Thai food recognition and nutritious calculation on line chatbot with deep learning - Tangchoopong, Thanaphon; Shakulkhu, Supapong; Phuyawong, Jutamars (Rattanakosin Journal of Science and Technology, 2022)
The majority of people nowadays pay more attention to the nutrients they consume on a daily basis, and most individuals usually take their meal photos. However, the food photo does not utilize to provide the best benefit to building a personal nutritious record. This research presents a system for recording and calculating individual food nutrition from food photos via LINE Chatbot. The proposed system is divided into 2 parts: the first part is the development of a model for image recognition of Thai food by comparing the performance of the model trained with the YoloV5 algorithm and Faster RCNN, then selecting the best model to use in the system. The model was trained on a Thai food dataset including 3,329 Thai food images divided into 20 classes. The experimental result showed that in terms of Average Precision (AP) score at IoU equals 0.5 YoloV5 and Faster RCNN received 0.85 and 0.79 respectively. To find the number of calories received per day, the second part is the gathering of Thai food ingredient nutritious data to calculate food nutrition from recipes, then the system can record the personal daily calories so that it can provide food nutritious data to users via Line Platform.