School cafeterias face significant challenges in maintaining operational efficiency while minimizing food waste within the educational sector. Currently, the methods available for counting dishes are predominantly manual or semi-manual. Accurately counting served plates is crucial for evaluating meal portions and planning food preparation, yet these methods frequently result in inaccuracies and inefficiencies. To address these challenges, this project introduces an innovative automated system for counting washed dishes used in the school’s dining hall. The system employs embedded systems equipped with a proper machine learning model to detect dishes placed in trays and count them at the washing station in the kitchens of schools. By automating the dish counting process, the system improves operational efficiency, reduces food waste, and provides precise data for meal planning, inventory management, and budget planning. Initial results show promising accuracy and efficiency, with the best model achieving an average precision of 0.71, a precision of 95.2%, and a recall of 70.5% using Google Cloud’s AutoML platform. However, further optimization is needed for real-world deployment. This project is constrained by limited time for labeling images and a budget of 300$. This project represents a collaboration between Linnaeus University, Kalmar Municipality, and SensIot Company, reflecting a shared commitment to sustainability through reduced food waste in educational institutions.