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
- Maria Sajeeve, Karthik Vinod, Kausalya Sumesh, Joby Jose, Minu Cherian, KALO:AI-Powered Precision in Nutrition Tracking , International Journal on Emerging Research Areas: Vol. 5 No. 1 (2025): IJERA
- Honey Joseph, A Survey and Analysis on Predicting Heart Disease Using Machine Learning Techniques , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Shiney Thomas, Elsa George, Alphonsa Francis, Anna Job, Ann Maria James, Wildlife Detection And Recognition Using YOLO V8 , International Journal on Emerging Research Areas: Vol. 4 No. 2 (2024): IJERA
- Febin Cheriyan, Deni Tom Jacob, Joanna Daniel, Haby S Mathews, Honey Joseph, Pneumonia Detection From Chest X-Rays Using Deep Learning : A Comprehensive Review , International Journal on Emerging Research Areas: Vol. 6 No. 1 (2026): IJERA
- Aniruddha Das, Avisikta Modak, The Carbon footprint of Machine Learning Models , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Honey Joseph, Aaron M Vinod, Abin Mathew varghese, Aby Alex, Aleena Sain, Crop Yield Prediction Using ML , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- B Bidhun, Deepak Dayanandan, Joel Joy, Vargheese Francis, Vani V Prakash, A Comprehensive Review of Lightweight and Attention-Driven Deep Learning Models for Automated Cataract Detection , International Journal on Emerging Research Areas: Vol. 6 No. 1 (2026): IJERA
- Jacob George, Jerin Xavier, Jovin J George, Joyel Xavier, Subini Therese Babu, Pharmaceutical Sales Forecasting using Machine Learning , International Journal on Emerging Research Areas: Vol. 6 No. 1 (2026): IJERA
- Rhea Maria James, Richy Sara George, Sayooj Kumar M, Nihal Muhammed Ayoob, Shan Krishna, Tintu Alphonsa Thomas, A Machine Learning Framework for Tumour Classification Using Transcriptomic and Multi-Omics Datasets , International Journal on Emerging Research Areas: Vol. 6 No. 1 (2026): IJERA
- Dr. Indu John, A Adithya, Alwin Rajan, Amal Biso George, Farhaan M Hussain, HEALTH GUARD-A Multiple Disease Prediction Model Based on Machine learning , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
You may also start an advanced similarity search for this article.
