SRS Fresh and rotten Fruit classification using Deep Learning Techniques

We will use deep learning algorithms to predict either fruit is fresh or rotten.
The first step will be to label the images, for this purpose we can use different available tools like LabelImg. Then we will setup environment apply data augmentation,train our model and check the results

Scope of Project Fresh and rotten Fruit classification using Deep Learning Techniques:

Project scope is the part of project planning that involves determining and documenting a list of specific project  goals, deliverables, tasks, costs and deadlines. Defining the project scope involves adopting a clear vision and an agreement on the outcomes of the project. This allows each milestone of the project to stay on target

Functional and Non Functional Requirements of Fresh and rotten Fruit classification using Deep Learning Techniques:

Functional Requirements :

A functional requirement shows that what the system must do what services the system present to users. It describes a software system or its component. A function is nothing but inputs to the software system, its behavior, and outputs

Functional Requirements of Fresh and rotten Fruit classification using Deep Learning Techniques:

1. There are six classes given in the data set, you need to annotate all the images manually.
2. We can use data augmentation to artificially increase number of images.
3. Next step is to annotate the images according to the different classes provided in the data set in required format using LabelImg.
4. Once the data set is annotated ,environment must be set.
5. Next step is to split the data in test and train.
6. Then the model can be trained.
7. You can set environment on your own machine or use google colab.
8. Model must be retrained if desired accuracy is not achieved by enhancing data set or
changing training parameters.

NON-FUNCTIONAL REQUIREMENTS Fresh and rotten Fruit classification using Deep Learning Techniques:

• Application is user friendly.
• Application Perform fast manipulation and calculations.
• Application is adaptable.
• Application will be able to work on all types of operating systems.
• Application will be capable to handle multi user activities simultaneously.
• There will be back up system to face any problem in system
• All the options should be learning friendly I.e. member could easily understand what that option will do if he clicked on it.
• Response Time is very awesome.

Some others are:

• Accessibility
• Maintainability
•  Fault Tolerance.
• Security
• Robustnes

Use Case Diagram of Diagnosis of Fresh and rotten Fruit classification using Deep Learning Techniques

a use case diagram can summarize the details of your system’s users   and their interactions with the system. Scenarios in which your system or application interacts with organizations, people, or external systems. Goals that your system or application helps those entities achieve

Usage Scenarios Diagnosis of Fresh and rotten Fruit classification using Deep Learning Techniques:

A brief user story explaining who is using the system and what they are trying to accomplish. A Scenario is made up of a number of simple, discrete steps that are designated as being performed by either the System or a  User.

ADOPTED METHODOLOGY for Fresh and rotten Fruit classification using Deep Learning Techniques:

The adopted methodology for this project is vu process model. Vu process model is a combination of water-fall model and spiral model. This combination has many advantages. This model has high risk analysis so avoidance of risk would be achieved. This model is easy to understand and use. Now first we will discuss the Water-fall model.

Work Plan of Diagnosis of Fresh and rotten Fruit classification using Deep Learning Techniques:

Work plan

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