| Autor: |
Rahmanda Afridiansyah, De Rosal Ignatius Moses Setiadi |
| Jazyk: |
English<br />Indonesian |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Journal of Applied Informatics and Computing, Vol 8, Iss 2, Pp 453-462 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2548-6861 |
| DOI: |
10.30871/jaic.v8i2.8677 |
| Popis: |
The purpose of this study is to evaluate the effectiveness of the EfficientNetB1 model in identifying fish diseases across two distinct datasets: South Asian Fish Diseases and Salmon Fish Diseases. The South Asian Fish Diseases dataset includes seven categories: red bacterial disease, aeromoniasis, gill bacterial disease, fungal saprolegniasis, parasitic disease, and white tail viral disease. The Salmon dataset is divided into two parts: FreshFish and InfectedFish. Using the EfficientNetB1 algorithm, each dataset was separately trained and tested to predict species and disease. Results showed an accuracy of 98.14% for the South Asian Fish Diseases dataset and 99.18% for the Salmon Diseases dataset. These findings support the argument that the model possesses sufficient capability to detect diseases affecting various fish species. This suggests that the model could be a valuable tool in the aquaculture industry for disease management and detection strategies. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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