Project Outline/Description
Nowadays, standard intake of healthy food is necessary for keeping a balanced diet to avoid obesity in the human body.
Fruit Recognition And Calorie Estimation Using Cnn is a task that can be accomplished with the help of deep learning. This task involves the use of Convolutional Neural Networks (CNNs) to detect and classify different types of fruits and estimate their calories.
The CNNs can be trained on images of different fruits to detect and classify them, and then the estimated calories for each are calculated based on their nutritional content. Additionally, the calories can be adjusted according to the size of the fruit. This task can be useful in applications such as food tracking and calorie monitoring.
Process
- Pre-processing: Pre-processing is the first step in any deep learning project. This step involves cleaning, normalizing, resizing and augmenting the data.
- Building a Neural Network Model: After pre-processing the data, a suitable neural network model needs to be built. This can be done using the Keras or TensorFlow libraries.
- Training the Model: The model needs to be trained on the data so that it can learn to recognize different types of fruits and estimate their calories accurately.
- Testing the Model: Once the model has been trained, it needs to be tested to ensure that it is able to detect and classify different types of fruits and estimate their calories accurately.
- Deployment: After testing the model, it can be deployed as an application or web service and integrated into an existing application or website.
Application :
- Food tracking and calorie monitoring.
It can be used to help people monitor their calorie intake and make healthier food choices. - It can be used in food production and distribution to monitor and track the nutritional content of fruits and other food items.
Requirements
Hardware requirement: –
- Desktop or laptop computer – GPU (Graphics Processing Unit)
Software requirement: –
- Python programming language
- Keras neural networks library
- TensorFlow machine learning library
- OpenCV image processing library
What you will learn?
- Will have a comprehensive understanding of the datasets, methods and evaluation metrics.
- Identify the challenges and limitations of existing datasets and methods, and analyze the state-of-the-art performance of automated diabetic retinopathy detection systems.
- How to Build an neural network model by training the dataset.
- Design a user interface for providing user input and display the result.
- Plot Confusion matrix for different classes.
- Displays the performance parameters Accuracy, Precision, sensitivity and specificity respectively.
