Food Processing and Calorie Detection

Abstract

Food is one of the most basic needs of all living things on Earth. People want the food they eat to be pure, fresh, and of a standard quality. The food processing industry's automation and standards-setting mechanisms ensure the quality of the food. Worldwide, people are becoming increasingly sensitive to food these days. An unbalanced diet can result in weight gain, obesity, diabetes, and other problems. As a result, numerous algorithms were developed to assess food images and calculate calories. This method proposes a powerful deep Convolutional Neural Network (CNN) architecture to locate and recognize local food photographs. Food is available in a wide range of flavors, textures, and shapes, which emphasizes how challenging it is to recognize food in pictures. Conversely, deep learning has become more and more well-liked as a successful picture-recognition method. We created a system for identifying foods and estimating calories, which uses a picture of a meal as input to determine a person's daily caloric intake. We provide a fresh dataset of the most well-liked foods, collected from publicly available web resources. Convolutional neural networks, or CNNs, are used as classifiers to identify foods, and we can determine a food's calorie content based on its gram weight. Keywords — Calorie estimation, deep learning, convolutional neural networks, and food detection and recognition.