- This is one of the study to discuss the relationship between nutritional ingredients identification in food and inspecting Calories through Machine Learning models to perform the data analysis, the experiments on real life dataset show that our method improves the performance with efficient accuracy .
- Additionally, Our system will recommend food for some Different Age groups.
- Our work is able to identify the Nutrition that we may get effected by lacking of certain nutritional ingredients in our body and recommends the food that can benefit the rehabilitation of those Age Groups.
- To achieve high accuracy and low time complexity, the proposed system implemented using CNN Machine Learning models.
- In the Existing System , One of the work proposed by Pouladzadeh et al. worked only for food items which were kept on a plate and calculated its calorie on the basis of its size, shape, color, texture respectively.
- But in reallife scenario, food is the combination of multiple ingredients and types.
- So, the task of calorie measurement for food items is indeed a challenging one.
- The complexity is higher in this model and time consuming.
DISADVANTAGES OF EXISTING SYSTEM
- 1.Low accuracy.
- 2.High complexities.
- 3.Time Consuming compared to other techniques.
- Proposed that we can identify the Nutrition information that may get effected due to lack of ingredients thus we recommend Calories according to the body’s intake on type of food consumption, minerals, and amount in grams.
- In this article, we propose a model which detects a given food image and displays the amount of calories in it. Further, it also displays a statistic analysis of the amount of calories consumed by user.
- There have been several number of models proposed for detection of food images, measuring the amount of calories present in food items and analyzing the calorie intake of a person by determining their daily dietary information as well.
- Several methods and algorithms have been implemented in the related works for calculating the same.
- The model, when trained convolutionally, generates the natural image samples which gives the better broad statistical structure of the natural images as compared with previously existing parametric generative methods
- Front End – Anaconda IDE
- Backend – SQL
- Language – Python 3.8
- Hardware Requirements:
•Hard Disk: Greater than 500 GB
- •RAM: Greater than 4 GB
- •Processor: I3 and Above