## 4 Discussion
This should encompass variations in tray types (e.g., material, compartment design), background conditions at collection points (e.g., window color, lighting conditions), and representative food categories (e.g., staple food preferences, residue patterns) to account for regional differences. To minimize these challenges, the system, through the logistics education platform, provides a user feedback service for the 'Clean Plate Campaign.' It employs incentive mechanisms like points redemption to encourage users to upload interpretation markers and plate sample data, providing suggestions for improvement of inadequate markers. This approach not only enriched the sample data and improved the network's recognition accuracy but also fostered high user engagement. According to user feedback, over 85% of users were satisfied with the point redemption mechanism, and more than 75% actively participated in data uploads and provided improvement suggestions. The high level of user involvement enhanced the sense of participation among students and staff, further contributing to the continuous refinement of dataset annotation. Compared to traditional methods like meal card exchange, online points redemption is more aligned with student needs. Automating the points system in place of manual operations reduces human, material, and time costs while ensuring objective recognition results. The rewards, transitioning from fixed meals to both material and spiritual incentives, effectively promote the progress of green school initiatives. In the practical deployment and expansion of the system, some issues still exist. Not only does the accuracy of tray recognition and adaptability in complex scenarios need improvement, but the system also faces new challenges when extending to broader dining and waste disposal scenarios. Variations in cafeteria lighting can cause plate edges to blur or colors to distort, reducing system performance. To address this, adaptive illumination enhancement algorithms (e.g., LIME) [10] or multispectral data fusion can be integrated to improve robustness under complex lighting conditions. Additionally, the dynamic cafeteria environment presents challenges such as changing utensil placement angles, dishware variations, and food residues obscuring plate contours, all of which impact model performance. Introducing adaptive learning mechanisms could enable the model to automatically adjust parameters in response to new data, enhancing generalization ability. To address the issue of partial occlusion caused by improper tray placement, the camera array layout can be optimized to achieve multi-angle coverage. Additionally, a guided tray placement platform can be designed to standardize user behavior. However, when the tray is at an extreme tilt angle or partially obscured by highly reflective tlabeware, vision-based recognition systems still face a risk of misclassification. To address this, future research could integrate weight sensing units into the visual system to construct a multimodal perception network. By combining visual features and mass variation signals, a cross-validation mechanism could be established--for instance, triggering weight change analysis when the confidence of visual recognition falls below a threshold--thereby significantly enhancing the system's fault tolerance in complex scenarios [23]. This multi-source information fusion strategy not only compensates for the limitations of a single sensor but also provides multi-dimensional data support for subsequent statistics on tableware recycling. In terms of system expansion, the current model primarily relies on data collected from fixed cameras in the canteen and has yet to cover broader dining and waste disposal scenarios (e.g., trash bins, dormitories). In the future, cameras or sensors could be deployed in waste collection areas and dormitories to enhance recognition capabilities. Additionally, the current model mainly focuses on leftover food on plates, overlooking the impact of individual and environmental factors on waste behavior.