Exploring Deep Learning–Based Models for Sociocultural African Food Recognition System - Grace Ataguba; Mona Alhasani; James Daniel; Emeka Ogbuju; Rita Orji (Human Behavior and Emerging Technologies)
Food recognition, a field under food computing, has significantly promoted people’s dietary decision-making and culinary customs. We present the design and evaluation of a sociocultural app for African food recognition using deep learning models such as transfer learning. Deep learning models have multiple processing layers that make them robust in image recognition. Based on this capability of deep learning models, we explored them in this study. A total of 3142 food image datasets were collected from three African countries: Nigeria, Ghana, and Cameroon. Using the datasets, we developed and trained a deep learning model for recognizing African foods. The model attained a test accuracy of 94.5%. The model was further deployed in a food recognition app. To evaluate the predictive ability of the app, we recruited 16 participants who were interviewed and subsequently used the app in the wild for 7 days. In a comparative evaluation between the app and human recognition capabilities, we found that the app recognized 71% of the instances of food images generated by the participants and tested with the app, while the human evaluators (participants) could only recognize 56% of the food datasets. Participants were mostly able to recognize some foods from their own country. Furthermore, participants suggested some design features for the app. In view of this, we offer design recommendations for researchers and designers of sociocultural food recognition systems.