TEAM MEMBERS:

RAMJI T B - Team Leader

ARAVIND R P - Team Member 1

BHUVANESH BABU K R - Team Member 2

KISHORE LAL A R - Team Member 3

GUHAN M - Team Member 4

According to the next 25 years, the less developed countries’ waste accumulation will increase drastically. With the increase in the number of industries in the urban area,the disposal of the solid waste is really becoming a big problem, and the solid waste includes paper, wood, plastic, metal, glass etc. The common way of managing waste is burning waste and this method can cause air pollution and some hazardous materials from the waste spread into the air which can cause cancer. Hence it is necessary to recycle the waste to protect the environment and human beings’ health, and we need to separate the waste into different components which can be recycled using different ways. The present way of separating waste/garbage is the hand-picking method, whereby someone is employed to separate out the different objects/materials. The person who separates waste, is prone to diseases due to the harmful substances in the garbage. With this in mind, it motivated us to develop an automated system which is able to sort the waste. and this system can take a short time to sort the waste, and it will be more accurate in sorting than the manual way. With the system in place, the beneficial separated waste can still be recycled and converted to energy and fuel for the growth of the economy. The system that is developed for the separation of the accumulated waste is based on the combination of Convolutional Neural Network

I have included the main file as Garbage classification which includes the files such as flask and Js

For more info about project visit this link:

https://github.com/naanmudhalvan-SI/PBL-NT-GP--20355-1682669998

• Know fundamental concepts and techniques of Convolutional Neural Network.

• Gain a broad understanding of image data.

• Know how to pre-process/clean the data using different data preprocessing techniques.

• Know how to build a web application using the Flask framework.

Install the following python packages

• pip install numpy
• pip install pandas
• pip install scikit-learn
• pip install tensorflow==2.3.2
• pip install keras==2.3.1
• pip install Flask

You must have prior knowledge of following topics to complete this project.

Deep Learning Concept

CNN

Flask: Flask is a popular Python web framework, meaning it is a third-party Python library used for developing web applications.hon web framework, meaning it is a third-party Python library used for developing web applications.*

The user interacts with the UI (User Interface) to choose the image.

The chosen image analyzed by the model which is integrated with flask application.

CNN Models analyze the image, then prediction is showcased on the Flask UI.

To accomplish this, we have to complete all the activities and tasks listed below

Data Collection.

Create Train and Test Folders.

Data Preprocessing.

Import the ImageDataGenerator library

Configure ImageDataGenerator class

ApplyImageDataGenerator functionality to Trainset and Testset

Model Building

Import the model building Libraries

Initializing the model

Adding Input Layer

Adding Hidden Layer

Adding Output Layer

Configure the Learning Process

Training and testing the model

Save the Model

Application Building

Create an HTML file

Build Python Code

• Pre Requistes
  

• Prior Knowledge
  

• Project Objectives
  

• Project Flow
  

• Project Strucure
  

  • Data Collection
    
    • Download the Dataset
      
      • https://www.kaggle.com/datasets/asdasdasasdas/garbage-classification
        
  • Image Preprocessing
    
    • Import Libraries
      
    • Configure Image Data Generator Class
      
    • Apply Image Data Generator Functionality To Train Set And Test Set
      
  • Model Building
    
    • Importing The Model Building Libraries
      
    • Initialize the model
      
    • Adding CNN Layers
      
    • Adding Dense Layers
      
    • Configure the Learning process
      
    • Training the Model
      
    • Testing the Model
      

• save the model
  

• Ideation Phase
  

  • Define the Problem statement
    
  • Empathize and Discover
    
  • Brainstorm & Prioritize Ideas
    

• Project Design Phase -1
  

  • Proposed Solution
    
  • Problem Solution Fit
    
  • Solution Architecture
    

• Project Design Phase -2
  

  • Data Flow Diagrams
    
  • Solution Requirements
    
  • Technology Architecture
    

• Project Development Phase
  

  • No.of functional features included in the section
    
  • Code-Layout Readability and Reusability
    
  • Utilization of Algorithms,Dynamic Programming, Optimal Memory Utilization
    
  • Debugging and Traceability
    
  • Exception Handling
    

• Performance and Final Submission Phase
  

  • Model Performance Metrics
    
  • Project Documentation
    
  • Project Demonstration
    

• Final Deliverables
  

  • Dataset
    
    • Dataset link
      
  • Demonstration Video
    
    • Demo.pdf
      
    • Demo.mp4
      
  • Final Code
    
  • Project Report
  • Output
    
    • output.pdf
      

• ASSIGNMENTS
  

  • Assignment 1
    
  • Assignment 2
    
  • Assignment 3
    

• QUIZZES
  

  • Quiz 1
    
  • Quiz 2
    
  • Quiz 3