Food Ingredient Detection Using Deep Learning

DOI

10.2991/978-94-6463-529-4_20How to use a DOI?

Keywords

Inceptionv3; Object recognition; Food Ingredients detection; LSTM; SVM; CNN

Abstract

Ingredients play a very crucial role in any dish, as they determine the flavor, taste, and texture of the dish, and they can impact the nutritional value of the dish. as if a dish includes many high-quality ingredients and protein sources that can provide necessary nutrients, vitamins, and minerals for a healthy diet. It’s very crucial to know which ingredients to use in a dish to get the desired outcome. The strategy we’ve taken in this paper can help resolve this issue. A model for identifying food ingredients was put into practise based on image datasets using convolutional neural networks (CNNs).This model involves CNNs to recognise the items included in training images of Indian sweet dishes. In order to expand the amount and diversity of the dataset, the model calls for gathering a dataset of 1000 images of Indian sweets dishes, preprocessing the images, and using data augmentation techniques. We then trained a Convolutional Neural Network (CNN) model using a modified version of the Inceptionv3 architecture, fine-tuned on the dataset using transfer learning. The results demonstrated a promising accuracy rate of 99.6% in ingredient detection, which is noteworthy given the dataset’s size of 1000 images. Consumers with dietary choices, such vegetarians or those who have food allergies, can benefit from ingredient detection utilizing CNNs. It is possible to provide healthy eating decisions by properly identifying the contents in a food item.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Akanksha Mane
AU  - Gayatri Sharma
AU  - Aditya Soraganvi
AU  - Anita Devkar
AU  - Roshani Raut
PY  - 2024
DA  - 2024/10/04
TI  - Food Ingredient Detection Using Deep Learning
BT  - Proceedings of the International Conference on Signal Processing and Computer Vision (SIPCOV-2023)
PB  - Atlantis Press
SP  - 218
EP  - 230
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-529-4_20
DO  - 10.2991/978-94-6463-529-4_20
ID  - Mane2024
ER  -