Fish fry counter based on digital image processing method
Large quantities of ornamental fish fry can be time-consuming and error-prone to count manually. The tedious counting of ornamental fish fry can also be stressful and result in the death of the fish fry, which can result in lost sales for ornamental fish businesses. In order to solve these issues fo...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/09/e3sconf_issat2024_07027.pdf |
| _version_ | 1797343516666363904 |
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| author | Andini Dianthika Puteri Riadi Indra Chandra Joseph Al Ariiq Fathan |
| author_facet | Andini Dianthika Puteri Riadi Indra Chandra Joseph Al Ariiq Fathan |
| author_sort | Andini Dianthika Puteri |
| collection | DOAJ |
| description | Large quantities of ornamental fish fry can be time-consuming and error-prone to count manually. The tedious counting of ornamental fish fry can also be stressful and result in the death of the fish fry, which can result in lost sales for ornamental fish businesses. In order to solve these issues for the ornamental fish businesses, the goal of this research is to develop a system for automatically counting the number of fish fry using the thresholding and morphology methods based on digital image processing. The fish fry counter has been tested with four distinct types of fish fry, is capable of counting up to 130 fish fry in 1-3 seconds for a single operation. The final result generated by this tool are an image with a description of the total number of fish fry encountered, the date and time of data collection, and the number of fish fry detected. This information are stored in a database with .xlsx extension. The experiments result appears that this tool can count the number of fish fry corresponding to different colored fish species. However, when calculating the total amount of fish fry that can fit into the container to its full capacity, the tool has an accuracy of 95.86% and an average error of 4.14% that is caused by the side of the container which contains fish fry that are not visible to the detection camera (blind spot). |
| first_indexed | 2024-03-08T10:48:50Z |
| format | Article |
| id | doaj.art-ecfdc39bb29d48cd8284e8301573368b |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-08T10:48:50Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-ecfdc39bb29d48cd8284e8301573368b2024-01-26T16:52:43ZengEDP SciencesE3S Web of Conferences2267-12422024-01-014790702710.1051/e3sconf/202447907027e3sconf_issat2024_07027Fish fry counter based on digital image processing methodAndini Dianthika Puteri0Riadi Indra Chandra Joseph1Al Ariiq Fathan2Electronics Engineering, Electrical Engineering DepartmentElectronics Engineering, Electrical Engineering DepartmentElectronics Engineering, Electrical Engineering DepartmentLarge quantities of ornamental fish fry can be time-consuming and error-prone to count manually. The tedious counting of ornamental fish fry can also be stressful and result in the death of the fish fry, which can result in lost sales for ornamental fish businesses. In order to solve these issues for the ornamental fish businesses, the goal of this research is to develop a system for automatically counting the number of fish fry using the thresholding and morphology methods based on digital image processing. The fish fry counter has been tested with four distinct types of fish fry, is capable of counting up to 130 fish fry in 1-3 seconds for a single operation. The final result generated by this tool are an image with a description of the total number of fish fry encountered, the date and time of data collection, and the number of fish fry detected. This information are stored in a database with .xlsx extension. The experiments result appears that this tool can count the number of fish fry corresponding to different colored fish species. However, when calculating the total amount of fish fry that can fit into the container to its full capacity, the tool has an accuracy of 95.86% and an average error of 4.14% that is caused by the side of the container which contains fish fry that are not visible to the detection camera (blind spot).https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/09/e3sconf_issat2024_07027.pdf |
| spellingShingle | Andini Dianthika Puteri Riadi Indra Chandra Joseph Al Ariiq Fathan Fish fry counter based on digital image processing method E3S Web of Conferences |
| title | Fish fry counter based on digital image processing method |
| title_full | Fish fry counter based on digital image processing method |
| title_fullStr | Fish fry counter based on digital image processing method |
| title_full_unstemmed | Fish fry counter based on digital image processing method |
| title_short | Fish fry counter based on digital image processing method |
| title_sort | fish fry counter based on digital image processing method |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/09/e3sconf_issat2024_07027.pdf |
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