Computer vision solution for uniform adherence in gastronomy schools: An artificial intelligence case study

Abstract

This research study presents an innovative application of computer vision technology in culinary education to ensure consistent student uniform adherence, crucial to accomplishing hygiene, safety, and professionalism standards. The proposed approach utilizes the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to generate a computer vision application prototype to identify specific culinary uniforms components, such as chef's jackets, aprons, hats, and pants. The development process using LandingLens, a code-free platform, involves several stages: business and data understanding, preparation, modeling, evaluation, and deployment. The final model was training with 77 images, and the application deployment was tested using 38 images. Results demonstrate the potential of artificial intelligence to enhance operational efficiency and uphold professional standards in culinary education. Integrating computer vision addresses the challenges associated with manual monitoring and opens opportunities for broader adoption of technology in culinary pedagogy and training.