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Shape-based sorting of agricultural produce using support vector machines in a MATLAB/SIMULINK environment

The ability to sort agricultural produce automatically is very important. This paper addresses one way to identify agricultural produce based on their shape. The techniques used are based on support vector machines. The images of the produce are loaded into MATLAB and the features extracted using im...

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Main Authors: Kyaw M.M., Ahmed S.K., Md Sharrif Z.A.
Other Authors: 35092761100
Format: Conference paper
Published: 2023
Subjects:
Agricultural produce
Feature extraction
Support vector machine
Agricultural machinery
Cameras
Edge detection
Gears
Image processing
Image retrieval
MATLAB
Multilayer neural networks
Signal processing
Support vector machines
Vectors
Image processing technique
MATLAB /simulink
MATLAB/Simulink environment
Shape based
Techniques used
Agricultural products
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id my.uniten.dspace-30767
record_format dspace
spelling my.uniten.dspace-307672023-12-29T15:52:53Z Shape-based sorting of agricultural produce using support vector machines in a MATLAB/SIMULINK environment Kyaw M.M. Ahmed S.K. Md Sharrif Z.A. 35092761100 25926812900 6507195893 Agricultural produce Feature extraction Support vector machine Agricultural machinery Cameras Edge detection Feature extraction Gears Image processing Image retrieval MATLAB Multilayer neural networks Signal processing Support vector machines Vectors Agricultural produce Image processing technique MATLAB /simulink MATLAB/Simulink environment Shape based Techniques used Agricultural products The ability to sort agricultural produce automatically is very important. This paper addresses one way to identify agricultural produce based on their shape. The techniques used are based on support vector machines. The images of the produce are loaded into MATLAB and the features extracted using image processing techniques based on edge detection. These features are then input to a classifier; i.e., a support vector machine, for identification. A regular digital camera is used for acquiring the image, and all manipulations are performed in a MATLAB / SIMULINK environment. The results obtained are an improvement over a previous technique. �2009 IEEE. Final 2023-12-29T07:52:53Z 2023-12-29T07:52:53Z 2009 Conference paper 10.1109/CSPA.2009.5069203 2-s2.0-70349925477 https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349925477&doi=10.1109%2fCSPA.2009.5069203&partnerID=40&md5=0658851a7e39f8ab6a659b0123e96afe https://irepository.uniten.edu.my/handle/123456789/30767 5069203 135 139 Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Agricultural produce
Feature extraction
Support vector machine
Agricultural machinery
Cameras
Edge detection
Feature extraction
Gears
Image processing
Image retrieval
MATLAB
Multilayer neural networks
Signal processing
Support vector machines
Vectors
Agricultural produce
Image processing technique
MATLAB /simulink
MATLAB/Simulink environment
Shape based
Techniques used
Agricultural products
spellingShingle Agricultural produce
Feature extraction
Support vector machine
Agricultural machinery
Cameras
Edge detection
Feature extraction
Gears
Image processing
Image retrieval
MATLAB
Multilayer neural networks
Signal processing
Support vector machines
Vectors
Agricultural produce
Image processing technique
MATLAB /simulink
MATLAB/Simulink environment
Shape based
Techniques used
Agricultural products
Kyaw M.M.
Ahmed S.K.
Md Sharrif Z.A.
Shape-based sorting of agricultural produce using support vector machines in a MATLAB/SIMULINK environment
description The ability to sort agricultural produce automatically is very important. This paper addresses one way to identify agricultural produce based on their shape. The techniques used are based on support vector machines. The images of the produce are loaded into MATLAB and the features extracted using image processing techniques based on edge detection. These features are then input to a classifier; i.e., a support vector machine, for identification. A regular digital camera is used for acquiring the image, and all manipulations are performed in a MATLAB / SIMULINK environment. The results obtained are an improvement over a previous technique. �2009 IEEE.
author2 35092761100
author_facet 35092761100
Kyaw M.M.
Ahmed S.K.
Md Sharrif Z.A.
format Conference paper
author Kyaw M.M.
Ahmed S.K.
Md Sharrif Z.A.
author_sort Kyaw M.M.
title Shape-based sorting of agricultural produce using support vector machines in a MATLAB/SIMULINK environment
title_short Shape-based sorting of agricultural produce using support vector machines in a MATLAB/SIMULINK environment
title_full Shape-based sorting of agricultural produce using support vector machines in a MATLAB/SIMULINK environment
title_fullStr Shape-based sorting of agricultural produce using support vector machines in a MATLAB/SIMULINK environment
title_full_unstemmed Shape-based sorting of agricultural produce using support vector machines in a MATLAB/SIMULINK environment
title_sort shape-based sorting of agricultural produce using support vector machines in a matlab/simulink environment
publishDate 2023
_version_ 1806426489972850688
score 13.214268

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