Image Data Acquisition and Classification of Vannamei Shrimp Cultivation Results Based on Deep Learning
Keywords:
Classification, Deep Learning, Image Data Acquisition, Vannamei Shrimp Cultivation
Abstract
This research aimed to employ deep learning techniques to address the classification of Litopenaeus vannamei cultivation results in land ponds and tarpaulin ponds. Despite their similar appearance, distinguishable differences exist in various aspects such as color, shape, size, and market price between the two cultivation methods, often leading to consumer confusion and potential exploitation by irresponsible sellers. To mitigate this challenge, the research proposed a classification method utilizing two Convolutional Neural Network (CNN) architectures: Visual Geometry Group-16 (VGG-16) and Residual Network-50 (ResNet-50), renowned for their success in various image recognition applications. The dataset comprised 2,080 images per class of vannamei shrimp from both types of ponds. Augmentation techniques enhanced the dataset’s diversity and sample size, reinforcing the model’s ability to discern shrimp morphology variations. Experiments were conducted with learning rates of 0.001 and 0.0001 on the Stochastic Gradient Descent (SGD) and Adaptive Moment Estimation (ADAM) optimizers to evaluate their effectiveness in model training. The VGG-16 and ResNet-50 models were trained with a learning rate parameter of 0.0001, leveraging the flexibility and reasonable control provided by the SGD optimizer. Lower learning rate values were chosen to prevent overfitting and enhance training stability. The model evaluation demonstrated promising results, with both architectures achieving 100% accuracy in classifying vannamei shrimp from soil ponds and tarpaulin ponds. Furthermore, experimental findings highlight the superiority of using SGD with a learning rate of 0.0001 over 0.001 on both architectures, underscoring the significant impact of optimizer and learning rate selection on model training effectiveness in image classification tasks.
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References
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Sciences, vol. 35, no. 3, pp. 211–226, 2023, https://doi.org/10.1016/j.jksuci.2023.02.016.
[5] S. Cao, D. Zhao, Y. Sun, X. Liu, and C. Ruan, “Automatic coarse-to-fine joint detection and segmentation of underwater nonstructural
live crabs for precise feeding,” Computers and Electronics in Agriculture, vol. 180, no. January, p. 105905, jan 2021,
https://doi.org/10.1016/j.compag.2020.105905.
[6] S. Si, X. Zhang, J. Yuan, X. Zhang, Y. Yu, S. Yang, and F. Li, “Identification, classification and expression analysis of the Ras
superfamily genes in the Pacific white shrimp, Litopenaeus vannamei,” Frontiers in Marine Science, vol. 10, no. January, pp.
1–17, 2023, https://doi.org/10.3389/fmars.2023.1063857.
[7] G. Wang, G. Van Stappen, and B. De Baets, “Automated detection and counting of Artemia using U-shaped fully convolutional
networks and deep convolutional networks,” Expert Systems with Applications, vol. 171, no. June, p. 114562, jun 2021, https:
//doi.org/10.1016/j.eswa.2021.114562.
[8] S. Zhao, S. Zhang, J. Liu, H.Wang, J. Zhu, D. Li, and R. Zhao, “Application of machine learning in intelligent fish aquaculture:
A review,” Aquaculture, vol. 540, no. July, p. 736724, jul 2021, https://doi.org/10.1016/j.aquaculture.2021.736724.
[9] V. Kaya, s. Akgu¨l, and O¨ . Zencir Tanir, “IsVoNet8: A Proposed Deep Learning Model for Classification of Some Fish Species,”
Tarim Bilimleri Dergisi, vol. 29, no. 1, pp. 298–307, 2023, https://doi.org/10.15832/ankutbd.1031130.
[10] S. Raziani and M. Azimbagirad, “Deep CNN hyperparameter optimization algorithms for sensor-based human activity recognition,”
Neuroscience Informatics, vol. 2, no. September, pp. 1–8, sep 2022, https://doi.org/10.1016/j.neuri.2022.100078.
[11] Y. Ye, Q. Huang, Y. Rong, X. Yu, W. Liang, Y. Chen, and S. Xiong, “Field detection of small pests through stochastic gradient
descent with genetic algorithm,” Computers and Electronics in Agriculture, vol. 206, no. March, p. 107694, mar 2023, https:
//doi.org/10.1016/j.compag.2023.107694.
[12] J. Pinto, M. Mestre, J. Ramos, R. S. Costa, G. Striedner, and R. Oliveira, “A general deep hybrid model for bioreactor systems:
Combining first principles with deep neural networks,” Computers & Chemical Engineering, vol. 165, no. September, pp. 1–10,
sep 2022, https://doi.org/10.1016/j.compchemeng.2022.107952.
[13] M. Kundroo and T. Kim, “Federated learning with hyper-parameter optimization,” Journal of King Saud University - Computer
and Information Sciences, vol. 35, no. 9, p. 101740, 2023, https://doi.org/10.1016/j.jksuci.2023.101740.
[14] G. Pinto, Z. Wang, A. Roy, T. Hong, and A. Capozzoli, “Transfer learning for smart buildings: A critical review of algorithms,
applications, and future perspectives,” Advances in Applied Energy, vol. 5, no. February, pp. 1–23, feb 2022, https://doi.org/10.
1016/j.adapen.2022.100084.
[15] S. Hermawan, S. Rahmawati, Q. Aditia, B.Wibowo, and A. Yuswanto, “Designing aWater Temperature control and Monitoring
System for Vaname Shrimp cultivation based on the Internet of Things ( IoT ),” Jurnal Komputer dan Elektro Sains, vol. 1, no. 1,
pp. 14–17, 2023, https://doi.org/10.58291/komets.v1i1.96.
[16] Y. Zhang, C. Wei, Y. Zhong, H. Wang, H. Luo, and Z. Weng, “Deep learning detection of shrimp freshness via smartphone
pictures,” Journal of Food Measurement and Characterization, vol. 16, no. 5, pp. 3868–3876, 2022, https://doi.org/10.1007/
s11694-022-01473-4.
[17] S. Liawatimena, E. Abdurachman, A. Trisetyarso, A. Wibowo, M. K. Ario, and I. S. Edbert, “Fish Classification System Using
YOLOv3-ResNet18 Model for Mobile Phones,” CommIT Journal, vol. 17, no. 1, pp. 71–79, 2023, https://doi.org/10.21512/
COMMIT.V17I1.8107.
[18] E. Prasetyo, N. Suciati, and C. Fatichah, “Multi-level residual network VGGNet for fish species classification,” Journal of King
Saud University - Computer and Information Sciences, vol. 34, no. 8, pp. 5286–5295, 2022, https://doi.org/10.1016/j.jksuci.
2021.05.015.
[19] M. K. Alsmadi and I. Almarashdeh, “A survey on fish classification techniques,” Journal of King Saud University - Computer
and Information Sciences, vol. 34, no. 5, pp. 1625–1638, 2022, https://doi.org/10.1016/j.jksuci.2020.07.005.
[20] A. Banerjee, A. Das, S. Behra, D. Bhattacharjee, N. T. Srinivasan, M. Nasipuri, and N. Das, “Carp-DCAE: Deep convolutional
autoencoder for carp fish classification,” Computers and Electronics in Agriculture, vol. 196, no. May, p. 106810, may 2022,
https://doi.org/10.1016/j.compag.2022.106810.
[21] T. Zhao, Z. Shen, H. Zou, P. Zhong, and Y. Chen, “Unsupervised adversarial domain adaptation based on interpolation image
for fish detection in aquaculture,” Computers and Electronics in Agriculture, vol. 198, no. July, p. 107004, jul 2022, https:
//doi.org/10.1016/j.compag.2022.107004.
[22] S. A. ElGhany, M. R. Ibraheem, M. Alruwaili, and M. Elmogy, “Diagnosis of Various Skin Cancer Lesions Based on Fine-
Tuned ResNet50 Deep Network,” Computers, Materials and Continua, vol. 68, no. 1, pp. 117–135, 2021, https://doi.org/10.
32604/cmc.2021.016102.
[23] H. L. Dawson, O. Dubrule, and C. M. John, “Impact of dataset size and convolutional neural network architecture on transfer
learning for carbonate rock classification,” Computers & Geosciences, vol. 171, no. February, pp. 1–11, feb 2023, https://doi.
org/10.1016/j.cageo.2022.105284.
[24] D. B´arbulo Barrios, J. Valente, and F. van Langevelde, “Monitoring mammalian herbivores via convolutional neural networks
implemented on thermal UAV imagery,” Computers and Electronics in Agriculture, vol. 218, no. March, pp. 1–11, mar 2024,
https://doi.org/10.1016/j.compag.2024.108713.
[25] Z. Zhou, C. Fu, and R. Weibel, “Move and remove: Multi-task learning for building simplification in vector maps with a graph
convolutional neural network,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 202, no. January, pp. 205–218, aug
2023, https://doi.org/10.1016/j.isprsjprs.2023.06.004.
[26] P. K. Mall, P. K. Singh, S. Srivastav, V. Narayan, M. Paprzycki, T. Jaworska, and M. Ganzha, “A comprehensive review of deep
neural networks for medical image processing: Recent developments and future opportunities,” Healthcare Analytics, vol. 4,
no. December, pp. 1–12, dec 2023, https://doi.org/10.1016/j.health.2023.100216.
[27] N. Hasan, S. Ibrahim, and A. Azlan, “Fish diseases detection using convolutional neural network (CNN),” Int. J. Nonlinear
Anal. Appl, vol. 13, no. November, pp. 2008–6822, 2022.
[28] K. M. Knausg°ard, A. Wiklund, T. K. Sørdalen, K. T. Halvorsen, A. R. Kleiven, L. Jiao, and M. Goodwin, “Temperate fish
detection and classification: a deep learning based approach,” Applied Intelligence, vol. 52, no. 6, pp. 6988–7001, 2022, https:
//doi.org/10.1007/s10489-020-02154-9.
[29] S. Sharma and K. Guleria, “A Deep Learning based model for the Detection of Pneumonia from Chest X-Ray Images using
VGG-16 and Neural Networks,” Procedia Computer Science, vol. 218, pp. 357–366, 2023, https://doi.org/10.1016/j.procs.2023.
01.018.
[30] J. N. Mogan, C. P. Lee, K. M. Lim, and K. S. Muthu, “VGG16-MLP: Gait Recognition with Fine-Tuned VGG-16 and Multilayer
Perceptron,” Applied Sciences, vol. 12, no. 15, pp. 1–12, jul 2022, https://doi.org/10.3390/app12157639.
[31] B. Mandal, A. Okeukwu, and Y. Theis, “Masked Face Recognition using ResNet-50,” arXiv, pp. 1–8, 2021, https://doi.org/https:
//doi.org/10.48550/arXiv.2104.08997.
[32] E. Radhi and M. Kamil, “An Automatic Segmentation of Breast Ultrasound Images Using U-Net Model,” Serbian Journal of
Electrical Engineering, vol. 20, no. 2, pp. 191–203, 2023, https://doi.org/10.2298/SJEE2302191R.
[33] H. Ismail, A. F. M. Ayob, A. M. S. M. Muslim, and M. F. R. Zulkifli, “Convolutional Neural Network Architectures Performance
Evaluation for Fish Species Classification,” Journal of Sustainability Science and Management, vol. 16, no. 5, pp. 124–139,
2021, https://doi.org/10.46754/JSSM.2021.07.010.
[34] J. Deka, S. Laskar, and B. Baklial, “Automated Freshwater Fish Species Classification using Deep CNN,” Journal of The
Institution of Engineers (India): Series B, vol. 104, no. 3, pp. 603–621, 2023, https://doi.org/10.1007/s40031-023-00883-2.
[35] A. Febriana, K. Muchtar, R. Dawood, and C.-Y. Lin, “USK-COFFEE Dataset: A Multi-Class Green Arabica Coffee Bean
Dataset for Deep Learning,” in 2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom).
IEEE, jun 2022, pp. 469–473, https://doi.org/10.1109/CyberneticsCom55287.2022.9865489.
[36] T. Valeeprakhon, K. Orkphol, and P. Chaihuadjaroen, “Deep Constitutional Neural Networks based on VGG-16 Transfer Learning
for Abnormalities Peeled Shrimp Classification,” International Scientific Journal of Engineering and Technology (ISJET),
vol. 6, no. 2, pp. 13–23, 2022.
[37] H. T. Rauf, M. I. U. Lali, S. Zahoor, S. Z. H. Shah, A. U. Rehman, and S. A. C. Bukhari, “Visual features based automated
identification of fish species using deep convolutional neural networks,” Computers and Electronics in Agriculture, vol. 167,
no. July, p. 105075, dec 2019, https://doi.org/10.1016/j.compag.2019.105075.
Published
2024-06-08
How to Cite
Melinda, M., Muthiah, Z., Arnia, F., Elizar, E., & Irhmasyah, M. (2024). Image Data Acquisition and Classification of Vannamei Shrimp Cultivation Results Based on Deep Learning. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 23(3), 491-508. https://doi.org/https://doi.org/10.30812/matrik.v23i3.3850
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