Ritu Agarwal, Tanupriya Choudhury, N. J. Ahuja, Tanmay Sarkar
{"title":"Hybrid Deep Learning Algorithm-Based Food Recognition and Calorie Estimation","authors":"Ritu Agarwal, Tanupriya Choudhury, N. J. Ahuja, Tanmay Sarkar","doi":"10.1155/2023/6612302","DOIUrl":null,"url":null,"abstract":"Every individual requires some sort of system that informs them about portions and calories of food, as well as providing them with directions on how to consume it. In our study, we propose a hybrid architecture that makes use of deep learning algorithms to forecast the number of calories in various food items on a bowl. This consists of three major components: segmentation, classification, and calculating the volume and calories of food items. When we use a Mask RCNN, the images are first segmented. Using the YOLO V5 framework, features are collected from the segmented images and the food item is categorized. In order to determine the dimensions of each food item, we identify the items first. In order to calculate the quantity of the food item, the estimated dimension must be used. The calories are then computed using the food item’s volume. The aforementioned approaches, which were trained on the dataset’s food images, that correctly identified and forecasted a food item’s calories had an accuracy of 97.12%. To Provide directions on how to consume food is expected by individual and will be completed after knowing intake of volume of food.","PeriodicalId":15717,"journal":{"name":"Journal of Food Processing and Preservation","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Food Processing and Preservation","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1155/2023/6612302","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"FOOD SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Every individual requires some sort of system that informs them about portions and calories of food, as well as providing them with directions on how to consume it. In our study, we propose a hybrid architecture that makes use of deep learning algorithms to forecast the number of calories in various food items on a bowl. This consists of three major components: segmentation, classification, and calculating the volume and calories of food items. When we use a Mask RCNN, the images are first segmented. Using the YOLO V5 framework, features are collected from the segmented images and the food item is categorized. In order to determine the dimensions of each food item, we identify the items first. In order to calculate the quantity of the food item, the estimated dimension must be used. The calories are then computed using the food item’s volume. The aforementioned approaches, which were trained on the dataset’s food images, that correctly identified and forecasted a food item’s calories had an accuracy of 97.12%. To Provide directions on how to consume food is expected by individual and will be completed after knowing intake of volume of food.
期刊介绍:
The journal presents readers with the latest research, knowledge, emerging technologies, and advances in food processing and preservation. Encompassing chemical, physical, quality, and engineering properties of food materials, the Journal of Food Processing and Preservation provides a balance between fundamental chemistry and engineering principles and applicable food processing and preservation technologies.
This is the only journal dedicated to publishing both fundamental and applied research relating to food processing and preservation, benefiting the research, commercial, and industrial communities. It publishes research articles directed at the safe preservation and successful consumer acceptance of unique, innovative, non-traditional international or domestic foods. In addition, the journal features important discussions of current economic and regulatory policies and their effects on the safe and quality processing and preservation of a wide array of foods.