Revolutionizing Nutritional Management Through Food Scanning And Object Detection: A New Android Application For Adults
Abstract
The proliferation of mobile technology has led to the development of numerous applications aimed at promoting a healthy lifestyle, such as monitoring food intake and providing suggestions for a healthy diet. However, many of these apps require significant time and effort to manually input food items. To address this issue, we present the development of a new machine learning-based Android application that simplifies food management for adults, especially those in rural environments or with limited technical knowledge. The proposed application uses AWS Rekognition to scan food items and obtain nutritional information, such as the percentage of diabetes, cholesterol, and other key factors affecting health. The app also utilizes image recognition to detect fruits and vegetables, providing their nutritional contents. Additionally, for packed food items, the app scans the ingredients list to predict vital information
regarding the user’s health. The machine learning algorithm in the application helps in improving the accuracy of the scanned information and provides better nutritional recommendations. The application is designed to have a simple and user-friendly
interface, providing a convenient solution for managing food intake.
Keywords:
diet, scan, detection, machine learningPublished
Issue
Section
License
Copyright (c) 2023 International Journal on Emerging Research Areas

This work is licensed under a Creative Commons Attribution 4.0 International License.
All published work in this journal is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
How to Cite
Similar Articles
- Prayag Suresh, Sneha Susan Alex, Rojan Varghese, Thomas Zacharias, Shiney Thomas, Survey of Strabismus Detection Techniques , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Sandra Raju, Dr S Sruthy, A Reliable Method for Detecting Brain Tumors in Magnetic Resonance Images Utilizing EfficientNet , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Athira Sankar, Sajishma S R, Alan Raj, Vaishnavi A K, Reshmi S Kaimal, Hydro Sense: Empowering Water Quality Monitoring Through IoT And ML , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Krishnendu B, Sreelakshmi A, Sumayya Maheen, Zameel Hassan, Honey Joseph, Chatbot-Enabled Symptom Assessment: Revolutionizing Disease Diagnosis and Patient Care , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- ANU ROSE JOY, Christeena Antony, Dona Mariyam John, Anuja Sara Mathew, Christeen Mareia Paul, UnLocking Emotion Recognition in ASD Children: Analyzing Facial Expressions , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Anna Jose, Anit Devesiya, Albin Scaria Sabu, Anand Baby John, Prof.Maria Yesudas, AMIGO APPLICATION , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Lida K Kuriakose, Overview of Lip Reading Methods: Issues, Current Developments, and Future Prospects , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Joyal Joby Joseph, Michael Abraham Philips, Noel J Abraham, Steffi Maria Saji, Shiney Thomas, A Review of Parkinson Disease Detection Techniques , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- V Naveen, S Rekha, A Concise Review On E-Commerce Website For Visually Impaired , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Juby Mathew, Maria Jojo, Neha Ann Samson, Noell Biju Michael, Ron T Alumkal, PulseSync: IoT-Enabled Monitoring and Predictive Analytics for Healthcare , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
You may also start an advanced similarity search for this article.