Efficient Fruit Calorie Measurement using Machine Learning

Abstract

Food is undeniably a fundamental necessity for all living organisms on Earth. Humans, in particular, seek freshness, purity, and standard quality in their food. To ensure these standards are met, the food processing industry has implemented rigorous standards and automation processes. With a growing global awareness of the impact of diet on health, individuals are increasingly mindful of their dietary choices. An imbalanced diet can lead to various health issues such as weight gain, obesity, and diabetes. Consequently, there has been a surge in the development of systems aimed at analyzing food images to determine calorie and nutrition levels. In this paper, a food portion recognition system is employed to accurately measure calorie and nutrition values. Users simply need to capture a picture of the food, which is then analyzed to detect the type of food portion. This is achieved through segmentation techniques, including skull stripping, followed by classification using support vector machine algorithms. This comprehensive approach ensures precise determination of calorie content, as well as identification of the type of energy present in the food.Overall, this system represents a significant advancement in the field of dietary assessment, offering a seamless and accurate means of monitoring food intake and nutritional values