Food Recognition System Research Articles

A modified time adaptive self-organizing map with stochastic gradient descent optimizer for automated food recognition system

Numerous decades of study have been devoted to associating artificial intelligence and culinary type recognition. Automated food identification systems are significant in many disciplines, comprising dietary valuation, menu analysis, and nutritional tracking. In the past, traditional image analysis algorithms caused in poor classification accuracy, but deep learning methods have enabled the identification of food types and its constituents. This study proposed a novel method to develop food recognition competence and accuracy by connecting a Stochastic Gradient Descent (SGD) optimizer to a Modified Time Adaptive Self-Organizing Map (MTA-SOM). Food arrival differences subsequent from lighting, changing perspectives, and occlusions sometimes provide challenges to traditional food recognition algorithms. In this research, propose an MTA-SOM that learns and adapts to changing food item appearances by dynamically changing its topology over time. This research leverages the self-organizing possessions of SOMs and the fine-tuning properties of SGD by relating the MTA-SOM and the SGD optimizer, thereby maximizing the advantages of both techniques. The research method includes collecting a large number of food images from a difference of cuisines and presentation styles in order to assess the effectiveness of the proposed method. This proposed method performs an extensive test and connect MTA-SOM and SGD to present approaches of food recognition. Important advances in precision and robustness are produced as the system learns to recognize food items more precisely and adapts to changes in food appearance. By automating food detection with high precision and adaptability, our method could revolutionize our capability to interact with food-related data and offer important insights into dietary practices and nutritious decisions.

Large-scale Food Image Datasets and Food Recognition Systems: A Survey

With the development of the Internet, more and more people like to share their daily food, which makes the Internet rich in food image data. Many researchers collect food images on the Internet, label them, and combine machine learning and other methods to achieve food recognition systems. These food recognition systems can quickly help users record their daily diet and analyze their eating habits. The advanced food identification system can currently accurately identify hundreds of foods. At the same time, it can explore the food nutrition content and give food reference suggestions by accessing the food nutrition database. In this paper, we collected and evaluated large-scale food image data sets and food recognition systems.

McDonald's food target recognition and calorie display based on YOLOv5 algorithm

This research paper delves into the development and assessment of a novel food recognition and evaluation system tailored for McDonald's menu items, leveraging the capabilities of the YOLOv5 algorithm. The study demonstrates that the system can successfully identify McDonald's food items from images and seamlessly query calorie and nutritional information from a backend database. The data is then presented to the user, aiding in more informed dietary choices and promoting public health awareness. The system has particular utility for McDonald's customers, facilitating real-time decisions that align with individual health goals and nutritional requirements. Our experimental findings show a high degree of accuracy and efficiency, although the system's scope is currently limited to five key menu items. Future directions for this work include expanding the range of recognizable food categories and implementing user feedback mechanisms to refine recognition accuracy. Moreover, the paper discusses potential optimizations for reducing system response time and further enhancing the practical utility of the technology. This research serves as a significant step towards utilizing computer vision technologies for public health interventions, aiming to combat the rise of obesity and related diseases.

Exploring Deep Learning–Based Models for Sociocultural African Food Recognition System

Food recognition, a field under food computing, has significantly promoted people’s dietary decision‐making and culinary customs. We present the design and evaluation of a sociocultural app for African food recognition using deep learning models such as transfer learning. Deep learning models have multiple processing layers that make them robust in image recognition. Based on this capability of deep learning models, we explored them in this study. A total of 3142 food image datasets were collected from three African countries: Nigeria, Ghana, and Cameroon. Using the datasets, we developed and trained a deep learning model for recognizing African foods. The model attained a test accuracy of 94.5%. The model was further deployed in a food recognition app. To evaluate the predictive ability of the app, we recruited 16 participants who were interviewed and subsequently used the app in the wild for 7 days. In a comparative evaluation between the app and human recognition capabilities, we found that the app recognized 71% of the instances of food images generated by the participants and tested with the app, while the human evaluators (participants) could only recognize 56% of the food datasets. Participants were mostly able to recognize some foods from their own country. Furthermore, participants suggested some design features for the app. In view of this, we offer design recommendations for researchers and designers of sociocultural food recognition systems.

Computer Vision based Food Recognition with Nutrition Analysis

Computer vision based food recognition is a technology application Overall computer vision based food nutrition analysis i That uses machine learning algorithms to identify an classify different types of food in digital images or videos. This technologyhas a wide range of applications, including dietary analysis and tracking, nutrition labelling, and food safety. One of the key benefitsof food recognition is the ability to automatically extract nutritional information from food images. This can be particularly useful for individuals who are trying to maintain a healthy diet, as it allows them to easily track their daily caloric intake and ensure that they are getting the necessary nutrients. There are several different approaches to food recognition, including using machine learning algorithms to analyse images of food, using pattern recognition techniques to identify specific features of different types of food, and using deep learning techniques to classify images based on theirvisual characteristics. Overall, computer vision based food recognition hasthe potential to revolutionize the way we think aboutfood and nutrition, and could have a significant impact on public health. Which uses Computer vision is a field of artificial intelligence that enables computersto interpret and understand the visual world. One application of computervision is food recognition, which involves using a machine learning model to identify and classify different types of food based on their visual characteristics. Convolutional neural networks (CNNs) are a type of neural network that is particularly well- suited for image classification tasks. They can be trained to recognize patterns in images and classify them based on those patterns. To build a food recognition system using CNNs, you would need to first collect a dataset of images of different types of food. You would then use this dataset to train a CNN to classify the images into different categories, such as fruits, vegetables, meats, etc. Once you have trained your model,you can use it to classify new images of food and determine their nutritional content. This can be done by using the classification results tolook up the nutritional information for each type of food in a database. Over all, using a CNN for food recognition and nutrition analysis can be a useful tool for applications such as helping people tracks their dietary intake or providing recommendations for healthy meals. Keywords – Convolutional Neural Network, Deep Learning, IoU, Machine Learning, R-CNN

Food Recognition System: A New Approach Based on Wavelet-LSTM

An automated system for analyzing daily dietary intake is essential for human well-being and healthcare. This work presents a novel wearable necklace embedded with a piezoelectric sensor and a microcontroller to monitor food ingestion of users. To effectively represent the food ingestion patterns, the sensor signal is dynamically segmented using a bidirectional search technique. Each segmented food intake pattern consists of a chewing sequence and a swallow peak. We exploit wavelet transform to decompose the complex food ingestion patterns, collected by the sensor, into frequency sub-bands at discrete scales. The frequency sub-bands are used as sequences to train long short-term memory (LSTM) for the recognition of 5 food categories. Our proposed recognition model based on wavelet-LSTM recognizes 5 food classes with an accuracy of 98.1%

VTnet+Handcrafted based approach for food cuisines classification

In this paper, we propose a novel hybrid transformer architecture for food cuisine detection and classification. The work carried out within this paper develops a combination of Vision Transformer ensemble architecture with hand-crafted features, thereby making a hybrid Vision Transformer food recognition system. Recently, Vision transformers have been introduced as an alternative means of classification to convolutional neural networks. It performs pattern detection and classification without convolutions and interprets an image as a sequence of patches. The combination of Vision Transformer and hand-crafted features like GIST, HoG (Histogram of Oriented Gradients), and LBP (Local Binary Pattern) were employed on the dataset. The dataset was specifically created (for this work) from the public logging system. It consisted of 13 food categories with 400 images of Indian food items like Ghevar, Idli, Dosa, and much more. It helped to capture a variety of images from every domain and culture. This work made use of the common and readily available food items, which can further be increased by adding on the specialties (dishes) from different regions. Various experiments were performed on CNN with various classifiers like Random forest, and SVM. Further, we compared our proposed approach with several ensembles of CNN architectures. The experiments proved that our proposed approach outperformed the state-of-the-art ensemble CNN architectures for detecting food cuisines. The proposed hybrid approach achieved an accuracy of 94.63%, sensitivity 84.42%, specificity 95.23%, and kappa coefficient 0.93, which was the best amongst all approaches.

A Food Recognition System for Diabetic Patients Based on an Optimized Bag-of-Features Model

Healthcare delivery might be completely changed by computer vision technologies. It might be used, in instance, to assess the carbohydrate content of meals for 1 diabetes patients. Based on the bag-of-features (BoF) paradigm, this paper suggests a way for automated food recognition. A thorough technical analysis was carried out to identify and optimize the components that make up the BoF architecture in order to guarantee the best outcomes. Also estimated were the associated parameters. A visual dataset with about 5000 food photos that was divided into 11 groups was developed in order to construct and test the prototype system. Using the scale-invariant 3 feature transform on the HSV colour space, the optimised method calculates dense local features. After that, it utilizes a visual dictionary with 10,000 visual words to create a linear support vector machine classifier is then used to categorize the food photos. The system demonstrated the viability of the suggested strategy in a very difficult picture dataset by achieving a classification accuracy of about 88%. By giving diabetes patients a more precise and effective means to control their carbohydrate consumption, this cutting-edge technology might have a profound effect on their quality of life.

Food Dish Pedictor using YOLOv4

Abstract: In recent years, the demand for automated food recognition systems has increased due to the large variety of dishes available especially in Indian cuisine and also due to the growing awareness of the importance of a healthy diet. Identifying multiple food items from an image is a challenging task, especially when it comes to Indian cuisine, which is known for its diverse range of dishes and ingredients. The goal of this paper is to create a food dish predictor for five common Indian cuisines utilizing the cutting-edge object detection algorithm - YOLOv4. The five foods selected for this study are Aloo paratha, Biryani, Poha, Khichdi, and Chapati. A dataset is created by taking the images of selected dishes from various platforms such as social media and forming classes, which was then used to train the YOLOv4 model. To accurately train the machine to identify the dishes, the dataset was manually labeled. The YOLOv4-based food dish predictor that has been proposed could be put to use in a number of applications. The predictor can assure proper order fulfillment in food delivery services by recognising food plates in real-time from customer-provided photos. The predictor's capacity to automatically extract dish information can assist menu recognition software, saving restaurant personnel time and allowing for rapid menu revisions. Meal recommendation engines can utilize the predictor to provide personalized meal choices based on user preferences and previous eating experiences. Overall, the YOLOv4-based food dish predictor helps multiple sectors of the food business to increase productivity, consumer experiences, and decision-making capacities. The findings of this investigation show how well YOLOv4 can recognise and identify food items, particularly Indian food items.

In-TFK: a scalable traditional food knowledge platform, a new traditional food dataset, platform, and multiprocess inference service

Traditional Food Knowledge (TFK) is needed to define the acculturation of culture, society, and health in the context of food. TFK is essential for a human’s cultural, economic, and health aspects. Variations of ethnicity, culture, and lifestyle affect the diversity of traditional Indonesian food. Recognition of food is needed to maintain the sustainability of traditional food. Nowadays, there are many food dataset collections, but there needs to be a dataset that specifically collects standard food datasets. Our main contributions to the TFK research field are professional food image data acquisition, innovative development of an automatic, scalable food recognition system, and multi-process inference service. There are 34 variations of traditional foods from all regions in Indonesia that were acquired in this dataset. The dataset comprises 1644 high-quality images captured by professional cameras and 1020 by a smartphone. Several deep learning models are implemented in food recognition systems. This system can accommodate the addition and reduction of food variations in the knowledge recognition system and is capable of multiple concurrent requests at a time. The current prototype incorporates traditional types of food from Indonesia. However, the food model can also be expanded to other countries traditional foods. The automatic recognition systems are evaluated using several deep-learning network models. The experiment results have shown that the AUROC score is 0.99, and the request success rate can be improved by 70% with a multiprocess inference service.

Analysis of Nutrition Using Image Classification and CNN

Abstract: Food is a necessity for human survival, as evidenced by several medical conferences. People today are more concerned with their diet and food choices to prevent developing or existing ailments. In order to prevent obesity and chronic diseases that are linked to dietary consumption, nutrition management is a crucial component of day-to-day life. Due to people's dependence on smart technologies, using nutrition analysis tools helps individuals understand their everyday eating habits, study nutrition trends, and maintain a balanced diet. In this case, we create a deep prototype food recognition system to examine and research food components from photos of regular meals This research aims to analyze the nutrient content of meals using picture classification. Unlike traditional artificial neural networks, convolutional neural networks can estimate the scoring function directly from image pixels. There are a number of these layers, and the outputs are concatenated at various points to get the final tensor of outputs. We used convolutional neural networks (CNN) to automatically determine the nutritional value of images.

MEFood: A Large-Scale Representative Benchmark of Quotidian Foods for the Middle East

Automatic food recognition systems have been receiving increasing attention in the research community with the advancements in inductive learning (e.g., classification in computer vision) due to their applicability in the healthcare and hospitality industry. However, food recognition is challenging due to its fine-grained nature and its high correlation with culture, geo-location, and language. To make food recognition systems feasible for the Middle Eastern region, we present a large-scale dataset (MEFood) of commonly consumed food items in the Middle East, thereby providing a dataset for current development and establishing a benchmark for future research. We have also thoroughly examined the MEFood dataset highlighting its challenging aspects and its real-world nature. Additionally, we have conducted a thorough experimental study benchmarking the mainstream computer vision and mobile networks on classification, runtime, and resource utilization metrics. Our results highlight that EfficientNet-V2 achieves performance closer to the best-performing individual model on the MEFood dataset while having the least resource utilization and minimal inference times. Finally, we have performed a thorough error analysis study to glean additional insights about the networks and MEFood dataset.

Real-time Food Recognition and Documentation Android System for the Learning of Nigerian Foods using Deep Learning Method

Real-time Food Recognition and Documentation Android System for the Learning of Nigerian Foods using Deep Learning Method

Eliminate the hardware: Mobile terminals-oriented food recognition and weight estimation system

Food recognition and weight estimation based on image methods have always been hotspots in the field of computer vision and medical nutrition, and have good application prospects in digital nutrition therapy and health detection. With the development of deep learning technology, image-based recognition technology has also rapidly extended to various fields, such as agricultural pests, disease identification, tumor marker recognition, wound severity judgment, road wear recognition, and food safety detection. This article proposes a non-wearable food recognition and weight estimation system (nWFWS) to identify the food type and food weight in the target recognition area via smartphones, so to assist clinical patients and physicians in monitoring diet-related health conditions. In addition, the system is mainly designed for mobile terminals; it can be installed on a mobile phone with an Android system or an iOS system. This can lower the cost and burden of additional wearable health monitoring equipment while also greatly simplifying the automatic estimation of food intake via mobile phone photography and image collection. Based on the system's ability to accurately identify 1,455 food pictures with an accuracy rate of 89.60%, we used a deep convolutional neural network and visual-inertial system to collect image pixels, and 612 high-resolution food images with different traits after systematic training, to obtain a preliminary relationship model between the area of food pixels and the measured weight was obtained, and the weight of untested food images was successfully determined. There was a high correlation between the predicted and actual values. In a word, this system is feasible and relatively accurate for one automated dietary monitoring and nutritional assessment.

Real-time Food Recognition and Classification System to Aid Diabetic Patients - Systematic Review

Real-time Food Recognition and Classification System to Aid Diabetic Patients - Systematic Review

Feature Weighting Based Food Recognition System

Feature Weighting Based Food Recognition System

An Intelligent Approach for Food Standards Prediction Using Machine Learning

Abstract: Nowadays, standard intake of healthy food is necessary for keeping a balanced diet to avoid health issues in the human body. This project proposes a food recognition system that uses a convolution neural network as a base model for image prediction and then returns nutrition facts such as calories in the given single food image. Knowing the nutrition content of the food that we are consuming helps in maintaining balanced diet. We have aimed with a variety of food categories, each containing thousands of images, and through machine learning training to achieve higher classification accuracy. Firstly, we have planned to train and optimize a CNN, state-of-art model using Tensorflow, we are using CNN as the convolution layers are tweak able and easy to implement. Second, we adapt our model with GUI features as well as nutrition analysis.

Food Calories Estimation Using Image Processing

One of the most difficult aspects of obesity therapy is determining how much food obese individuals consume on a daily basis. A computer vision-based approach is used in this study to estimate calorie consumption from food photographs. Propose a Food Recognition System (FRS) for the calculation of calories and nutrient values. The system next analyses and categorises the photos to determine the type of food and portion size, and then uses that information to calculate the calories in the food. In today's food identification applications, emerging food categorization methods are critical. A new food recognition algorithm is provided for this purpose, taking into account the shape, colour, size, and texture features of the item. A better classification will be attained by using various combinations of these features. Dietitians can use this food calorie and nutrition assessment method to track and control their daily food intake. The texture, shape, and size properties of the food image are then retrieved using the CNN. The better the classification, the more accurately the calories of the food may be estimated..

Fine-Grained Food Classification Methods on the UEC FOOD-100 Database

The development of an automatic food recognition system has severalinteresting applications ranging from waste food management, to advertisement, to calorie estimation, and daily diet monitoring. Despite the importance of this subject, the number of related studies is still limited. Moreover, the comparison in the literature was currently done over the best-shot performance without considering the most common method of averaging over several trials. This article surveys the most common deep learning methods used for food classification, it presents the publicly available databases of food, it releases benchmark results for the food classification experiment averaged over five-trials, and it beats the current best-shot performance experiment reaching the state-of-the-art accuracy of 90.02% on the UEC Food-100 database. The best results have been achieved by the ensemble method averaging the predictions of ResNeXt and DenseNet models. All the experiments are run on the UEC Food-100 database because it is one of the most used databases, and it is challenging due to the presence of multifood images, which need to be cropped before processing. This article aims to contribute to automatic food recognition by presenting the most common algorithms used for food classification, introducing the main databases of food items currently available, and reaching the state-of-the-art performance in the best-shot classification experiment of the UEC Food-100 database. That is, this article improves the current best-shot performance by 0.44 percentage points, and fixes it to 90.02%. Furthermore, with the best of our knowledge, this is the first article to introduce to the research community comparison of performances of the classification experiment on the UEC Food-100 database averaged over five-trails. As expected, performance averaged is slightly lower thanthe best-shot one.

Mediterranean Food Image Recognition Using Deep Convolutional Networks.

We present a new dataset of food images that can be used to evaluate food recognition systems and dietary assessment systems. The Mediterranean Greek food -MedGRFood dataset consists of food images from the Mediterranean cuisine, and mainly from the Greek cuisine. The dataset contains 42,880 food images belonging to 132 food classes which have been collected from the web. Based on the EfficientNet family of convolutional neural networks, specifically the EfficientNetB2, we propose a new deep learning schema that achieves 83.4% top-1 accuracy and 97.8% top-5 accuracy in the MedGRFood dataset for food recognition. This schema includes the use of the fine tuning, transfer learning and data augmentation technique.