In this final review ppt we have usecase diagrams

final review ppt with use case diagrams

1. LITERATURE SURVEY S.No Title of the Paper Problem Statement Method/ Algorithm Used Advantages Disadvantages 1. Deep Neural Network for Food Image Classification and Nutrient Identification Food image classification and nutrient identification using deep neural networks. CNNs ( ResNet , VGG, Inception) | Deep learning Scalability, Feature extraction, High accuracy Data dependency, Black box effect, Limited interpretability 2. Natural Language Processing and Machine Learning Approaches for Food Categorization and Nutrition Quality Prediction Food categorization and nutrition quality prediction comparing NLP and ML approaches with traditional methods. RNNs + CNNs | Hybrid NLP-DL Text-image fusion, Richer understanding, Personalized recommendations Limited image focus, Specific app comparison, Less robust to noise

2. S.No Title of the Paper Problem Statement Method/ Algorithm Used Advantages Disadvantages 3. Advances Towards Automatic Detection and Classification of Parasites Microscopic Images Automatic detection and classification of parasites in microscopic images using deep convolutional neural networks. CNNs, Transfer Learning, Attention Mechanisms,Deep learning Efficient feature learning, Domain adaptation, High accuracy Medical domain specific, Technical complexity, Overfitting 4. Application of Deep Learning Methods for Recognizing and Classifying Culinary Dishes in Images Recognition and classification of culinary dishes in images using deep learning methods. Pre-trained CNNs Deep learning Fast training, Transfer learning, Good performance Limited architecture details, Narrow food range, Potential bias 5. An Automated Image-Based Dietary Assessment System for Mediterranean Foods Development of an automated image-based dietary assessment system for Mediterranean foods. CNNs + Food databases, Hybrid DL-Database Accurate portion analysis, Detailed nutrition, User-friendly Limited dataset, Generalization, Less flexible to new foods

3. EXISTING SYSTEM The existing system for food image classification utilizes deep learning techniques, specifically Transfer learning and pre trained CNN . However it faces challenges such as low accuracy and efficiency, Limited dataset, Less flexible to new foods, adaptability, scalability and lack of robustness. To improve the performance, consider augmenting the data to enhance model robustness, optimizing model hyper parameters and testing with various types of food images. Fine tuning the existing convolutional neural networks may also help to enhance the model accuracy.

4. PROPOSED SYSTEM Our proposed system encompasses a sophisticated deep learning architecture tailored for food image classification. Leveraging convolutional neural networks (CNNs) and other state-of-the-art techniques, our system aims to surpass traditional methods, offering improved accuracy and efficiency. We detail the key components of the proposed system, emphasizing its adaptability to diverse datasets and potential applications in real-world scenarios. Through a comprehensive evaluation, we aim to demonstrate the efficacy and reliability of our approach in automating food image classification tasks.

5. MINIMUM SYSTEM REQUIREMENTS Software Requirements: Application platform : Python 2.0 & above Operating System : Windows /Linux Hardware Requirements: RAM : 2GB & Above Processor : Intel I3 & Above

6. ANALYSIS AND DESIGN

7. Analysis System Modules 1. Data Preprocessing Module: The Data Preprocessing Module plays a crucial role in preparing the input data for the deep learning model. It involves collecting a diverse and well-labeled dataset of food images, followed by tasks like resizing images to a consistent size, normalizing pixel values to ensure uniformity, and augmenting the data to enhance model robustness. Data preprocessing ensures that the neural network can effectively learn and generalize from the images it encounters during training. 2. Convolutional Neural Network (CNN) Model Module: The heart of the system, the CNN Model Module, is responsible for the actual classification task. It comprises a deep learning architecture, typically a Convolutional Neural Network (CNN). The CNN architecture is designed to automatically extract relevant features from food images through layers such as convolutional, pooling, and fully connected layers. These layers are fine-tuned to capture intricate details, textures, and patterns within the images, enabling accurate food classification. 3. Training and Evaluation Module: The Training and Evaluation Module is crucial for both model development and assessment. It involves sub-modules for training, validation, and testing. During training, the model learns to recognize patterns and associations in the preprocessed food images. Validation is used to fine-tune hyperparameters and monitor the model's performance, while testing evaluates its accuracy and generalization to unseen data. Metrics like accuracy, precision, recall, and F1-score are used to assess the model's effectiveness in accurately categorizing food images. This module helps ensure the model's reliability and suitability for practical food image classification applications.

8. User Modules 1. Image Upload and Classification Module: This module allows users to easily upload food images, and then employs a pre-trained deep learning model for image classification. Users receive instant feedback on the type of food in the image, facilitating quick and accurate categorization of various dishes, whether it's pizza, sushi, or salads. 2. Recipe Recommendation Module: After classifying food images, this module suggests relevant recipes based on the detected food category. Users can explore a diverse range of recipes tailored to their preferences, dietary restrictions, and ingredient availability. It provides a personalized culinary experience, helping users discover and create delicious meals from the food items they have. 3. Nutrition Analysis and Meal Planning Module: Users can set dietary goals and preferences, and this module analyzes classified food images to provide comprehensive nutritional insights. It assists users in planning balanced meals by recommending appropriate portion sizes and food combinations. It tracks daily intake and offers recommendations for achieving specific nutritional goals, promoting healthier eating habits and more informed meal planning.

9. LEVEL 0 DESIGN Data Flow Diagrams

10. LEVEL 1

11. UML DIAGRAMS CLASS DIAGRAM

12. USE CASE DIAGRAM uploads image DATA BASE preprocess image Feature extraction Classify the image usimg deep learning Display the result SYSTEM USER

13. SEQUENCE DIAGRAM

14. DEPLOYMENT DIAGRAM

15. ACTIVITY DIAGRAM

16. IMPLEMENTATION

17. Sample Code Source Code: Step 1 : load the libraries using the import libraries from flask import Flask,render_template , redirect, request import numpy as np import PIL from keras.models import load_model import cv2 Step 2 : Load and transform data. gro = load_model ( r'model files/Food_model.h5’) Food = load_model ( r'model files/Indian_food_20.h5’) fre = load_model ( r'model files/Food_freshness.h5') Step 3 : Plot and inspect the classes in the data classes = ['apple', 'banana', 'beetroot', 'bell pepper', 'cabbage', 'capsicum', 'carrot', 'cauliflower', 'chilli pepper', 'corn', 'cucumber', 'eggplant', 'garlic', 'ginger', 'grapes', ' jalepeno ', 'kiwi', 'lemon', 'lettuce', 'mango', 'onion', 'orange', 'paprika', 'pear', 'peas', 'pineapple', 'pomegranate', 'potato', ' raddish ', 'soy beans', 'spinach', ' sweetcorn ', ' sweetpotato ', 'tomato', 'turnip', 'watermelon’] menue = [ 'burger', ' butter_naan ', 'chai', 'chapati', ' chole_bhature ', ' dal_makhani ', 'dhokla', ' fried_rice ', ' idli ', ' jalebi ', ' kaathi_rolls ', ' kadai_paneer ', 'kulfi', ' masala_dosa ', ' momos ', ' paani_puri ', ' pakode ', ' pav_bhaji ', 'pizza', 'samosa ’] fresh = [' rotten','fresh ']

18. Step 4:Split and store the data into train and test data. def est ( test_image,model,labels ): img = cv2.imread( test_image ) img = img / 255.0 img = cv2.resize( img ,(224,224)) img = img.reshape (1,224,224,3) prediction = model.predict ( img ) pred_class = np.argmax (prediction, axis = -1) return labels[ pred_class [0]] def est2( test_image,model,labels ): img = cv2.imread( test_image ) img = img / 255.0 img = cv2.resize( img ,(228,228)) img = img.reshape (1,228,228,3) prediction = model.predict ( img ) pred_class = np.argmax (prediction, axis = -1) return labels[ pred_class [0]]

19. def food_classification (): if request.method == 'POST': # Check if a file was uploaded If 'file' not in request.files : return render_template ('food_classification.html', message='No file part') file = request.files ['file'] # Check if the file is empty if file.filename == '': return render_template ('food_classification.html', message='No selected file') # Check if the file is an image if file: # Save the file to a temporary location file_path = 'static/temp.jpg' file.save ( file_path ) # Predict food predicted_food = est2( file_path , gro , classes) return render_template ('food_classification.html', filename= file_path , predicted_food = predicted_food ) return render_template ('food_classification.html')

20. Input: The input image for food image classification depicts various food items arranged or captured in a frame.

21. Testing and validation-test cases 1. Dataset Diversity Test - Objective: Ensure the model's effectiveness across a broad spectrum of food categories, reflecting its ability to handle the vast diversity of global cuisines and dish presentations. - Importance: This test is essential because a model trained and validated on a diverse dataset is more likely to generalize well in real-world scenarios, accurately recognizing and classifying a wide range of food items from different cultures and cuisines. 2. Image Quality and Variation Test - Objective: Assess the model's robustness in classifying food images under varied conditions, including differences in resolution, lighting, and angle. - Importance: Food images in practical applications come from various sources, often captured under less-than-ideal conditions. Testing the model's performance with such variations ensures its usability across common use cases, such as in mobile apps where users upload photos taken in different environments.

22. Testing and validation-test cases 3. Cross-validation Test - Objective: Validate the model's generalizability and its capability to perform consistently across different data segments, thereby preventing overfitting. - Importance: Cross-validation helps in assessing how well the model can be expected to perform on unseen data, which is crucial for ensuring the reliability and trustworthiness of the classification results in diverse and real-world settings.

23. Conclusion In conclusion, food image classification through deep learning presents a transformative approach for various applications, from enhancing culinary experiences to nutritional tracking and inventory management. Leveraging the power of convolutional neural networks (CNNs) and other deep learning models, this approach has demonstrated remarkable accuracy in identifying and categorizing a wide range of food items from images. The integration of these technologies simplifies complex classification tasks, offering scalable solutions that adapt over time with continuous learning. However, challenges such as diverse cuisine representation and varying image qualities remain. Future advancements in algorithm efficiency, data diversity, and model interpretability are vital for further improving classification performance and broadening the scope of application, making automated food image classification an exciting and rapidly evolving field within artificial intelligence.

24. Future Enhancement The future enhancement of food image classification using deep learning approaches holds immense potential. Integrating advanced neural network architectures, such as transformer models and capsule networks, could significantly improve accuracy and processing speed, enabling real-time applications. Exploiting unsupervised and semi-supervised learning methods may enhance the model's ability to learn from unlabelled data, vastly increasing the diversity and volume of training datasets without extensive manual annotation. Incorporating multimodal data, such as textual recipes and nutritional information alongside images, could provide a more holistic understanding of food items, leading to richer and more context-aware classifications. Additionally, developing lightweight models for edge computing would make the technology accessible on mobile devices, allowing for broader application in health, dietary management, and culinary fields.

25. REFERENCES Kaur, R., Kumar, R., & Gupta, M. (2023). Deep neural network for food image classification and nutrient identification: A systematic review. Reviews in Endocrine and Metabolic Disorders, 1-21. Hu, G., Ahmed, M., & L'Abbé, M. R. (2023). Natural language processing and machine learning approaches for food categorization and nutrition quality prediction compared with traditional methods. The American Journal of Clinical Nutrition, 117(3), 553-563. Kumar, S., Arif, T., Alotaibi, A. S., Malik, M. B., & Manhas, J. (2023). Advances Towards Automatic Detection and Classification of Parasites Microscopic Images Using Deep Convolutional Neural Network: Methods, Models and Research Directions. Archives of Computational Methods in Engineering, 30(3), 2013-2039. Tvoroshenko, I., Gorokhovatskyi, V., Kobylin, O., & Tvoroshenko, A. (2023). Application of deep learning methods for recognizing and classifying culinary dishes in images. Konstantakopoulos, F. S., Georga, E. I., & Fotiadis, D. I. (2023). An Automated Image-Based Dietary Assessment System for Mediterranean Foods. IEEE Open Journal of Engineering in Medicine and Biology.

26. Butuner , R., Cinar , I., Taspinar , Y. S., Kursun , R., Calp , M. H., & Koklu , M. (2023). Classification of deep image features of lentil varieties with machine learning techniques. European Food Research and Technology, 249(5), 1303-1316. Simon, P., & Uma, V. (2023, March). Integrating inceptionresnetv2 model and machine learning classifiers for food texture classification. In International Conference on Communications and Cyber Physical Engineering 2018 (pp. 531-539). Singapore: Springer Nature Singapore. Gulzar , Y. (2023). Fruit image classification model based on MobileNetV2 with deep transfer learning technique. Sustainability, 15(3), 1906. Joshua, S. R., Shin, S., Lee, J. H., & Kim, S. K. (2023). Health to Eat: A Smart Plate with Food Recognition, Classification, and Weight Measurement for Type-2 Diabetic Mellitus Patients’ Nutrition Control. Sensors, 23(3), 1656. Abhishek , S., Anjali, T., & Raj, S. (2023, August). Harnessing Deep Learning for Precise Rice Image Classification: Implications for Sustainable Agriculture. In 2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 557-564). IEEE.

27. Thank You