## 4 Discussion  
The results affirm the effectiveness of the automated plate recognition system and demonstrate the feasibility of using deep learning in the creation of green schools. However, it is important to note that relying solely on limited manually annotated samples may not achieve high accuracy in plate recognition. In practice, issues such as misidentification can arise. Three key factors may contribute to lower recognition accuracy: (1) limitations in network selection and optimization for the \"plate recognition\" task; (2) an insufficient number of annotated samples and suboptimal annotation accuracy, making it difficult for the network to find the optimal parameter set; (3) challenges in deployment and expansion: environmental factors like lighting and complex backgrounds may affect recognition performance, and the system still has limitations in detecting food waste locations. To improve recognition accuracy, future research should focus on improvements in the three areas mentioned above, such as optimizing the network structure, increasing the number of high-quality annotated samples, enhancing the system's adaptability to complex environments, and improving its scalability. Figure 9: A complex scenario. The plate recognition network based on semantic segmentation has a unique feature: it processes input plate images on a per-pixel basis, identifying each pixel on the plate. Under conditions like limited sample size and unoptimized network parameters, this approach can lead to inconsistencies in pixel classification for the same target (see Figure 8). To address this, using a target detection-based plate recognition network could be a viable solution [19; 20; 21]. Unlike per-pixel recognition in semantic segmentation, target detection networks classify the entire object, effectively preventing inconsistencies within the same target. These networks can not only recognize object categories but also differentiate between objects of the same category, thus effectively distinguishing between similar but differently shaped objects and reducing the frequency of misclassification. Therefore, in future model optimization, employing target detection networks could ensure more comprehensive target recognition, avoiding misidentification of plates. This study performed necessary hyperparameter adjustments and optimizations through ablation experiments to ensure fairness between different methods [8; 18], but a comprehensive exploration of hyperparameter optimization has not been conducted. Future research could further explore the application of automated hyperparameter optimization techniques, such as Bayesian optimization and neural architecture search, to enhance model robustness and recognition accuracy. The problem of insufficient sample size has long been considered a key challenge hindering the full potential of deep learning technology. To tackle this, researchers have explored data processing methods like data augmentation to overcome difficulties in data collection and annotation [16; 22]. In this study, although the current dataset covers common cafeteria scenarios (such as simple and complex-shaped individual plates), the wide variety of food items and diverse plate shapes in school cafeterias pose challenges in sample acquisition. Additionally, manual annotation remains time-consuming and prone to inconsistencies. When using the coverage threshold method for labeling clean and non-clean plates, variations in food types (e.g., rice, vegetables, soup) and different forms of food residues (e.g., scattered rice grains, intact vegetable pieces, or residue with soup) may introduce discrepancies in shape and color, potentially leading to less precise annotations. The current dataset has demonstrated satisfactory performance in the canteen of Capital Normal University. However, to ensure the model's robustness across diverse university canteen environments, further dataset expansion is necessary.