International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023
p-ISSN: 2395-0072
www.irjet.net
WEB BASED NUTRITION AND DIET ASSISTANCE USING MACHINE LEARNING Mrs.M.Buvaneswari,M.E1, S.Aswath2, P.Karthik3, M.Mohammed Raushan4 1Assistant Professor, Dept. of Computer Science and Engineering, Paavai Engineering College, Tamil Nadu, India 2Student, Dept. of Computer Science and Engineering, Paavai Engineering College, Tamil Nadu, India
Student, Dept. of Computer Science and Engineering, Paavai Engineering College, Tamil Nadu, India Student, Dept. of Computer Science and Engineering, Paavai Engineering College, Tamil Nadu, India ---------------------------------------------------------------------***--------------------------------------------------------------------2. RELATED WORK Abstract - The importance of food for living had been 3 4
discussed in several medical conferences. Consumers now have more opportunities to learn more about nutrition patterns, understand their daily eating habits, and maintain a balanced diet. Due to the ignorance of healthy food habits, obesity rates are increasing at tremendous speed, and this leads more risks to people’s health. People should control their daily calorie intake by eating healthier foods, which is basic thing to avoid obesity. Although food packaging comes with nutrition (and calorie) labels, it’s still not very efficient and conveniant for people to refer to App-based nutrient systems which analyzes realtime images of food and analyze its nutritional content which can be very handy. It improves the dietary habits and helps in maintaining a healthy lifestyle. This project aims at developing web based application that estimates food attributes such as ingredients and nutritional value by classifying the input image of food. This method uses deep learning model (CNN) to identify food accurately and uses the Food API's to give the nutritional value of the identified food.
The existing system enables users to track their food intake and exercise. It has a large database of food items and can calculate the calorie and nutrient content of meals. it provides users with a personalized meal plan and support from a community of other users. It assigns points to foods based on their calorie, protein, fat, and fiber content. It provides users with prepackaged, portioncontrolled meals and snacks. It offers several different meal plans to meet different dietary needs.. it generates personalized meal plans based on users' dietary preferences and goals. It also provides recipes and grocery lists to make meal prep easier. It can be helpful in providing guidance and support for individuals looking to improve their nutrition and dietary habits. The authors propose a food recognition system that uses feature extraction and ensemble learning techniques. The system takes as input an image of a meal and uses feature extraction techniques to extract features such as color, texture, and shape. The system then uses ensemble learning techniques to combine the output of multiple classifiers to predict the food items present in the meal. The algorithm used in this project is Feature Extraction and Ensemble Learning Techniques.
Key Words: Diet Assistance, Machine Learning, Diet Recommender System, Food Recognition, Image processing, Feature Extraction.
1.INTRODUCTION
3. PROPOSED SYSTEM
Obesity has increased two-fold since 1980 and became the fifth highest cause of death in each year. WHO (World Health Organization) noted that about 2.8 million adults every year to experience deaths caused by obesity. That is because humans consume food without regard to the needs of calories and nutritional content. Some application had been developed in purpose to monitor caloric and nutritional needs. However, that applications are not easy to use because before users can use that applications they must know the name of the food. The aim of the project "Web based Nutrition and Diet Assistance using Machine Learning is to create a tool that can accurately identify and classify food items based on images, measure their calorie and nutrition values, calculate the user's BMI, and generate personalized diet recommendations.
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To overcome the limitations of existing system, the proposed system "Diet Assistance" is a web-based food item prediction system that aims to provide users with a convenient and efficient way to track their calorie and nutrition intake, as well as calculate their BMI and receive personalized diet plans. The system makes use of various computer vision techniques, such as preprocessing, region proposal network (RPN), gray-level co-occurrence matrix (GLCM) feature extraction, and convolutional neural network (CNN) classification. The system takes an input image of food items, preprocesses it using various techniques such as RGB to grayscale conversion, resizing, noise removal using a Gaussian filter, and binarization. Then, RPN is used to detect food in the image and GLCM is used to extract features of the food items such as shape,
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