Food Image Classification and Calorie Prediction for Dietary Analysis

Abstract

This paper presents a unique method to classify food items and estimate the calorie content based on photos by combining Convolutional Neural Networks (CNNs) with image processing techniques. To improve the quality of the dataset, we first curate a wide range of food photographs from different presentation styles and cuisines. We do this by applying pre-processing techniques including image segmentation and feature extraction. Next, using the CNN\'s capacity to extract hierarchical features from raw pixel data, a custom deep CNN architecture is trained on this dataset to efficiently classify diverse food items and achieve high accuracy in differentiating between different dishes. Furthermore, we tackle the problem of calorie prediction by employing regression models that include features extracted from the CNN, which allows one to forecast the calorie content of a food item based on its visual attributes. Our technique intends to enable users to make more educated nutritional decisions and better control their caloric intake by fusing image categorization with calorie prediction. The suggested approach shows encouraging results in terms of food item classification accuracy and precision in calorie estimate. Its possible uses include promoting better eating practices and assisting individuals, dietitians, and the food industry with their dietary monitoring. In addition, our technology gives customers access to weekly calorie intake information, which can help them avoid obesity-related illnesses like diabetes.

Introduction

I. INTRODUCTION

It's getting more and harder to maintain a balanced diet in the fast-paced world of today. Due to their hectic schedules and the increase of meal delivery services, consumers frequently find it difficult to monitor their caloric intake and make wise dietary decisions. This has increased the likelihood of chronic illnesses like obesity, emphasising the need for creative ways to encourage better eating practices.In order to tackle this problem, we suggest a unique approach that makes use of image recognition technology to make it simple for users to keep an eye on their caloric intake. Users may automatically track the food and calories they consume by snapping a picture of their meal, which empowers them to make better dietary decisions.

This system is convenient and accessible since it makes use of cutting-edge picture recognition techniques that work on a variety of platforms.Furthermore, our system has elements designed to encourage healthier eating habits, going beyond simple calorie tracking.

In this implementation paper, we will outline the technical details of our system, including the image recognition algorithms, machine learning models, and user interface design. We will also present the results of our experiments and discuss the potential impact of our system on promoting healthier lifestyles and preventing diet-related diseases.

Through this project, we aim to contribute to the ongoing efforts to address the global challenge of unhealthy eating habits and improve public health outcomes.

The three major objectives addressed by this Project work are the proposal of intelligent techniques for identification  of the food type  and effective estimation of its calorie value, application of intelligent algorithms for prediction of diabetic disease and then to recommend the food items with required calorie in order to control the severity caused by the disease.

Since data mining algorithms provide suitable rules for decision making, data mining techniques are used in this Project work for performing effective food recommendation.

II. ARCHITECTURE

        The architectural design of our Food Image Classification, Dietary Analysis, and Calories Prediction system encompasses several key components seamlessly integrated to provide users with comprehensive dietary insights The food image classifier, which is the brains of our system, uses deep learning models trained on a carefully selected dataset to reliably identify different food items in uploaded photographs. Our dietary analysis module, which gathers nutritional information from reliable sources to give customers comprehensive details about the makeup of their meals, is built on top of this classifier.

       Our system also includes a neural network or regression model that is in charge of estimating the number of calories in the recognised food items. To produce precise calorie estimations, our predictive model incorporates extra nutritional characteristics in addition to the elements that were derived from the image classifier. The main point of contact is the user interface, which enables users to quickly submit pictures of their meals and get real-time dietary analysis.our system incorporates a regression or neural network model tasked with predicting the calorie content of the identified food items. This predictive model leverages the extracted features from the image classifier along with additional nutritional parameters to generate accurate calorie estimates. The user interface acts as the primary interaction point, allowing users to effortlessly upload images of their meals and receive real-time dietary insights and calorie predictions.

III. PROJECT IMPLEMENTATION

Our project focuses on developing an automated system for detecting and measuring the calorie content of food items from images, along with analyzing the daily dietary intake of individuals. The implementation utilizes Convolutional Neural Networks (CNN) as the primary method for image classification, achieving accuracy rates upto 95%.

We combined the FooDD and Food101 datasets, resulting in a diverse dataset with a total of 8,931 food images across 8 categories. Hand-picking frequently consumed food items ensured a relevant and practical dataset for our system. Our image recognition system relies on CNN, a class of Deep Neural Network specifically designed for image processing. The CNN structure consists of four convolutional layers with a 3x3 kernel size, followed by max-pooling layers to reduce image dimensionality. Two fully connected (FC) layers, including a Softmax classifier, are employed for final classification.

IV. RESULTS

  The proposed system demonstrated compelling results during implementation:

V. FUTURE SCOPE

The future prospects  are highly promising and dynamic. Significant progress has been made in refining models, expanding datasets, and optimizing accuracy. Practical implementation in real-world scenarios, such as dietary monitoring apps, is forthcoming, enabling direct user feedback. Wearable integration for real-time insights and personalized recommendations is on the horizon. Expansion into comprehensive nutrient tracking and behavioral insights is anticipated, alongside comparative analysis of different models to refine performance.

Conclusion

Food Image Classification and Calorie Prediction for Dietary Analysis\" is a ground-breaking technological breakthrough with significant applications in the field of dietary analysis. In order to enable people to make educated food decisions, this project uses machine learning to reliably classify food items from photos and predict their calorie content. As we get to the end of this voyage, it is clear that there is a lot of potential at the nexus of nutrition research and technology. The system has the potential to completely change the way we think about food because of its capacity to offer dietary insights, motivate healthy eating practices, and support personalised nutrition. We can create a future where nutritional choices are sustainable as well as healthier by adopting multi-modal analysis, integrating real-time input, and consistently improving accuracy. We can create a future where nutritional choices are sustainable as well as healthier by adopting multi-modal analysis, integrating real-time input, and consistently improving accuracy. As we proceed, we expect that this initiative will positively benefit society overall by contributing to the disciplines of public health and nutrition research as well as individual well-being. Here, at the intersection of technology and nutritional analysis, is where the road to a healthier and better-informed future begins.

References

[1] Singh A. et al., Food Image Classification and Calorie Prediction for Dietrary Analysis. International Journal for Research in Applied Science and Engineering Technology,2024

Copyright

Copyright © 2024 Aman SIngh, Khushi Magotra, Adishri Kadam, Aman Shukla, Dhanashree Phalke, Dipali Kirange. 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.