The accuracy of the germination rate of seeds based on image processing and artificial neural networks

The accuracy of the germination rate of seeds based on image processing and artificial neural networks

This paper describes a computer vision system based on image processing and machine learning techniques which was implemented for automatic assessment of the tomato seed germination rate. The entire system was built using open source applications Image J, Weka and their public Java classes and linke...

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Bibliographic Details
Main Authors: Škrubej Uroš, Rozman Črtomir, Stajnko Denis
Format: Article
Language:English
Published: Sciendo 2015-12-01
Series:Agricultura
Subjects:
image processing
artificial neural networks
seeds
tomato
Online Access:http://www.degruyter.com/view/j/agricultura.2015.12.issue-1-2/agricultura-2016-0003/agricultura-2016-0003.xml?format=INT
id doaj-b2e1b39c8ac94e6bb43e5d8c349cf3b6
record_format Article
spelling doaj-b2e1b39c8ac94e6bb43e5d8c349cf3b62020-11-24T21:12:53ZengSciendoAgricultura1581-54392015-12-01121-2192410.1515/agricultura-2016-0003agricultura-2016-0003The accuracy of the germination rate of seeds based on image processing and artificial neural networksŠkrubej Uroš0Rozman Črtomir1Stajnko Denis2Lokovica 8a, SI-3325 Šoštanj, SloveniaUniversity of Maribor, Faculty of Agriculture and Life Sciences, SI-2311 Hoče, SloveniaUniversity of Maribor, Faculty of Agriculture and Life Sciences, SI-2311 Hoče, SloveniaThis paper describes a computer vision system based on image processing and machine learning techniques which was implemented for automatic assessment of the tomato seed germination rate. The entire system was built using open source applications Image J, Weka and their public Java classes and linked by our specially developed code. After object detection, we applied artificial neural networks (ANN), which was able to correctly classify 95.44% of germinated seeds of tomato (Solanum lycopersicum L.).http://www.degruyter.com/view/j/agricultura.2015.12.issue-1-2/agricultura-2016-0003/agricultura-2016-0003.xml?format=INTimage processingartificial neural networksseedstomato
collection DOAJ
language English
format Article
sources DOAJ
author Škrubej Uroš
Rozman Črtomir
Stajnko Denis
spellingShingle Škrubej Uroš
Rozman Črtomir
Stajnko Denis
The accuracy of the germination rate of seeds based on image processing and artificial neural networks
Agricultura
image processing
artificial neural networks
seeds
tomato
author_facet Škrubej Uroš
Rozman Črtomir
Stajnko Denis
author_sort Škrubej Uroš
title The accuracy of the germination rate of seeds based on image processing and artificial neural networks
title_short The accuracy of the germination rate of seeds based on image processing and artificial neural networks
title_full The accuracy of the germination rate of seeds based on image processing and artificial neural networks
title_fullStr The accuracy of the germination rate of seeds based on image processing and artificial neural networks
title_full_unstemmed The accuracy of the germination rate of seeds based on image processing and artificial neural networks
title_sort accuracy of the germination rate of seeds based on image processing and artificial neural networks
publisher Sciendo
series Agricultura
issn 1581-5439
publishDate 2015-12-01
description This paper describes a computer vision system based on image processing and machine learning techniques which was implemented for automatic assessment of the tomato seed germination rate. The entire system was built using open source applications Image J, Weka and their public Java classes and linked by our specially developed code. After object detection, we applied artificial neural networks (ANN), which was able to correctly classify 95.44% of germinated seeds of tomato (Solanum lycopersicum L.).
topic image processing
artificial neural networks
seeds
tomato
url http://www.degruyter.com/view/j/agricultura.2015.12.issue-1-2/agricultura-2016-0003/agricultura-2016-0003.xml?format=INT
work_keys_str_mv AT skrubejuros theaccuracyofthegerminationrateofseedsbasedonimageprocessingandartificialneuralnetworks
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AT stajnkodenis theaccuracyofthegerminationrateofseedsbasedonimageprocessingandartificialneuralnetworks
AT skrubejuros accuracyofthegerminationrateofseedsbasedonimageprocessingandartificialneuralnetworks
AT rozmancrtomir accuracyofthegerminationrateofseedsbasedonimageprocessingandartificialneuralnetworks
AT stajnkodenis accuracyofthegerminationrateofseedsbasedonimageprocessingandartificialneuralnetworks
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