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Classification of cultivated rice Seed MR219 and MR269 varieties based on colour features using machine vision technique

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Bibliographic Details
Main Author: Ahmad Yusuf, Hashim
Other Authors: School of Bioprocess Engineering
Format: Other
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2022
Subjects:
Machine vision
Rice seed
MR219 variety
MR269 variety
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/74332
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spelling my.unimap-743322022-02-17T04:03:04Z Classification of cultivated rice Seed MR219 and MR269 varieties based on colour features using machine vision technique Ahmad Yusuf, Hashim School of Bioprocess Engineering Machine vision Rice seed MR219 variety MR269 variety Access is limited to UniMAP community. Final Year Project (FYP) was carried out to extract the colour feature of cultivated rice seed MR219 and MR269 using image analysis. Mostly in rice seed factory, seed inspection need experienced worker to distinguish the quality of the rice seed and the method done was very time consuming. Hence, Machine vision system that consist of couple-charged device (CCD) camera was used in seed testing. The study starts by collecting quality rice seed from the nearby legitimate distributer. This study was focusing on the rice seed type of MR219 and MR269 because these rice seed was often used for rice cultivation throughout Malaysia. Next, the images of MR219 and MR269 was captured using CCD camera. In addition, the study was carried out to create a programming using LabVIEW 2013 to provide convenience to any users who related in the research field of colour feature extraction. Through experiments carried out, the colour diversity of rice seeds can be determined and classified depending on the parameters set. The colour features parameter that are extracted are red, green, blue, hue, saturation, value, and intensity. In the analysis of data, MATLAB Neural Network software was use for data research and classification. The neural network pattern recognition tool was used to create and train network, and evaluate the performance using mean square error and confusion matrices. Studies conducted for the retrain 40 hidden layer show the MSE value of 1.885E-01 and the accuracy is 76.7%. 2022-02-17T04:03:04Z 2022-02-17T04:03:04Z 2016-06 Other http://dspace.unimap.edu.my:80/xmlui/handle/123456789/74332 en Universiti Malaysia Perlis (UniMAP)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Machine vision
Rice seed
MR219 variety
MR269 variety
spellingShingle Machine vision
Rice seed
MR219 variety
MR269 variety
Ahmad Yusuf, Hashim
Classification of cultivated rice Seed MR219 and MR269 varieties based on colour features using machine vision technique
description Access is limited to UniMAP community.
author2 School of Bioprocess Engineering
author_facet School of Bioprocess Engineering
Ahmad Yusuf, Hashim
format Other
author Ahmad Yusuf, Hashim
author_sort Ahmad Yusuf, Hashim
title Classification of cultivated rice Seed MR219 and MR269 varieties based on colour features using machine vision technique
title_short Classification of cultivated rice Seed MR219 and MR269 varieties based on colour features using machine vision technique
title_full Classification of cultivated rice Seed MR219 and MR269 varieties based on colour features using machine vision technique
title_fullStr Classification of cultivated rice Seed MR219 and MR269 varieties based on colour features using machine vision technique
title_full_unstemmed Classification of cultivated rice Seed MR219 and MR269 varieties based on colour features using machine vision technique
title_sort classification of cultivated rice seed mr219 and mr269 varieties based on colour features using machine vision technique
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2022
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/74332
_version_ 1729704626654019584
score 13.222552

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