International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 07 | July 2022
p-ISSN: 2395-0072
www.irjet.net
Food Classification and Recommendation for Health and Diet Tracking using Machine Learning Dr.Manjula G1, Sukanya Umesh2, Suryashree T S3, Shubha M4 1Dr.Manjula
G, Dept of CSE, EWIT, Karnataka, India Umesh, Dept of CSE, EWIT, Karnataka, India 3Suryashree T S, Dept of CSE, EWIT, Karnataka, India 4Shubha M, Dept of CSE, EWIT, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------2Sukanya
Abstract - Many researchers have been published recently
Food, on the other hand, is deformable by nature and exhibits a wide range of appearances. Food images have a substantial intra-class variance and a low inter-class variance; traditional approaches fail to differentiate complex aspects. As a result, food identification is a difficult issue for which traditional methods fall short of distinguishing intricate features. CNNs can quickly recognize these traits and hence improve classification accuracy. As a result, this paper tries to classify food images using CNNs.
on food classification and recommendation separately, but combination of food classification and recommendation using deep learning is rare. The CNN algorithm is presented in this work because it is higher accuracy than other algorithms. In the present generation people are very concerned about their food habits in order to maintain their healthy balanced diet. This paper classifies Indian food images. The model/system uses a deep leaning process to train the machine. For this project the dataset is collected from Kaggle, UCI and some of the images from Google chrome, which contains 1000 images. The dataset is classified into 12 classes namely biryani, bisibelebath, butter naan, chats, chapatti, Dhokla, dosa, idly, noodles, upma, poori, samosa. On a different set of tests, the average accuracy is 86.33 percent. This paper also contributes to diabetic patients and also recommends the healthy note.
1.1 Related Work
Words: Food classification, Deep learning, convolutional neural network (CNN), Machine learning (ML), Image Processing.
The majority of food image classification relies on manually defined feature descriptions [3]. Food images, on the other hand, are tough to categorise, these methodologies' accuracy was generally low, and they couldn't discern a wide range of foods. Because deep learning is a full auto machine learning technique that is best suited to foods image processing, it outscored existing techniques in this sector [1,2,3]. A recent study on deep learning food recognition applications [4] was published.
1. INTRODUCTION
1.2 Proposed Methodology
People require automatic food labeling application due to technological advancements. Many academics have been aiming for autonomous food detection utilizing machine learning and recent advances in computer vision for this purpose. Most people have a propensity of overeating, which leads to a lack of physical activity. People are stressed and busy lives make it difficult to keep track of correct food dietary requirements, emphasizing the importance of proper food classification and information. Technology plays important roles are now necessary for proper food labeling, which can only be accomplished utilizing the increasingly popular deep learning technology. Not only for the social network area, is automatic food identification also an emerging research issue. Indeed, researchers are concentrating their efforts in this field due to the growing medical benefits. Automatic food recognition techniques will aid in the assessment of calories, food quality detection, and the development of diet monitoring systems to counter obesity, among other things.
The proposed methodology for food images classification is shown in Fig. 1 and each block is explained in this section. Framework consists of following phases: Food image datasets, Image pre-processing, Train CNN models and Food Classification & Recommendation.
Key
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2. FOOD IMAGE CLASSIFICATION In this block the dataset is taken from Kaggle and downloaded manually through Google, where some of the train and test images have noise, different colour intensity and images with the wrong tag. It is safekeeping for proper train and test phase and also, we rescaled the images to 50x50 pixels.
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