Watermelon ripeness detector using near infrared spectroscopy
This study aimed to design and develop a watermelon ripeness detector using Near-Infrared Spectroscopy (NIRS). The research problem being solved in this study is developing a prototype wherein the watermelon ripeness can be detected without the need to open it. This detector will save customers from...
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| Format: | Article |
| Language: | English |
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Diponegoro University
2020-10-01
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| Series: | Jurnal Teknologi dan Sistem Komputer |
| Subjects: | |
| Online Access: | https://jtsiskom.undip.ac.id/index.php/jtsiskom/article/view/13744 |
| _version_ | 1797283332186177536 |
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| author | Edwin R. Arboleda Kimberly M. Parazo Christle M. Pareja |
| author_facet | Edwin R. Arboleda Kimberly M. Parazo Christle M. Pareja |
| author_sort | Edwin R. Arboleda |
| collection | DOAJ |
| description | This study aimed to design and develop a watermelon ripeness detector using Near-Infrared Spectroscopy (NIRS). The research problem being solved in this study is developing a prototype wherein the watermelon ripeness can be detected without the need to open it. This detector will save customers from buying unripe watermelon and the farmers from harvesting an unripe watermelon. The researchers attempted to use the NIRS technique in determining the ripeness level of watermelon as it is widely used in the agricultural sector with high-speed analysis. The project was composed of Raspberry Pi Zero W as the microprocessor unit connected to input and output devices, such as the NIR spectral sensor and the OLED display. It was programmed by Python 3 IDLE. The detector scanned a total of 200 watermelon samples. These samples were grouped as 60 % for the training dataset, 20 % for testing, and another 20 % for evaluation. The data sets were collected and are subjected to the Support Vector Machine (SVM) algorithm. Overall, experimental results showed that the detector could correctly classify both unripe and ripe watermelons with 92.5 % accuracy. |
| first_indexed | 2024-03-07T17:29:32Z |
| format | Article |
| id | doaj.art-d9a79b549267427491630ed184f6b9e6 |
| institution | Directory Open Access Journal |
| issn | 2338-0403 |
| language | English |
| last_indexed | 2024-03-07T17:29:32Z |
| publishDate | 2020-10-01 |
| publisher | Diponegoro University |
| record_format | Article |
| series | Jurnal Teknologi dan Sistem Komputer |
| spelling | doaj.art-d9a79b549267427491630ed184f6b9e62024-03-02T18:27:50ZengDiponegoro UniversityJurnal Teknologi dan Sistem Komputer2338-04032020-10-018431732210.14710/jtsiskom.2020.1374412841Watermelon ripeness detector using near infrared spectroscopyEdwin R. Arboleda0https://orcid.org/0000-0001-9371-8895Kimberly M. Parazo1Christle M. Pareja2Department of Computer and Electronics Engineering, College of Engineering and Information Technology, Cavite State University, PhilippinesDepartment of Computer and Electronics Engineering, College of Engineering and Information Technology, Cavite State University, PhilippinesDepartment of Computer and Electronics Engineering, College of Engineering and Information Technology, Cavite State University, PhilippinesThis study aimed to design and develop a watermelon ripeness detector using Near-Infrared Spectroscopy (NIRS). The research problem being solved in this study is developing a prototype wherein the watermelon ripeness can be detected without the need to open it. This detector will save customers from buying unripe watermelon and the farmers from harvesting an unripe watermelon. The researchers attempted to use the NIRS technique in determining the ripeness level of watermelon as it is widely used in the agricultural sector with high-speed analysis. The project was composed of Raspberry Pi Zero W as the microprocessor unit connected to input and output devices, such as the NIR spectral sensor and the OLED display. It was programmed by Python 3 IDLE. The detector scanned a total of 200 watermelon samples. These samples were grouped as 60 % for the training dataset, 20 % for testing, and another 20 % for evaluation. The data sets were collected and are subjected to the Support Vector Machine (SVM) algorithm. Overall, experimental results showed that the detector could correctly classify both unripe and ripe watermelons with 92.5 % accuracy.https://jtsiskom.undip.ac.id/index.php/jtsiskom/article/view/13744watermelonnear-infrared spectroscopysupport vector machineripeness level |
| spellingShingle | Edwin R. Arboleda Kimberly M. Parazo Christle M. Pareja Watermelon ripeness detector using near infrared spectroscopy Jurnal Teknologi dan Sistem Komputer watermelon near-infrared spectroscopy support vector machine ripeness level |
| title | Watermelon ripeness detector using near infrared spectroscopy |
| title_full | Watermelon ripeness detector using near infrared spectroscopy |
| title_fullStr | Watermelon ripeness detector using near infrared spectroscopy |
| title_full_unstemmed | Watermelon ripeness detector using near infrared spectroscopy |
| title_short | Watermelon ripeness detector using near infrared spectroscopy |
| title_sort | watermelon ripeness detector using near infrared spectroscopy |
| topic | watermelon near-infrared spectroscopy support vector machine ripeness level |
| url | https://jtsiskom.undip.ac.id/index.php/jtsiskom/article/view/13744 |
| work_keys_str_mv | AT edwinrarboleda watermelonripenessdetectorusingnearinfraredspectroscopy AT kimberlymparazo watermelonripenessdetectorusingnearinfraredspectroscopy AT christlempareja watermelonripenessdetectorusingnearinfraredspectroscopy |