Breadtec: An assistant detection app for common filipino bread varieties using yolo11/ Malcolm Xavier L. Dizon, Benj Anne T. Mameles, Rhea Miguela M. Manansala, Hana Joy Q. Marinduque, and Juan Gabriel Q. Segovia.--

Bachelor's thesis

College of Science.-- Bachelor of science in computer science: Technological University of the Philippines, 2025.

Includes bibliographic references and index.

Filipino bread varieties represent an important aspect of the country's culinary heritage,
yet many people struggle to identify specific types. This study developed BreadTec, a mobile
application that uses machine learning to detect and classify common Filipino bread varieties
from images. The research followed the Cross-Industry Standard Process for Data Mining
(CRISP-DM) methodology for model development and Agile methodology for mobile
application development. A YOLO11s object detection model was trained on a dataset of
Filipino bread images collected from local bakeries and web sources, then deployed to Google
Cloud Run for real-time inference. The Android application was developed using Android Studio
and integrated with Firebase for data storage.
The trained model achieved a mean Average Precision ([email protected]) of 0.909, with
detection accuracy exceeding 95% under optimal conditions and above 85% under challenging
conditions. User evaluation using ISO 25010 quality characteristics with 40 respondents showed
an overall mean score of 3.37 out of 4.0, surpassing target scores and indicating strong user
satisfaction across all measured quality attributes.
The study demonstrates the technical feasibility of using deep learning for cultural food
recognition applications. BreadTec effectively combines technological functionality with cultural
education, providing a digital tool for preserving Filipino culinary heritage while potentially
supporting local bakeries through enhanced consumer knowledge of traditional bread varieties.