Image Processing Model to Estimate Nutritional Values in Raw and Cooked Vegetables

Abstract

The availability of high-calorie foods with contentious nutritional content has led to a worldwide increase in chronic disease. Therefore, monitoring of eating habits and practising healthy eating habits is recommended. Clinical diet assessment methods and mobile calorie tracking apps can be used to record daily food consumption but are often not user-friendly. Convenient image-based assessment models are currently available to recognise and estimate the nutritional value of foods directly from food images, but they do not consider how nutritional value changes after cooking. Consequently, VegeNet, a multi-output InceptionV3-based convolutional neural network model has been developed, which estimates the nutritional values of cooked and uncooked vegetables. The explicit use of the cooking state is the main contribution of this work. This deep learning model successfully classifies the food images at 97% accuracy and estimates the nutritional values at 15.30% mean relative error, making it suitable as a visual-based added food assessment solution. This can help users save time and avoid under-reporting problems.

Keywords

• nutrition; dietary assessment
• convolutional neural network
• inceptionv3
• deep learning
• image processing