FOOD IMAGE RECOGNITION AND CALORIE PREDICTION by IRJET Journal

FOOD IMAGE RECOGNITION AND CALORIE PREDICTION

Nisha P.K1 , Basil Kunjumon 2, Ajin Johnson3, Abhiram K Rajan4 , Adams Jacob 5

1Asst.Professor,Dept.of Computer Science and Engineering, Sree Narayana Gurukulam College of Engineering, Kadayiruppu, Kerala, India

2,3,4,5 Dept.of Computer Science and Engineering, Sree Narayana Gurukulam College of Engineering, Kadayiruppu, Kerala, India

Abstract - As the number of health issues linked to obesity and overeating rises, individuals are increasingly mindfuloftheirdietaryhabitstostaveoffconditionssuch as hypertension, diabetes, and cardiovascular diseases associatedwithexcessweightAccordingtodatafromthe World Health Organization (WHO), a staggering 2.8 millionpeoplesuccumbannuallytocomplicationsrelated to being overweight or obese. A pivotal aspect of any effective dietary regimen is monitoring calorie intake. Therefore,weproposeanoveldeeplearningmethodology forestimatingthecaloriccontentoffooditemsdepictedin user-captured images. Our approach employs a layered framework encompassing Image Acquisition, Food Item Classification, Surface Area Detection, and Calorie Prediction.

Key Words: Food Recognition, Calorie Estimation

1.INTRODUCTION

In the modern era, where technology intertwines seamlessly with daily life, there is a growing interest in leveragingartificialintelligence(AI)toaddresshealthand nutrition challenges. One such area that has gained significant attention is food image recognition coupled with calorie prediction. This project delves into the innovative realm of utilizing advanced computer vision techniques to recognize and analyze food items from images,subsequentlypredictingtheircaloriccontent.The significance of this project lies in its potential to revolutionize the way individuals manage their dietary habits and make informed choices about their nutrition. With the increasing prevalence of smartphones and the pervasive use of social media, people frequently capture and share images of their meals. However, manually tracking nutritional information remains a cumbersome task.ByharnessingthepowerofAI,weaimtostreamline this process and empower individuals to effortlessly obtain accurate and timely information about the caloric content of their meals. The primary objectives of this project include developing a robust food image recognition system capable of identifying various food items from images captured in diverse real-world scenarios. Additionally, the project aims to implement a caloriepredictionmodelthatleveragesmachinelearning algorithms to estimate the nutritional content of the

recognized food items. This dual approach not only addresses the challenge of food identification but also providesvaluableinsightsintothepotentialimpactonan individual's overall dietary intake. The project's methodology involves the use of convolutional neural networks(CNNs)forfoodimagerecognition,trainingthe model on a comprehensive dataset that encompasses a wide variety of cuisines and presentation styles. Subsequently, a calorie prediction model will be implemented,utilizingregressionalgorithmstrainedona dataset containing nutritional information for a diverse range of food items. The integration of these two components will create a holistic system capable of automating the process of calorie estimation from food images.

This project is anticipated to contribute to the emerging fieldofhealth-techbyprovidingapracticalsolutiontothe challenges associated with dietary monitoring. The outcomesofthisresearchcouldhaveasubstantialimpact onpromotinghealthiereatinghabits,aidingindividualsin achievingtheirfitnessandwellnessgoals.Asweembark on this exploration at the intersection of artificial intelligence and nutrition, we aim to pave the way for a more technologically empowered and health conscious society

2. LITERATURE SURVEY

In the paper [1] “Image-basedThaiFoodrecognitionand calorie estimation using Machine Learning” by Rattikorn Sombutkaew,OrachatChitsobhuk.Thispapercontributes to the existing body of literature by introducing a novel calorie estimation system embedded within an Android mobileapplication.Theproposedsystemutilizesafusion oftechniques,employingamobilecameratocapturefood imagesandleveragingthedepthinformationfromtheAR Core library. The segmentation of the food area is accomplishedthroughtheimplementationofafine-tuned MaskR-CNN,trainedonaspecificThaifoodimagedataset. Furthermore, the study incorporates various machine learning methodologies, including Linear Regression, SupportVectorRegression,K-NearestNeighbor,andDeep NeuralNetwork,toestimatethequantityoffoodcalories within each captured image. Notably, the Deep Neural Networkemergesasthemostpromisingmodel,exhibiting

International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

Volume: 11 Issue: 04 | Apr 2024 www.irjet.net p-ISSN:2395-0072

superior predictive capabilities characterized by the highestaccuracy,thelowesterrorrate,andthehighestRSquarescoreamongtheconsideredmethods.Thisfinding underscores the effectiveness of employing advanced machine learning techniques in the realm of calorie estimation, particularly when applied to the diverse and nuanceddomainoffoodrecognition,asevidencedbythe specificfocusonThaicuisineinthedataset

The research paper [2]"Image Based Food Calories Estimation Using Various Models of Machine Learning” proposedbyHaoyuHu,ZihaoZhangandYulinSong.This paperaimsthatforthefinalfewdecades,ithasbeenthe prevalentslantinChinathatindividualsareputtingmore consideration on making strides their soundness and directing calorie admissions for each dinner, so that we construct a show for calorie estimation of Chinese nourishment.Inan endeavortospecific ourconcerns on this issue, and with our incredible interface, we utilized question discovery to assess the calories tally of a few celebrated Chinese dishes as well as that of Western dishes. Based on the nourishment pictures and already characterized calorie information, we built a few imagebased calorie estimation models, which we trusted can preciselydistinguishthetitleoftheChineseandWestern nourishments and give their calorie admissions and formula,andatlonglastoffersupperarrangeexhortation for distinctive bunches of individuals. To distinguish the dish, we utilized the SSD (Single Shot MultiBox Locator) for real-time handling of protest discovery and classification. We too utilized a computer application called “labeling” to physically name our dishes with their individualuniquedishnames.Utilizingourmodels,clients can effortlessly calculate the calorie admissions of their wanted nourishments by taking photographs, sparing a partoftimecomparedtotheirordinarystrategies.

The research paper [3] "Comparison of Food Calorie Measurement Using Image Processing and Machine Learning Techniques” proposed by Boshra Alshujaa, Fatima AlNuaimi,WathiqMansoorand ShadiAtalla.This paperlooksathowwelldifferentcomputerprogramscan guesshowmanycaloriesareinfoodbylookingatpictures of it. Additionally, we will compare the most commonly used food picture collections and methods for counting calories.Thestudylooksathowwellcomputerprograms canrecognizefoodinpictures andwhatkindofpictures work best for training them. It uses the latest research from the past five years to suggest the best way to use techniques and data to create a food image system that works well. When comparing different machine learning programs,wefoundoutthatCNNandSVMarethebestfor recognizingfoodimages.In addition,the bestcollections of food pictures to use for teaching and testing ML programs that process food images are Food-101, UECFood100, and ECUSTFD. In terms of figuring out how manycaloriesareinfood,usingamathematicalmodelwas

thebestwaytodoit.Thisstudyshowshowtomakefood picture apps better by using good algorithms, data, and strategies.

Thepapertitled[4]"MachineLearningbasedapproachon Food Recognition and Nutrition” proposed by Zhidong Shen,AdnanShehzad,SiChen,HuiSun,JinLiu.Thispaper saysthattoday,it'simportanttoeathealthyfoodtostay balanced and avoid getting fat. In this paper, we have createdanewsystemusingmachinelearningthatcansort foodpicturesandguesswhatkindoffooditis.Thispaper suggests using a special kind of computer model to sort foodintodifferentgroups.Thismodelwouldbeapartofa systemusedtotrainthecomputertorecognizedifferent typesoffood.Themaingoalofthenewmethodistomake the pre-training model more accurate. The paper describesabasicsystemthatusestheclientservermodel. Thecustomerasksfortheservertofindanimageandthen the server works on it. The prototype system has three main parts: a module that uses a pre-trained model to classify things, a module that uses text data to estimate attributes,andamodulethatworksontheserverside.We tried lots of different types of food and used computer trainingtogetbetteratrecognizingthem.

The research paper [5] The authors Simon Mezgec and Barbara Korousic Seljak proposed a project on “Using DeepLearningforFoodandBeverageImageRecognition”. Lately,deeplearninghas becomethebestatrecognizing foodimages.Inthispaper,weexplainhowournewdeep learning system, called Nutrient, and our method for classifyingfakefoodinimages,canhelpthefield.NutriNet was taught using a big collection of food pictures, which hadmoretypesoffoodthanearlierstudies.Itwasalsothe firsttoidentifypicturesofdrinks.

[6] The literature on food image recognition and calorie predictionusingdeeplearningapproachesencompassesa range of studies focused on developing efficient and accuratesystemsfordietaryassessment.Researchinthis area has explored various deep learning architectures, including convolutional neural networks (CNNs), for recognizing food items from images captured by smartphonesorotherdevices.Thesestudieshighlightthe evolution of food recognition systems, from traditional methods to modern deep learning techniques, and emphasize the importance of high-quality datasets, preprocessing techniques, and model evaluation metrics. Additionally, there is a growing body of research addressingthechallengesofcalorieestimationfromfood images,withafocusonimprovingaccuracyandusability forpracticalapplications.Overall,theseliteraturesources underscore the potential of deep learning-based approaches to revolutionize dietary assessment and promote healthier eating habits, contributing to the mitigation of public health issues such as obesity and relateddiseases.

International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

Volume: 11 Issue: 04 | Apr 2024 www.irjet.net p-ISSN:2395-0072

[7]Theliteraturesurroundingfoodimagerecognitionand calorie prediction using convolutional neural network (CNN) approaches is rich and diverse, reflecting the growing interest in leveraging deep learning for dietary assessment. Several studies have demonstrated the efficacyofCNNsinaccuratelyidentifyingfooditemsfrom imagescapturedbysmartphonesorotherdevices.These systems typically involve preprocessing techniques such as segmentation to isolate food regions, followed by feature extraction and classification using CNN architectures. Researchers have emphasized the importance of high-quality datasets for training CNN to assess performance accurately. Additionally, efforts have been made to address challenges related to incomplete calorie estimation, often due to limited availability of volume and mass records in food image datasets. Integratingsupportvectormachines(SVM)withCNNshas shown promise in categorizing food items into specific categories, such as burgers or pizzas, enhancing the granularityofcalorieprediction.Motivatedbythesocietal impactofobesityandrelatedhealthissues,thesestudies underscore the potential of CNN-based systems to facilitatedietarymonitoringandpromotehealthiereating habits. By providing users with real-time nutritional information and calorie estimates, these systems aim to empower individuals to make informed choices about theirfoodintake,ultimatelycontributingtotheprevention ofobesityandassociateddiseases.

[8] The literature on food recognition and calorie estimation using image processing techniques highlights the significance of accurate dietary assessment in addressing obesity and related health issues. Obesity, often linked to high-calorie food consumption and sedentary lifestyles, poses numerous health risks, particularly in children and adolescents. To combat this, variousapproacheshavebeenproposed,emphasizingthe importance of monitoring calorie intake and making informed dietary choices. Image-based systems offer a promising solution by leveraging convolutional neural networks (CNNs) for food recognition and calorie estimation. These systems are trained on large datasets comprising diverse food categories and utilize CNNs to accuratelyidentifyfooditemsfromimages.Byassociating standard calorie values with recognized food items and considering their weights, these systems provide rapid and precise calorie estimates, enabling users to monitor their nutritional intake effectively. Challenges in this domainincludeaccuratelyrecognizingsimilarfooditems and managing large datasets for training. However, the benefits of such systems, including precise food recognition and rapid calorie estimation, outweigh these challenges. Future research directions may involve converting web applications into mobile apps for increased accessibility, expanding the range of trained foodcategories,andimprovingtherecognitionofcomplex foodcompositions.Overall,theapplicationofCNNmodels

in image recognition demonstrates superior accuracy comparedtotraditionalmachinelearningmodels,offering apromisingavenueforaddressingdietary-relatedhealth concerns.

Table -1: COMPARISON OF LITERATURE SURVEY

NAME ADVANTAGES DIADVANTAGES

[1] imagebased Thai Food recognition and calorie estimation using Machine Learning

1. Accurate Recognition: The system accurately recognizes Thai food items, enhancing user experience.

2. Calorie Estimation: Provides accurate calorie estimation for Thai food, aiding in dietary management.

3. Potential for Health Monitoring: In addition to estimating calorie content, the system could potentially be expanded to provide other nutritional information, such as macronutrient breakdowns or allergendetection, to support users in making informed dietary choices.

[2] Image Based Food Calories Estimation Using Various Models of Machine Learning

1.User-Friendly Interface:Utilizing images for food calorie estimation provides a more intuitiveanduserfriendly experience compared to

1. Limited Food Scope: The system may struggle with recognizing nonThai food items, limiting its versatility.

2. Dependency on Thai Food Dataset: Relies heavily on a specific Thai food dataset, potentially limiting generalizability.

3. PrivacyConcerns: Users may have concerns about privacy and data security,especiallyif the system requires uploading images of their meals to a remote server for analysis. Ensuring the protection of user data and implementing robust privacy measures is essential tomitigate theseconcerns.

1.Accuracy Challenges: Estimating the calorie content of a mealbasedsolelyon images can be inherently challenging due to variationsinportion

International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

Volume: 11 Issue: 04 | Apr 2024 www.irjet.net p-ISSN:2395-0072

manual input methods.

2.Efficiency: Image-based calorie estimation can save time for userscomparedto traditional methods that require detailed manual input of each ingredient. This efficiency encourages consistent trackingofdietary intake, which is crucial for maintaining a healthylifestyle.

3.Diverse Food Recognition:

Machine learning models can be trained on large datasets to recognize a wide variety of foods from different cuisines and cultures. This diversity allows the system to provide accurate calorie estimates for meals beyond a specific cuisine, catering to a globaluserbase.

[3 Comparison ofFood Calorie Measurement UsingImage

1.AccurateCalorie Estimation: Combining image processing and machine learning techniques can lead to more accurate calorie estimation compared to traditional

sizes, cooking methods, and ingredient composition. Machine learning modelsmaystruggle to account for these factors accurately, leading to potential inaccuracies in calorieestimates.

2.Limited Food Recognition:Despite advancesinmachine learning, the system may still encounter difficulties in accurately recognizing and categorizing certain foods, especially those with complex orambiguous visual characteristics. This limitation can affect the overall reliability of the calorie estimation process.

3.Dependency on Data Quality: The accuracyofmachine learning models heavily depends on the quality and diversity of the trainingdata.

Processing andMachine

Learning Techniques. methods.

2.Automation: Theintegrationof image processing and machine learning allows for the automation of the calorie measurement process, reducing the need for manualdataentry and human intervention.

3.Customization and Adaptability: Machine learning models can be trainedondiverse datasets to recognize a wide variety of foods and adapt to different dietary preferences and cultural cuisines. This customization allows the system to provide accurate calorie estimates for a broad range of meals, catering to the needs of diverse user populations.

as significant computational resources for training and optimization.

2.Accuracy Challenges: Despite advancements in image processing and machine learning, accurately estimating the calorie content of food from images remains a complex and inherently uncertaintask.

3.Data Dependency: The accuracy of machine learning models relies heavily on the qualityanddiversity of the training data. Insufficient or biased datasets may lead to suboptimal performance and generalization errors, particularly for foods that are underrepresented or have complex visual characteristics.

1.Complexity of Implementation: Developing an effective system for food calorie measurement using image processing and machine learning techniques requiresexpertisein both fields, as well

[4] Machine Learning based approach on Food Recognition and Nutrition proposed.

1.Automation: Machine learning algorithms can automate the process of food recognition and nutrition analysis, reducingtheneed for manual data entry and human intervention. This automation saves time and

1.Data Dependence: The accuracy of machine learning models depends heavily on the quality andquantity of the training data. Insufficient or biased datasets may lead to poor performance and inaccurate nutrition analysis,particularly

International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

Volume: 11 Issue: 04 | Apr 2024 www.irjet.net p-ISSN:2395-0072

resources, making it more efficient for individuals and organizations to track their dietaryintake.

2.Accuracy: Machine learning models can be trained on large datasets of labeled food images, allowing them to accurately recognize and classify a wide variety of foods. This accuracy improves the reliability of nutrition analysis compared to manual methods, which may be prone to human error.

3.Real-Time Analysis: Machine learning algorithms can provide real-time feedback on the nutritional content of foods, enabling users to make immediate and informed decisions about their dietary choices.

[5]Using Deep Learningfor Foodand Beverage Image

1.High Accuracy: Deep learning models, particularly convolutional neural networks (CNNs), have shown remarkable accuracy in image recognition tasks,

for foods that are underrepresented or have complex visual characteristics.

2.Overfitting: Machine learning modelsmaybecome overlyspecializedto the training data, leading to overfitting and poor generalization to new, unseen foods. Regularization techniques and careful validation are necessary to mitigate the risk of overfitting and ensure the robustness of the model.

3.Interpretability: Complex machine learning models such as deep neural networks can be difficult to interpret and understand, making it challenging to explaintherationale behind their predictions.

Recognition. including food and beverage recognition. They canlearncomplex patterns and features from images,leadingto highly accurate recognition results.

2.Versatility:Deep learning models can be trained to recognize a wide variety of food and beverage items, including different cuisines, dishes, and packaging variations. This versatility

especially for finegrained food categories or niche cuisines.

Computational Resources: Training deep learning modelsforfoodand beverage recognition often requires significant computational resources, including powerful GPUs or TPUsandlarge-scale distributed computing infrastructure. This can pose challenges for individuals or organizations with limited access to suchresources.

Data Requirements: Deep learning modelsrequirelarge amounts of labeled training data to achieve high accuracy. Collecting and labeling such datasets can be time-consumingand labor-intensive,

[6] Food Image Recognition and Calorie Prediction Using CNN Approach

3.Automation: Once trained, deep learning models can automate the process of food and beverage recognition, reducingtheneed for manual labeling or data entry. This automation saves time and resources, making it more efficient forbusinessesand individuals to analyze and manage their food-relateddata.

-1 HighAccuracy: CNNs are wellsuited for image recognition tasks duetotheirability to capture spatial hierarchies of features in images.Theyhave

Overfitting: Deep learning models are prone to overfitting, where they memorize training data rather than learning generalizable patterns. Regularization techniques, data augmentation, and careful model selection are necessary to mitigate overfitting and ensure robust performance on unseendata.

1.Limited Accuracy: While CNNs are effective at recognizing food items from images, accurately predicting the calorie content of those items is more

International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

Volume: 11 Issue: 04 | Apr 2024 www.irjet.net p-ISSN:2395-0072

demonstrated high accuracy in recognizing food items from images, which is essential for accurate calorie prediction.

2.Automated Calorie Estimation: By combining image recognition with calorie prediction models, the system can automate the process of estimating the calorie content of food items from images.

3.User-Friendly Interface: Food image recognition and calorie prediction using CNNs provide a user-friendly interface for tracking dietary intake. Users simply need to take a photo of their meal, and the system will automatically estimate the calorie content, making it convenient and accessible for a wide range of individuals.

challenging. Factors suchasportionsize, cooking methods, and ingredient variations can affect the accuracy of calorie prediction, leading to potential inaccuracies in the system'sestimates.

2.Data Dependency: TheaccuracyofCNN models depends heavily on the qualityanddiversity of the training data. Insufficient or biased datasets may lead to poor performance and generalization errors, particularly for foods that are underrepresented or have complex visual characteristics.

3.Interpretability: CNN models are often considered "black-box" models, meaning their internal workings are difficult to interpretorexplain.

[7] Food Recognition and Calorie Estimation

Using Image Processing

1.Accuracy: Image processing techniques can accurately identify and segment food items within

1.Complexity: Developing an effective image processing system for food recognition and calorie estimation requires

images, enabling precise calorie estimation based on portion sizes and ingredient composition. This accuracyiscrucial for providing reliable nutritional information to users.

2.Automation:

Once trained, image processing algorithms can automate the process of food recognition and calorieestimation, reducingtheneed for manual data entry and human intervention. This automation saves time and resources, making it more efficient for individuals and organizations to track their dietaryintake.

2.Accuracy Challenges: Image processing algorithms may struggle to accuratelyrecognize and segment food itemsinimageswith complex backgrounds, occlusions, or lighting conditions. Variationsinportion sizes and ingredient composition can also affect the accuracy of calorie estimation, leading to potential inaccuracies in the system's predictions.

3.Dependency on Data Quality: The accuracy of image processing algorithms depends heavily on the qualityanddiversity of the training data. Insufficient or biased datasets may lead to poor performance and generalization errors, particularly for foods that are underrepresented or have complex

3.Real-Time Analysis: Image processing algorithms can provide real-time feedback on the nutritional content of foods, allowing users to make immediate and informed decisions about their dietary choices.This realtime analysis is particularlyuseful for individuals managing specific health conditions or dietary expertise in computervisionand machinelearning,as well as significant computational resources for training and optimization. The complexity of implementationmay pose challenges for individuals or organizations without specialized knowledge or resources.

International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

Volume: 11 Issue: 04 | Apr 2024 www.irjet.net p-ISSN:2395-0072

restrictions.

1.1High Accuracy: Deep learning models, particularly Convolutional Neural Networks (CNNs), have demonstrated high accuracy in image recognition tasks, including food recognition. They can learn complex patterns and features from images,leadingto highly accurate recognition results, which is crucial for accurate calorie estimation.

[8] Food Recognition and Calorie Estimation Using Deep Learning

2.Automation: Once trained, deep learning models can automate the process of food recognition and calorieestimation, reducingtheneed for manual data entry and human intervention This automation saves time and resources, making it more efficient for individuals and organizations to track their dietaryintake.

3.Scalability:Deep learning models can scale to handle large datasets and high volumes of images, making

.Data Requirements: Deep learning modelsrequirelarge amounts of labeled training data to achieve high accuracy. Collecting and labeling such datasets can be time-consumingand labor-intensive, especially for finegrained food categories or niche cuisines.

2.Computational Resources: Training deep learning models for food recognition and calorie estimation often requires significant computational resources, including powerful GPUs or TPUsandlarge-scale distributed computing infrastructure This can pose challenges for individuals or organizations with limited access to suchresources

3.Interpretability: Deep learning models are often considered "blackbox" models, meaning their internal workings are difficult to interpret or explain This lack of interpretability can beadisadvantagein critical applications where transparency

them suitable for applications with a vast amount of food images, such as dietary tracking apps, recipe databases, and food inventory management systems. and accountability are essential, such as healthcare or foodsafety.

3. FUTURE SCOPE

ThefutureofAI-poweredfoodrecognitionholdsexciting possibilities, including enhanced nutrient analysis, allergendetection,andseamlessintegration withkitchen appliances. These advancements are poised to revolutionizethewayindividualsapproachnutritionand dietarymanagement.

4. CONCLUSIONS

AI-poweredfoodrecognitionandcaloriemeasurement arereshapingdietplanning,offeringpersonalizednutrition solutions that empower individuals to make informed dietary choices. As technology continues to evolve, the potential for improving health and wellness through AIdrivennutritionislimitless.

REFERENCES

[1] SombutkaewandChitsobhuk'ssystemintegrating ARCoredepthinformation andMaskR-CNNfor Thaifoodrecognitionandcalorieestimation.

[2] Hu, Zhang, and Song's model for calorie estimation of Chinese and Western dishes utilizingSSDforreal-timeobjectdetection.

[3] Alshujaa et al.'s comparison of food calorie measurement using CNN and SVM on datasets suchasFood-101andUEC-Food100.

[4] Shen etal.'s machinelearningapproach for food recognitionandnutritionestimationemployinga client-servermodel.

[5] Mezgec and Seljak's deep learning system NutriNetforfoodandbeverageimagerecognition.

[6] For "Food Image Recognition and Calorie Prediction Using Deep Learning Approach" by Mrs.Anuja.D.Tawlareetal.Tawlare,A.D.,Ranjan, P., Shaikh, A. S., Jain, S., & Maruti, S. S. (Year of publication)."FoodImageRecognitionandCalorie

International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

Prediction Using Deep Learning Approach. DOI: https://www.doi.org/10.56726/IRJMETS33887

[7] For "Food Image Recognition and Calorie Prediction Using CNN Approach" by Mr. Pranjal Ranjan et al.Ranjan, P., Shaikh, A. S., Jain, S., Shinde,S.,&Tawlare,A.D.(Yearofpublication). "Food Image Recognition and Calorie Prediction Using CNN Approach. https://www.doi.org/10.58257/IJPREMS31091

[8] For "Food Recognition And Calorie Estimation UsingImageProcessing"byS.Sumaetal..Suma,S., Bharathi,M.,Harikumar,G.,&Deepak,B.(Yearof publication). "Food Recognition And Calorie EstimationUsingImageProcessing.Publishedin: Vol-6 Issue-1, 2020, International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE),ISSN(O): 2395-4396 ,Website: www.ijariie.com, Page numbers:1292-1296