Food Nutrition Detection System Using Deep Learning and Fuzzy Logic
Year: 2020
Type: article
Abstract: Food is one of the most essential requirements for the survival of any living being on this earth. Nutrients present in the food provide chemical energy required for the proper functioning of various organs and for performing various physical activities which in turn keeps the body fit and active. To achieve this, proper intake of fresh, pure, nutrient-enriched and standard quality food is very essential. Poor quality food not only impacts the health and wellbeing of the person but also increased the risk of chronic diseases such as obesity, diabetes, heart failure, etc. Proper monitoring of food intake is one of the most effective ways of keeping track of the dietary habit of an individual. Existing methods especially sensor-based are however able to detect the nutritional value of the food but those systems are quite difficult to use in day to day life. In this paper, we are developing and designing an efficient food nutrition detection system that is built using deep learning and fuzzy logic. An android application will be designed as a user interface for displaying the results to the user. The proposed system gives an advantage of the least user efforts over the other report based/questionnaire system where the user is required to manually give input about their food intake habits regularly.
Source: Journal of Critical Reviews
Authors: Garima Koushik, K. Rajeswari
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W3042657854