| Title: | The accuracy of the germination rate of seeds based on image processing and artificial neural networks |
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| Authors: | ID Škrubej, Uroš (Author)
ID Rozman, Črtomir (Author)
ID Stajnko, Denis (Author) |
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| Files: |  Agricultura_2015_Skrubej,_Rozman,_Stajnko_The_accuracy_of_the_germination_rate_of_seeds_based_on_image_processing_and_artificial_neural.pdf (353,43 KB)
MD5: 58852E46FCAEACDCD25118BE522DAA41
PID: 20.500.12556/dkum/f3a4fb9f-929f-46f0-a765-f52c91759b2d
 https://www.degruyter.com/view/j/agricultura.2015.12.issue-1-2/agricultura-2016-0003/agricultura-2016-0003.xml
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FKBV - Faculty of Agriculture and Life Sciences
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| Abstract: | 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.). |
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| Keywords: | image processing, artificial neural networks, seeds, tomato |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Year of publishing: | 2015 |
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| Number of pages: | str. 19-24 |
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| Numbering: | Letn. 12, št. 1/2 |
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| PID: | 20.500.12556/DKUM-68970  |
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| ISSN: | 1580-8432 |
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| UDC: | 004.9:631.547.1 |
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| ISSN on article: | 1580-8432 |
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| COBISS.SI-ID: | 4129068 |
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| DOI: | 10.1515/agricultura-2016-0003 |
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| NUK URN: | URN:SI:UM:DK:4ZTRS3HE |
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| Publication date in DKUM: | 14.11.2017 |
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| Views: | 1588 |
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| Downloads: | 497 |
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| Metadata: |    |
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| Categories: | Misc.
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