Computer vision and artificial neural network techniques for classification of damage in potatoes during the storage process
The research methodology consists of several stages to develop a noninvasive method of identifying the turgor of potato tubers during the storage. During the first stage, a graphic database (set of training data) has been created for selected varieties of potatoes. As a next step, special proprietar...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Czech Academy of Agricultural Sciences
2019-04-01
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| Series: | Czech Journal of Food Sciences |
| Subjects: | |
| Online Access: | https://cjfs.agriculturejournals.cz/artkey/cjf-201902-0008_computer-vision-and-artificial-neural-network-techniques-for-classification-of-damage-in-potatoes-during-the-st.php |
| _version_ | 1797899594431987712 |
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| author | Krzysztof Przybył Piotr Boniecki Krzysztof Koszela Łukasz Gierz Mateusz Łukomski |
| author_facet | Krzysztof Przybył Piotr Boniecki Krzysztof Koszela Łukasz Gierz Mateusz Łukomski |
| author_sort | Krzysztof Przybył |
| collection | DOAJ |
| description | The research methodology consists of several stages to develop a noninvasive method of identifying the turgor of potato tubers during the storage. During the first stage, a graphic database (set of training data) has been created for selected varieties of potatoes. As a next step, special proprietary software called 'PID system' was used together with a commercial MATLAB package to extract parameters defining the digital image descriptors. This included: hue space models, shape coefficient and image texture. Thirdly, Artificial Neural Network (ANN) training was conducted with the use of Statistica and MATLAB tools. As a result of the analysis, a neural model has been obtained, which had the greatest classification features. |
| first_indexed | 2024-04-10T08:32:29Z |
| format | Article |
| id | doaj.art-3015d526b7f0441b8e022f317d8a72c9 |
| institution | Directory Open Access Journal |
| issn | 1212-1800 1805-9317 |
| language | English |
| last_indexed | 2024-04-10T08:32:29Z |
| publishDate | 2019-04-01 |
| publisher | Czech Academy of Agricultural Sciences |
| record_format | Article |
| series | Czech Journal of Food Sciences |
| spelling | doaj.art-3015d526b7f0441b8e022f317d8a72c92023-02-23T03:28:36ZengCzech Academy of Agricultural SciencesCzech Journal of Food Sciences1212-18001805-93172019-04-0137213514010.17221/427/2017-CJFScjf-201902-0008Computer vision and artificial neural network techniques for classification of damage in potatoes during the storage processKrzysztof Przybył0Piotr Boniecki1Krzysztof Koszela2Łukasz Gierz3Mateusz Łukomski4Institute of Food Technology and Plant Origin, Poznan University of Life Sciences, Poznan, PolandInstitute of Biosystems Engineering, Poznan University of Life Sciences, Poznan, PolandInstitute of Biosystems Engineering, Poznan University of Life Sciences, Poznan, PolandFaculty of Machines and Transport, Poznan University of Technology, Poznan, PolandInstitute of Biosystems Engineering, Poznan University of Life Sciences, Poznan, PolandThe research methodology consists of several stages to develop a noninvasive method of identifying the turgor of potato tubers during the storage. During the first stage, a graphic database (set of training data) has been created for selected varieties of potatoes. As a next step, special proprietary software called 'PID system' was used together with a commercial MATLAB package to extract parameters defining the digital image descriptors. This included: hue space models, shape coefficient and image texture. Thirdly, Artificial Neural Network (ANN) training was conducted with the use of Statistica and MATLAB tools. As a result of the analysis, a neural model has been obtained, which had the greatest classification features.https://cjfs.agriculturejournals.cz/artkey/cjf-201902-0008_computer-vision-and-artificial-neural-network-techniques-for-classification-of-damage-in-potatoes-during-the-st.phpartificial neural networksharalick's texture analysisimage analysisstorage of potatoes |
| spellingShingle | Krzysztof Przybył Piotr Boniecki Krzysztof Koszela Łukasz Gierz Mateusz Łukomski Computer vision and artificial neural network techniques for classification of damage in potatoes during the storage process Czech Journal of Food Sciences artificial neural networks haralick's texture analysis image analysis storage of potatoes |
| title | Computer vision and artificial neural network techniques for classification of damage in potatoes during the storage process |
| title_full | Computer vision and artificial neural network techniques for classification of damage in potatoes during the storage process |
| title_fullStr | Computer vision and artificial neural network techniques for classification of damage in potatoes during the storage process |
| title_full_unstemmed | Computer vision and artificial neural network techniques for classification of damage in potatoes during the storage process |
| title_short | Computer vision and artificial neural network techniques for classification of damage in potatoes during the storage process |
| title_sort | computer vision and artificial neural network techniques for classification of damage in potatoes during the storage process |
| topic | artificial neural networks haralick's texture analysis image analysis storage of potatoes |
| url | https://cjfs.agriculturejournals.cz/artkey/cjf-201902-0008_computer-vision-and-artificial-neural-network-techniques-for-classification-of-damage-in-potatoes-during-the-st.php |
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