detected on the sample images from Figure 13.  
Figure 13: Exploration results for some image detections from other datasets. and analytical feedback allows users to understand both the qualitative and quantitative aspects of the food waste detection process. Similarly, for the nine images presented in Figure 13, which come from external datasets, the same processing pipeline was applied using the proposed platform. As illustrated in Figure 14, the platform performed object detection, clustering, and subsequently calculated the estimated food waste percentage for each image. The results of this analysis are shown in the table below, providing further evidence of the platform's applicability and robustness when tested on data sources different from the original dataset. With the exception of some instances of misclassification, the images were generally processed correctly by the platform, and the obtained results largely reflect the actual food waste present on each plate, demonstrating the reliability and consistency of our approach. This precise alignment between automatic detection and real-world conditions is a key aspect of the platform's effectiveness. To determine whether the image was correctly classified or misclassified, we validated by observation. This means that if the plate is full of food, it is expected to retrieve 100% FW, and if it is half empty, 50%, and so on.