Abstract:

Abstract- Food is an essential requirement for all living beings, and for humans, it is crucial that food is fresh, pure, and of standard quality. In recent years, there has been a growing awareness among people about the importance of a balanced diet, with an increasing number of individuals focusing on managing their nutritional intake. An unbalanced diet can lead to various health problems, such as weight gain, obesity, and diabetes. To address these concerns, several systems have been developed to analyze food images and calculate the calorie and nutritional content of meals. This paper presents an effective system for managing daily food intake, specifically designed to assist patients and dietitians in tracking and controlling their nutrition. The proposed system uses food images to estimate the calorie content and nutritional value by leveraging image processing, segmentation, and classification techniques. The system's core process involves capturing a picture of the food item, followed by segmentation to isolate the food portions. Next, the system uses skull stripping to enhance the image and then applies Support Vector Machine (SVM) classification to identify the type of food and calculate its nutritional and caloric content accurately. The food portion recognition system not only provides an accurate estimate of the total calorie intake but also identifies the type of energy in the food, such as carbohydrates, proteins, and fats. This approach improves the current methods of calorie measurement by automating the process and reducing the dependency on manual input, making it more efficient and user-friendly. the proposed system has the potential to greatly enhance food intake management, providing more precise and reliable data for individuals looking to monitor their diets. By incorporating image processing, segmentation, and advanced machine learning techniques like SVM, this system offers a promising solution to the challenges of nutrition and calorie tracking, contributing to better health and dietary management.

Description:

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