Machine learning base fruit grading system using Feature Fusion

The aim of this project is to designed a fruit grading system using the Machine learning and feature fusion techniques. A publically available fruit dataset is used to train the proposed CNN architecture. We created a secondary dataset by making it the binary class dataset of apples only. A

Project Title

Machine learning base fruit grading system using Feature Fusion

Project Area of Specialization

Artificial Intelligence

Project Summary

The aim of this project is to designed a fruit grading system using the Machine learning and feature
fusion techniques. A publically available fruit dataset is used to train the proposed CNN architecture.
We created a secondary dataset by making it the binary class dataset of apples only. A complete fruit
grading system is designed and developed, composed of software and hardware modules. Initially, an
apple is placed on a conveyer belt and image is acquired which is further pre-processed by applying
cropping and background removal. Afterwards, handcrafted and deep features are extracted and these
features are fused together and forwarded to classifier for binary classification of apple categories. On
the basis of classification result, the apple is moved to its relevant class/category. The grading
algorithm is designed using MatLab, whereas, the hardware modules include: a conveyer system, DC
and Servo Motors, Camera with uniform illumination and a processor to execute the algorithm. This
system can be used in the agriculture sectors to provide best quality fruits.

Project Objectives

1. To designed machine vision system that is capable of automating the visual inspection of fruits in real-time.
2. To attain more prominent Features information by using Features Fusion method.
3. To improve the accuracy of system.
4. To reduce time and maintain the quality of fruits.

Project Implementation Method

1^st Phase:

Algorithm designing (CNN)

2nd Phase:

Training and testing

3rd Phase:

Hardware implementation

4th Phase:

Final testing of software and hardware.

Benefits of the Project

1. Quality control applications
2. Agriculture industries
3. Liquor industries

This project  will be use for fuits grading qualitatively wherever, it is needed .

Technical Details of Final Deliverable

The final deliverable of the project is complete fruit grading system The grading algorithm is designed using MatLab, whereas, the hardware modules include: a conveyer system, DC and Servo Motors, Camera with uniform illumination and a processor to execute the algorithm. A publically available fruit dataset is used to train the proposed CNN architecture.
We created a secondary dataset by making it the binary class dataset of apples only. A complete fruit
grading system is designed and developed, composed of software and hardware modules. Initially, an
apple is placed on a conveyer belt and image is acquired which is further pre-processed by applying
cropping and background removal. Afterwards, handcrafted and deep features are extracted and these
features are fused together and forwarded to classifier for binary classification of apple categories. On
the basis of classification result, the apple is moved to its relevant class/category. 

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Agriculture

Other Industries

Food , Manufacturing , Health

Core Technology

Artificial Intelligence(AI)

Other Technologies

Others

Sustainable Development Goals

Good Health and Well-Being for People, Industry, Innovation and Infrastructure

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com