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Essay: College of Computing and Informatics Technology Diet Track Application

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COLLEGE OF COMPUTING AND INFORMATICS TECHNOLOGY
DIET TRACK APPLICATION
By
GROUP 15

DEPARTMENT OF COMPUTER SCIENCE
SCHOOL OF COMPUTING AND INFORMATICS TECHNOLOGY

A Project Proposal submitted to the School of Computing and Informatics Technology For the Study Leading to a Project Report in Partial Fulfilment of the Requirements for the Award of the Degree of Bachelor of Science in
Computer Science of Makerere University

Supervisor:
Dr. Marriette Katarahweire
School of Computing and Informatics Technology, Makerere University
kmarriette@cit.ac.ug

DEC, 2022
GROUP MEMBERSHIP
SNo
Names
Registration Number
Signature
1
KISAKYE JULIUS
20/U/5671/EVE

2
AMPURIRE RONALD
20/U/5703/EVE

3
KAMUSIIME MOREEN
20/U/5767/PS

4
GUMA NELSON NDUHURA
20/U/5701/EVE

1.0 INTRODUCTION
1.1 Background to the Problem
As a growing economy, Uganda is going through a diet transition. More attention is being given to the increase in cases of diet related diseases or conditions such as diabetes and hypertension. In the article, Food Intake and Cardio-metabolic Risk Factors in Rural Uganda [1] the authors noted that the rural Ugandan population is most likely transitioning towards an unhealthier diet. This will lead to increase in the diet-related medical conditions and worsening the effects of diseases like Coronavirus thereby greatly increasing health expenses. Research [2] however shows that regular diet tracking is vital for healthier living.
Lately, Ugandans have been more aware of the impact of the food they eat, especially during the COVID-19 lockdown. This has led to more people looking for ways of monitoring what they eat. This follows a worldwide trend towards easier ways of tracking diets like mobile diet tracking. Applications [3] in other countries have been developed for this using image recognition and some with bar code readers for their well-documented foods and their nutritional information. However, these applications are not ideal for Ugandans since they have image recognition models that have not been trained on Ugandan food images. This means that Ugandan meals cannot be recognized. This project will focus on foods consumed in Uganda since there has not been any project like this in Uganda yet.

1.2 Problem Statement
There is no cheap and convenient way for Ugandans to regularly examine their diets and its effects on their health at the moment. So, Ugandans have been following a trend of eating food that has led to poor nutrition and other adverse effects on their health. As a result, this has spiked the rate at which diet related diseases or conditions such as diabetes, high blood pressure and chronic diseases such as hypertension and cardiovascular disease are increasing that cause illnesses, stress and some other health problems such as being overweight or obese in humans.
Poor food intake patterns and overweight are associated with different immediate complications and psychological disorders. A lifestyle with these diseases is very expensive for most Ugandans and in some cases deadly.
The price for regular dietary assessment for most people is high and keeping records is hard. The dietary information of Ugandan foods is also limited and the available applications rely on bar codes to scan packed food which might not work in Uganda and image recognition models that do not cater for Ugandan foods.

1.3 Objectives of the project
1.3.1 Main Objective
To develop an application that will track diets and record the nutritional value in the food Ugandans consume.
1.3.2 Specific Objectives
1. To create a dataset of Ugandan food images
2. To train a machine learning model to identify foods in the meal
3. To compute and approximate food nutrients in the meal
4. To design and develop an application to run, test and validate the model

1.4 Project significance
This project will grant Ugandans the ability to tell the nutritional components of the food they are to consume and make them more informed of their dietary trends. They will have an application that has been configured for Uganda and able to recognize Ugandan foods.
During the project, we will create a dataset of Ugandan food images. This will be of use in documenting Ugandan foods and use them in other image recognition projects. The project will also create a database of Ugandan dietary trends. The most common foods and the frequency of their consumption will be collected and stored. This information is very helpful to the health research community in Uganda.
For scholars and academic researchers, this study forms a basis upon which future research on diet intake may be established. The findings may be resourceful in providing viable information to academicians and researchers on inclusion of the diet tracking among people.

1.5 Project scope
The project will cover the most common foods and recipes available in Central and Eastern Uganda. This is because they are the only documented foods we could find and we can easily get pictures of them to train the image recognition model. We shall choose a few categories of those foods to focus on so that we can collect more images per category.
We shall collect about two thousand food images. The number will be increased or decreased depending on the resources available and effectiveness of the model. The small image sample dataset will therefore require us to use transfer learning, to make a model capable of identifying features in the images. The project will also focus on energy (in calories), protein and carbohydrates in the foods.

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