In the contents of the finalizing chapter for this thesis, this segment will discuss and provide you with
the necessary details and outlooks about this platform. During the discussion of these points the major
issues, workarounds and limitations will be discussed as well. Ideas about the implementation of this
project into the future, where the present framework can be adjusted according to future projects for a
viable commercial system will be discussed as well.
The determined goal of this project is to reduce time and make the system of tracking food nutrients
efficient. The first goal of minimizing the time wasted is reduced using the process of image processing,
to identify the food and give updates without a manual key logging process of the user. (
Technical deyake danna
)
The second goal was achieved via the process of using many entries to determine an average nutrition
content of each food, resulting in a more accurate readout.
By viewing the platform I developed, in comparison to the others available in the market, I have moved
in the direction to make the platform easier to understand and operate by automation of the process.
The platform has the ability to give aid to individuals of every age, without depending on their
technological knowledge to operate and have the aid of this application to achieve their goals. By using
different sources of measurements more than weight, gender and height I was able to implement an
algorithm that is much more suited to determine the nutritional requirement of each individual.
Compared to other applications this is an increase in accuracy. The image processing algorithm is under
the constant learning process, where given enough users and time the margin of identification can be
moved nearly to a perfect score. No other application in the market carries the ability to identify and
divide portions generally based on an image.