Theses (Master In Health Informatics & Analytics)

Classification and identification of fruits and vegetables through image recognition technology has witnessed significant growth in recent years, and this advancement is driven by the importance of nutritional profiling in determining dietary recommendations, particularly for people managing diabetes. Various studies were done using image recognition technology to identify the images and create nutritional information on the scanned images. The nutritional information emphasized in all the previous studies were mainly calorie value and some included information on sodium, potassium, iron, calcium, vitamins, protein, fats, and carbohydrate. However, the existing literature falls short in comprehensively integrating image recognition of fruits and vegetables with their sugar content values and how it affects the blood glucose levels in individuals with diabetes. Therefore, this project aims to bridge these gaps and proposes a novel solution to educate individuals managing diabetes on nutritional values such as calorie value and sugar content in fruits and vegetables and how the consumption affects blood glucose levels through image recognition technology. It is carried out by creating a workflow that demonstrates image classification and recognition, as well as a web application in Jupyter Notebook using Python libraries. A Kaggle dataset which consists of 3825 images of fruits and vegetables was used in this project. Three pre-trained deep-learning models named AlexNet, MobileNet-v2, and YOLOv4 were chosen for the image classification as these models have given the highest accuracy percentage for fruits and vegetables image classification in previous literature. The AlexNet model has given an accuracy percentage of 48 %, whereas MobileNet-v2 has given an accuracy percentage of 94% upon training the dataset with 3825 images. Due to the limitation of hardware and labeling, the YOLOv4 model was not used to train the dataset for image classification.