Automated Rice Classification and Grading using Deep Learning

Pakistan is one of the top producers of rice in the world and is well recognized for producing and exporting high quality rice. However, we are still using manual practices for classification and grading. Manual grading and classification leads to several problems including mixing of different varie

Project Title

Automated Rice Classification and Grading using Deep Learning

Project Area of Specialization

Computer Science

Project Summary

Pakistan is one of the top producers of rice in the world and is well recognized for producing and exporting high quality rice. However, we are still using manual practices for classification and grading. Manual grading and classification leads to several problems including mixing of different varieties of rice and different qualities of same rice variety. This causes problems in producing quality export product. To overcome this problem, we are proposing an automatic rice classification and grading system using machine learning algorithms. Classification of Pakistani basmati rice varieties based on rice grain features including size, shape, and color. The classified varieties are graded into quality grades (A, B, and C) by using SVM (Support Vector Machine) to differentiate between good, average, and less than the average rice grain on the basis of parameters including head rice, broken, and half rice grains.

Project Objectives

This study, aims to disrupt the traditional and manual system of grading and classification of rice grains by automatic system. The proposed system is envisioned to satisfy the exports requirement that will increase the international demand of rice.

OBJECTIVES:

The proposed study will achieve following objectives:

• To automatically classify the rice grains variety.
• To classify the rice grains into quality grades (A, B, and C) on the basis of broken head, half, and less than the half rice grains.

Project Implementation Method

To collect image data, a digital camera will be mounted on stand at a fixed location with the distance between the lens and sample to be around 14cm. All images will be captured with black background and uniform light intensity to improve the data collection quality. We select fifteen different varieties of rice grains for experimental evaluation. All images will be stored in JPG format in separate folders named after that variety. The proposed methodology comprises of four main stages as given below

                                  

Algorithm 1

Input: Colored rice grains images

Output: predicted rice grains variety and grading.

Start

Step1: Data collection.

Step2: Preprocessing

2.1)  Scaling

2.2)  Image Enhancement.

2.3)  Perform image segmentation.

2.4)  Feature Extraction

Step3: Classification module.

Step4: Grading module.

Stop

Image Acquisition

Pre-processing

Classification of variety

Grading

Benefits of the Project

Rice is an important food crop and it is cultivated in several areas across Pakistan including in Punjab it is sown in Gujranwala, Sheikhupura, Wazirabad, Sialkot, Faisalabad, Sargodha, Kasure, and district Gujrat,. In Sindh, Thatta, Shikarpur, and Jacobabad, Dadu, Larkana, Badin districts are important in farming of rice crop. In Pakistan, different varieties of rice grains are mixed together causing rice adulteration that effects the national as well as an international trade and exports. There is strong need to overcome this problem by developing an automatic system for automatic grading and classification of rice grains in Pakistan. The benefits of this system include:

• Detection of rice adulteration
• Detection of quality of rice grain
• Grading of rice on the basis of quality (full rice, splitted, half)

Technical Details of Final Deliverable

To collect image data, a digital camera will be mounted on stand at a fixed location with the distance between the lens and sample to be around 14cm. All images will be captured with black background and uniform light intensity to improve the data collection quality. We select fifteen different varieties of rice grains for experimental evaluation. All images will be stored in JPG format in separate folders named after that variety. The proposed methodology comprises of four main stages as given below

                                  

Algorithm 1

Input: Colored rice grains images

Output: predicted rice grains variety and grading.

Start

Step1: Data collection.

Step2: Preprocessing

2.1)  Scaling

2.2)  Image Enhancement.

2.3)  Perform image segmentation.

2.4)  Feature Extraction

Step3: Classification module.

Step4: Grading module.

Stop

Image Acquisition

Pre-processing

Classification of variety

Grading

Final Deliverable of the Project

Software System

Core Industry

Agriculture

Other Industries

IT , Food

Core Technology

Artificial Intelligence(AI)

Other Technologies

Others

Sustainable Development Goals

Decent Work and Economic Growth, Industry, Innovation and Infrastructure, Life on Land

Required Resources

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