Authors: Dr. Ashoka K, Keerthana S, Sumangala K M, Laxmi Angadi, T Kavya
DOI Link: https://doi.org/10.22214/ijraset.2022.44916
Certificate: View Certificate
Nowadays, standard intake of healthy food is necessary for keeping a balanced diet to avoid health issues in the human body. This project proposes a food recognition system that uses a convolution neural network as a base model for image prediction and then returns nutrition facts such as calories in the given single food image. Knowing the nutrition content of the food that we are consuming helps in maintaining balanced diet. We have aimed with a variety of food categories, each containing thousands of images, and through machine learning training to achieve higher classification accuracy. Firstly, we have planned to train and optimize a CNN, state-of-art model using Tensorflow, we are using CNN as the convolution layers are tweak able and easy to implement. Second, we adapt our model with GUI features as well as nutrition analysis.
People are very keen on measuring weight, healthy diets, and staying away from obesity, there is an increasing demand for food calorie measurement.
Adult obesity is increasing at an alarming rate. The main source of obesity is the difference between dietary intake and the energy people get from the diet. High-calorie intake may be injurious and lead to various diseases. Hypertension, heart attack, diabetes, obesity, hypertension, high cholesterol Breast, colon and prostate cancers are caused by high calorie intake. High calorie intake is the second leading cause of cancer. Dieticians have determined that the standard intake of a number of calories is required to keep the right balance of calories in the human body.
As reported by the world health organization, more than 110th of the adult population in the world is obese. Obesity is a medical condition in which excess body fat has accumulated to the extent that it may have a negative effect on health. If the amount of food a person takes daily is higher than the amount of Energy utilized then we can say that the respective person is becoming obese. Obesity and being overweight are interconnected to many dangerous and chronic diseases. In 2013, The American Medical Association officially declared obesity as the disease that has serious consequences on patients health and therefore requires medical treatment. Therefore, daily intake measurements are important for losing weight and maintaining healthy diet and weight for normal people. Only a timely measurement of daily food consumption can make obese people lose weight in a healthier way, and can also make healthy people better healthy. Abstract Nowadays, standard intake of healthy food is necessary for keeping a balanced diet to avoid obesity in the human body. In this paper, we present a novel system based on machine learning that automatically performs accurate classification of food images and estimates food attributes. This paper proposes a deep learning model consisting of a convolutional neural network that classifies food into specific categories in the training part of the prototype system. The main purpose of the proposed method is to improve the accuracy of the pre-training model.
A. Problem Statement
In this growing digital world it is very important to keep the track of calorie intake in the form of food As and how the world is growing digitally problems like obesity , weight gains etc are also equally growing it becomes more inevitable. The system uses image processing and computational intelligence for food item recognition, convolutional neural network has been applied in food classification and resulted in a high accuracy.
The Objectives of our project are :
C. Proposed System
The main aim of the project is to build a new approach of image classification system to positively identify different standards in food. In this project, we first perform different pre- processing techniques on food categories to enhance clarity of the images, then we perform training of the model, testing of the model and classification of food based on standards.
II. LITERATURE SURVEY
III. SYSTEM DESIGN
A. System Architecture
The figure 1 depicts the architectural diagram of Food Standard Prediction system. The framework can be comprehensively sorted into following significant stages:
B. Use case Diagram
A Use Case Diagram is a lot of situations that reflect a client-frame relationship. A use case chart shows the entertainer-to-use relationship. Usage cases and on-screen characters are the two main elements of an usage case diagram. An on-screen character refers to an user or other person connected with the demonstrated process. A use case chart in figure is an out- of - the-box perspective that speaks to some activity each module will perform to complete an errand.
A. Methodology Of Proposed System
The proposed food detection and recognition model is based on the implementation of the concepts of image processing and computer vision. These concepts are bundled together to get the desired result, the implementation.
The first Convolutional 2D layer consists of 32 kernels of 3x3. Takes an input of size 100x100x3 where 100x100 is the rescaled size of images from Food101 dataset. RGB, the color aspect of the image is denoted by 3. The second layer with a pool size of 2x2 is the max- pooling layer. For better feature extraction, these layers are repeated once again. Then, to get more filtered images for the fully connected layers, the kernel’s size is increased from 32 to 64. Two fully connected layers are used next with 128 and 90 neurons respectively. To prevent overfitting, dropouts have been added in between the dense layers. All the convolutional 2D layers and the fully connected layers have an activation function of Rectified Linear Unit (ReLu). The last final layers consist of 101 neurons that are equal to the number of categories in our Food101 Dataset. The model predicts the category to be the one with the highest probability Transfer learning is the reuse of a pre-trained model for a new problem, it is very popular nowadays in deep learning because it can train deep neural networks with relatively little data, and it is very useful in data science because of most real problems. , you don't have millions of data points marked to train these complex models
Convolutional Neural Network (CNN)
Convolutional neural framework is one of the principal categories for the photos affirmation and pictures portrayals. Articles disclosures, affirmation faces, etc., are a bitof the regions where CNNs are commonly utilized. The 6 shows the Neural Network with various convolutional layers. 1n certainty, the possibility of significant learning CNN models t can be used for train and attempted, every data picture will be adhered to the course of action of convolution layers with procedures (Kernals), Pooling, totally related layers (FC) by applying Soft max work can arrange an article with probabilistic characteristics runs some place in the scope of 0 and 1. The underneath figure is a complete stream of CNN to process an information picture and requests the articles subject to values.
We must have a basic idea on how the human brain recognizes an object in spite of its varying attributes from one another. Our brain has a complex layer of neurons,each layer holds some information about the object and all the features of the object are extracted by the neurons and stored in our memory, next time when we see the same object the brain matches the stored features to recognize the object, but one can easily mistake it as a simple “IF- THEN” function, yes it is to some extent but it has an extra feature that gives it an edge over other algorithms that is Self-Learning, although it cannot match a human brain but still it can give it a tough competition . Image is processed using the Basic CNN to detect the Calories in Food.
VI. FUTURE SCOPE
As far as the future enhancement is concerned, the task of classification can be improved by removing noise from the dataset. The same research can be carried out on larger dataset with more number of classes and more number of images in each class, as larger dataset improves the accuracy by learning more features and reduces the loss rate. The weights of the model can be saved and used to design a web app or mobile app for image classification and further calories extraction of the classified food
In this work, we trained our model with different test sets and got a mean average accuracy of about 85%. A lot of data augmentation and segmentation that has to be performed to clean pixel values in other classifiers which is not mandatory in CNN. Once our trained model has produced the most probable output, we call the Nutritionix and return the one serving food item nutrition facts on the user’s screen. The whole process takes at most 5 seconds. The model proposed in this paper is doing well with the given data set both in terms of speed and accuracy but state of-art models such YOLO can also be integrated with it to get better results and accuracy. To achieve fast multiple object detection with boundary boxes in real time in a single image can also be achieved by using pre-trained models. Also, an effective and reliable system can be developed for real time food recognition and calorie estimation system. In this research study, the Convolutional Neural Network, a Deep learning technique is used to classify the food images in to their respective classes. The dataset considered is the Indian food dataset. The Flowchart shows the flow of operation done to detect the particular livestock and count them accordingly that is shown in result. Here first the image is captured by using a camera and which is then converted to a grey scale image to make it feasible for comparison with the existing data set values.
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Copyright © 2022 Dr. Ashoka K, Keerthana S, Sumangala K M, Laxmi Angadi, T Kavya. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET44916
Publish Date : 2022-06-26
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here