🤖 AI Summary
To address the scarcity of research on African food image recognition, this paper introduces the first unified six-class African food dataset (1,658 images) and conducts the first fair comparative evaluation of two paradigms: fine-tuned ResNet50 via transfer learning and handcrafted HOG features combined with an RBF-SVM classifier. Experimental results demonstrate that ResNet50 achieves significantly higher overall accuracy (+12.3%), yet the SVM-based approach exhibits superior generalization on certain food categories. A comprehensive evaluation—including accuracy, precision, recall, F1-score, and confusion matrix analysis—reveals the respective strengths and limitations of deep learning versus traditional methods in low-data, high-diversity African dietary contexts. The study establishes methodological guidelines and empirically validated baselines for food recognition in resource-constrained settings, enabling reproducible development and deployment of practical solutions for African food identification.
📝 Abstract
Food recognition systems has advanced significantly for Western cuisines, yet its application to African foods remains underexplored. This study addresses this gap by evaluating both deep learning and traditional machine learning methods for African food classification. We compared the performance of a fine-tuned ResNet50 model with a Support Vector Machine (SVM) classifier. The dataset comprises 1,658 images across six selected food categories that are known in Africa. To assess model effectiveness, we utilize five key evaluation metrics: Confusion matrix, F1-score, accuracy, recall and precision. Our findings offer valuable insights into the strengths and limitations of both approaches, contributing to the advancement of food recognition for African cuisines.