Mobile Food Image Classification with Allergen Information using EfficientNetB0

Abstract

Food allergens represent a significant public health concern because they can trigger severe and potentially life-threatening reactions in sensitive individuals. Seven allergens focus on this study, which consist of tree nuts, shellfish, milk, peanuts, soybeans, eggs, and wheat. However, restaurant menus in Malaysia and many other regions often lack explicit warnings about allergenic ingredients. This study addresses this critical gap by developing a comprehensive mobile application for real-time food image classification that integrates EfficientNetB0 with personalized allergen information and safety alerts. The model was trained on 7,556 food images across eight food categories based on Kaggle/recipe datasets and achieved 96% test accuracy. The native mobile app integrates camera capture and gallery upload, provides instant classification, and confidence-scored predictions using a 70% threshold, and displays user-specific allergen symptom warnings through food-allergen mapping based on clinical sources and classification history. Experimental evaluation demonstrated promising classification performance and functional reliability of the mobile application. This mobile-deployed solution offers a practical approach for supporting safer food choices and improving public health awareness, while aligning with UN Sustainable Development Goal 3