An Advanced Android Based Trained Fruit and Vegetable Identification with Nutrition

Authors

  • Lakshmi Kalyani Kanulla, Sudheera Karri, Shyam Dutt Kondapanani, Yaswanth Reddy Challa, Dr. P. V. R. D. Prasada Rao

Abstract

This paper describes a process by using images taken by the video camera connected to the Device to identify fruit and vegetables to identify nutrition. The device assists customers to mark the desired fruit and vegetables with nutrients, such as calories, water content, proteins, carbohydrates, sugar, fibre and fat, and other information about fruits. It aims to limit the number of human machine interactions, speed up the identity and increase the usability, compared to current manual systems, of the graphical user interface. We achieved quicker deployment through android: business Android apps have a speedy, several-hour development period.The programming language provides a competitive aim Multiple platforms and makes it easy to port the software to multiple operating flexibility as well as scalability with the arrival of Android Studio. Different neural networks were tested and retrained for classification of an object. A heuristic assessment was conducted with multiple users to verify usability, concluding that the system introduced is easier to use than existing systems.The future extension of this project is voice assistance of fruits and vegetables nutrition.

Published

2020-12-31

Issue

Section

Articles