Automatic Fish Classification Using Lanczos Resampling and Deep Learning
| dc.contributor.author | Kuswantori, Ari | |
| dc.contributor.author | Suesut, Taweepol | |
| dc.contributor.author | Suthanupaphwut, Worapanya | |
| dc.contributor.author | Tangsrirat, Worapong | |
| dc.contributor.author | Nunak, Navaphattra | |
| dc.date.accessioned | 2026-08-06T10:52:03Z | |
| dc.date.available | 2026-08-06T10:52:03Z | |
| dc.date.issued | 2025-09-01 | |
| dc.description.abstract | The development of automation in the fish industry, a vital sector of the food industry, is a highly relevant and essential topic. This development is essential for boosting output and mitigating the risk of future food shortages brought on by the world’s population expansion. Automatic fish classification using computer vision has been widely developed in fish industry automation, and a lot of research on that topic has been published. However, while some research has produced promising results using complex methods, others have applied simpler approaches with less satisfactory outcomes. This study suggests a straightforward but efficient technique for differentiating between fish species by concentrating on their main characteristics, such as body form and scale patterns. To effectively support these image capturing properties, the Lanczos re-sampling technique is used in this study. Additionally, our basic deep learning model can correctly learn and identify fish species thanks to a fish picture categorization engine created using Google Teachable Machine. Utilizing the Fish-Pak dataset, a popular fish picture dataset frequently used in studies on fish species classification, the suggested approach successfully overcomes the difficulty and attains a high accuracy rate of 97.16%. | |
| dc.identifier.citation | International Journal of Electrical and Electronic Engineering and Telecommunications, 14(5), 296-303, 2025 | |
| dc.identifier.doi | 10.18178/ijeetc.14.5.296-303 | |
| dc.identifier.issn | 23192518 | |
| dc.identifier.other | 2-s2.0-105018705803 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/17207 | |
| dc.source | International Journal of Electrical and Electronic Engineering and Telecommunications | |
| dc.subject | automatic fish classification | |
| dc.subject | deep learning | |
| dc.subject | Fish-Pak dataset | |
| dc.subject | Google Teachable Machine (GTM) | |
| dc.subject | Lanczos resampling | |
| dc.title | Automatic Fish Classification Using Lanczos Resampling and Deep Learning | |
| dc.type | Article |