Authors :
Harshavardan. R.; Kanish. S.; Madhav Suta Adityan. G; Rathi Gopalakrishnan
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/yc7w8wud
Scribd :
https://tinyurl.com/4cccz3x5
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR921
Abstract :
Food monitoring and nutritional analysis play
a crucial role in addressing allergen-related health issues,
and their importancecontinues to grow in our daily lives.
In this study, we utilizeda convolutional neural network
(CNN) to recognize and analyze food images, assess the
nutritional content of dishes, and provide information on
potential allergens. Identifying food items from images
poses a significant challenge due to the wide variety of
foods available. To address this, we leveraged the
Logmeal API, which utilizes CNN to identify various
types of meals, their ingredients, and potential allergens.
Keywords :
Convolutional Neural Network (CNN), Food Image Recognition, Convolution Layers, Nutrition,Logmeal API,Food Allergies
Food monitoring and nutritional analysis play
a crucial role in addressing allergen-related health issues,
and their importancecontinues to grow in our daily lives.
In this study, we utilizeda convolutional neural network
(CNN) to recognize and analyze food images, assess the
nutritional content of dishes, and provide information on
potential allergens. Identifying food items from images
poses a significant challenge due to the wide variety of
foods available. To address this, we leveraged the
Logmeal API, which utilizes CNN to identify various
types of meals, their ingredients, and potential allergens.
Keywords :
Convolutional Neural Network (CNN), Food Image Recognition, Convolution Layers, Nutrition,Logmeal API,Food Allergies
