Comparison of EfficientNetB1 Model Effectiveness in Identifying Fish Diseases in South Asian Fish Diseases and Salmon Fish Diseases

Rahmanda Afridiansyah; De Rosal Ignatius Moses Setiadi

doi:10.30871/jaic.v8i2.8677

Rahmanda Afridiansyah Universitas Dian Nuswantoro
De Rosal Ignatius Moses Setiadi Universitas Dian Nuswantoro https://orcid.org/0000-0001-6615-4457

DOI: https://doi.org/10.30871/jaic.v8i2.8677

Keywords: Convolutional Neural Network, Classification, EfficientNetB1, Fish Disease

Abstract

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.

Downloads

Download data is not yet available.

References

W. Zhou and X. Li, “Fish diversity and selection of taxa for conservation in the Salween and Irrawaddy Rivers, Southeast Asia,” Sci. Rep., vol. 14, no. 1, p. 2393, Jan. 2024, doi: 10.1038/s41598-024-51205-5.

E. Paujiah, I. Zulfahmi, J. M. Affan, M. Fina, and B. Nafis, “Composition, Conservation Status, and Market Value of Fish Landed at the Labuhan Haji Fishing Port, Aceh, Indonesia,” J. Penelit. Pendidik. IPA, vol. 10, no. 7, pp. 4158–4171, Jul. 2024, doi: 10.29303/jppipa.v10i7.8333.

H. D. Rodger, “Fish Disease Causing Economic Impact in Global Aquaculture,” in Fish Vaccines, 2016, pp. 1–34. doi: 10.1007/978-3-0348-0980-1_1.

S. Fanijo, “AI4CRC: A Deep Learning Approach Towards Preventing Colorectal Cancer,” J. Futur. Artif. Intell. Technol., vol. 1, no. 2, pp. 143–159, Sep. 2024, doi: 10.62411/faith.2024-28.

R. K. Rachman, D. R. I. M. Setiadi, A. Susanto, K. Nugroho, and H. M. M. Islam, “Enhanced Vision Transformer and Transfer Learning Approach to Improve Rice Disease Recognition,” J. Comput. Theor. Appl., vol. 1, no. 4, pp. 446–460, Apr. 2024, doi: 10.62411/jcta.10459.

A. L. Hermawan, “Klasifikasi Penyakit Daun Jagung Menggunakan Lightweight Convolutional Neural Network,” JIIFKOM (Jurnal Ilm. Inform. dan Komputer), vol. 2, no. 2, pp. 1–7, Jul. 2023, doi: 10.51901/jiifkom.v2i2.347.

G. A. Sandag, P. Tangka, and W. Italipessy, “Enhancing Monkeypox Disease Detection Performance: A Transfer Learning Approach for Accurate Image Identification,” in 2023 5th International Conference on Cybernetics and Intelligent System (ICORIS), Oct. 2023, pp. 1–6. doi: 10.1109/ICORIS60118.2023.10352275.

F. Kurniawan, G. B. Satrya, and F. Kamalov, “Lightweight Fish Classification Model for Sustainable Marine Management: Indonesian Case,” arXiv. Jan. 04, 2024. [Online]. Available: http://arxiv.org/abs/2401.02278

J. Lu, L. Tan, and H. Jiang, “Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification,” Agriculture, vol. 11, no. 8, p. 707, Jul. 2021, doi: 10.3390/agriculture11080707.

M. Tan and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” May 2019, [Online]. Available: http://arxiv.org/abs/1905.11946

W. Hastomo et al., “Classification of Brain Image Tumor using EfficientNet B1-B2 Deep Learning,” Semesta Tek., vol. 27, no. 1, pp. 46–54, May 2024, doi: 10.18196/st.v27i1.19691.

D. I. Roesma, D. H. Tjong, D. R. Aidil, F. D. L. Prawira, and A. Saputra, “Freshwater fish diversity from Siberut Island, a small island in the western part of Sumatra, Indonesia,” Biodiversitas J. Biol. Divers., vol. 25, no. 2, Mar. 2024, doi: 10.13057/biodiv/d250244.

Y. A. Auliya, I. Fadah, Y. Baihaqi, and I. N. Awwaliyah, “Green Bean Classification: Fully Convolutional Neural Network with Adam Optimization,” Math. Model. Eng. Probl., vol. 11, no. 6, pp. 1641–1648, Jun. 2024, doi: 10.18280/mmep.110626.

A. Pratiwi and A. Fauzi, “Implementation Of Deep Learning on Flower Classification Using CNN Method,” J. Tek. Inform., vol. 5, no. 2, 2024, doi: 10.52436/1.jutif.2024.5.2.1674.

S. K. Wulandari and J. Jasmir, “Penggunaan Resnet-50 Untuk Deteksi Penyakit Ikan Air Tawar di Akuakultur Studi Kasus pada Akuakultur Asia Selatan,” in Prosiding Seminar Nasional Bisnis, Teknologi Dan Kesehatan (SENABISTEKES), 2024, pp. 17–24.

M. S. Ahmed and S. M. Jeba, “SalmonScan: A novel image dataset for machine learning and deep learning analysis in fish disease detection in aquaculture,” Data Br., vol. 54, p. 110388, Jun. 2024, doi: 10.1016/j.dib.2024.110388.

T. S. Winanto, C. Rozikin, and A. Jamaludin, “Analisa Performa Arsitektur Transfer Learning Untuk Mengindentifikasi Penyakit Daun Pada Tanaman Pangan,” J. Appl. Informatics Comput., vol. 7, no. 1, pp. 68–81, Jul. 2023, doi: 10.30871/jaic.v7i1.5991.

S.-Y. Lin and C.-L. Lin, “Brain tumor segmentation using U-Net in conjunction with EfficientNet,” PeerJ Comput. Sci., vol. 10, p. e1754, Jan. 2024, doi: 10.7717/peerj-cs.1754.

M. Ha, “Top-Heavy CapsNets Based on Spatiotemporal Non-Local for Action Recognition,” J. Comput. Theor. Appl., vol. 2, no. 1, pp. 39–50, May 2024, doi: 10.62411/jcta.10551.

S. Biswas, “Freshwater Fish Disease Aquaculture in south asia,” kaggle, 2026. https://www.kaggle.com/datasets/subirbiswas19/freshwater-fish-disease-aquaculture-in-south-asia%0A

T. S. Nabila and A. Salam, “Classification of Brain Tumors by Using a Hybrid CNN-SVM Model,” J. Appl. Informatics Comput., vol. 8, no. 2, 2024, doi: 10.30871/jaic.v8i2.8277.

A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, “A survey of the recent architectures of deep convolutional neural networks,” Artif. Intell. Rev., vol. 53, no. 8, pp. 5455–5516, Dec. 2020, doi: 10.1007/s10462-020-09825-6.

M. T. H. Khan Tusar, M. T. Islam, A. H. Sakil, M. N. H. N. Khandaker, and M. M. Hossain, “An Intelligent Telediagnosis of Acute Lymphoblastic Leukemia using Histopathological Deep Learning,” J. Comput. Theor. Appl., vol. 2, no. 1, pp. 1–12, May 2024, doi: 10.62411/jcta.10358.

M. A. Hambali and P. A. Agwu, “Adversarial Convolutional Neural Network for Predicting Blood Clot Ischemic Stroke,” J. Comput. Theor. Appl., vol. 2, no. 1, pp. 51–64, Jun. 2024, doi: 10.62411/jcta.10516.