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Nutritional Value extraction of food exploiting computer vision and near infrared Spectrometry
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Resumo(s)
The population growth in the last few decades has led to the development of urban
areas, which induced an increased difficulty in finding quality food. The difficulty in
finding quality nourishment and a growing offer in the fast-food industry due to the
fast pace at which life is lived in big cities has caused increasing obesity and sedentary
lifestyle. In 2016 more than 1.9 billion adults aged 18 years and older were overweight[1].
However, this tendency has started to reverse, and with the increasing concern for
diseases such as obesity and diabetes, people started return to shopping in farmers mar kets and choosing wisely the locals where they eat, which led to the development of more
healthy fast food chains. This new tendency has made new technologies appear that were
created to help improve customer choices and facilitate choosing the best food items that
have the best quality.
This dissertation will analyse the different devices and solutions in the market, such
as near-infrared sensors and computer vision. The objective of this dissertation is to
build a system that can detect which type of food item we choose and obtain nutritional
information.
The development begins with researching the different options of small devices that
already exist in the market and with which a person can take shopping and assist them by
obtaining the nutritional information, such as SCIO or Tellspec. This device cannot detect
which type of food is being analysed, so human interaction it is still needed to obtain the
best results possible. However, it can return the nutritional information necessary for the
first part of this dissertation’s development. Besides being small (palm-handed), these
sensors are also cheap and faster compared to equivalent laboratory equipment.
The second objective of this dissertation was developed to solve the lack of detection
of which type of food is present in the module. To solve this problem and taking into
account the objective, it was decided to use computer vision and, more specifically, image
recognition and deep machine learning applied in food databases.
This dissertation’s main objective is to create a module that can classify and obtain
the nutritional information of different types of food. It also serves as a helping hand in
the kitchen to control the quality and quantity of the food that the user ingests daily.
There will be an exhaustive testing session for the near-infrared sensors using different
types of fruits to prove the concept. For the computer vision, it will be applied a deep
learning algorithm with supervised training to obtain a high accuracy result.
Descrição
Palavras-chave
Near-infrared sensor Detection Classification Module Food Nutrition