Skip to main content

Advertisement

SpringerLink
Log in

FishRNFuseNET: development of heuristic-derived recurrent neural network with feature fusion strategy for fish species classification

  • Regular paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

The classification of fish species has become an essential task for marine ecologists and biologists for the estimation of large quantities of fish variants in their own environment and also to supervise their population changes. Different conventional classification is expensive, time-consuming, and laborious. Scattering and absorption of light in deep sea atmosphere achieves a very low-resolution image and becomes highly challenging for the recognition and classification of fish variants. Then, the performance rate of existing computer vision methods starts to reduce underwater because of highly indistinct features and background clutter of marine species. The attained classification issues can be resolved using deep structured models, which are highly recommended to enhance the performance rate in fish species classification. But, only a limited amount of fish datasets is available, which makes the system more complex, and also, they need enormous amounts of datasets to perform training. So, it is essential to develop an automated and optimized system to detect, categorize, track, and minimize manual interference in fish species classification. Thus, this paper aims to suggest a new fish species classification model by the optimized recurrent neural network (RNN) and feature fusion. Initially, standard underwater images are acquired from a standard database. Then, the gathered images are pre-processed for cleaning and enhancing the quality of images using “contrast limited adaptive histogram equalization (CLAHE) and histogram equalization”. Then, the deep feature extractions are obtained using DenseNet, MobileNet, ResNet, and VGG16, where the gathered features are given to the new phase optimal feature selection. They are performed with a new heuristic algorithm called “modified mating probability-based water strider algorithm (MMP-WSA)” that attains the optimal features. Further, the optimally selected features are further fed to the feature fusion process, where the feature fusion is carried out using the adaptive fusion concept. Here, the weights are tuned using the designed MMP-WSA. In addition, the fused features are sent to the classification phase, where the classification is performed using developed FishRNFuseNET, in which the parameters of the RNN are tuned by developed MMP-WSA for getting accurate classified outcomes. The proposed method is an effective substitute for time-consuming and strenuous approaches in human identification by professionals, and it turned as a benefit to monitor the biodiversity of fish in their place.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Allken V, Handegard NO, Rosen S, Schreyeck T, Mahiout T, Malde K (2019) Fish species identification using a convolutional neural network trained on synthetic data. ICES J Mar Sci 76(1):342–349

    Article  Google Scholar 

  2. Anderson Aparecido dos Santos AA, Gonçalves WN (2019) Improving pantanal fish species recognition through taxonomic ranks in convolutional neural networks. Ecol Inform 53.

  3. Jalal A, Salman A, Mian A, Shortis M, Shafait F (2020) Fish detection and species classification in underwater environments using deep learning with temporal information. Ecol Inform 57.

  4. Jin L, Liang H (2017) Deep learning for underwater image recognition in small sample size situations. OCEANS, pp 1–4.

  5. Li X, Shang M, Qin H, Chen L (2015) Fast accurate fish detection and recognition of underwater images with fast r-cnn. OCEANS, pp 1–5.

  6. Qin JLH, Li X, Zhang C (2016) Deepfish: accurate underwater live fish recognition with a deep architecture. Neurocomputing 187:49–58

    Article  Google Scholar 

  7. Qiu C, Zhang S, Wang C, Yu Z, Zheng H, Zheng B (2018) Improving transfer learning and squeeze- and excitation networks for small-scale fine-grained fish image classification. IEEE Access 6:78503–78512

    Article  Google Scholar 

  8. Rauf HT, Ikram Ullah Lali M, Zahoor S, Shah SZH, Rehman AU, Bukhari SAC (2019) Visual features based automated identification of fish species using deep convolutional neural networks. Comp Electron Agric, vol. 167.

  9. Rodrigues MTA, Freitas MHG, Pádua F, Gomes RM, Carrano EG (2015) Evaluating cluster detection algorithms and feature extraction techniques in automatic classification of fish species. Pattern Anal Appl 18(4):783–797

    Article  MathSciNet  Google Scholar 

  10. Salman A, Jalal A, Shafait F, Mian A, Shortis M, Seager J, Harvey E (2016) Fish species classification in unconstrained underwater environments based on deep learning. Limnol Oceanogr Methods 14(9):570–585

    Article  Google Scholar 

  11. Siddiqui S, Salman A, Malik I, Shafait F, Mian A, Shortis M, Harvey E (2018) Automatic fish species classification in underwater videos: exploiting pretrained deep neural network models to compensate for limited labelled data. ICES J Mar Sci 75:1–16

    Article  Google Scholar 

  12. Storbeck F, Daan B (2011) Fish species recognition using computer vision and a neural network. Fish Res 51:11–15

    Article  Google Scholar 

  13. Tamou B, Ben A (2018) Nasreddine underwater live fish recognition by deep learning. Image Signal Process, pp 275–283.

  14. Tharwat A, Hemedan AA, Hassanien AE, Gabel T (2018) A biometric-based model for fish species classification. Fish Res 204:324–336

    Article  Google Scholar 

  15. Villon S, Mouillot D, Chaumont M, Darling ES, Subsol G, Claverie T, Villéger S (2018) A deep learning method for accurate and fast identification of coral reef fishes in underwater images. Eco Inform 48:238–244

    Article  Google Scholar 

  16. White C, Svellingen DJ, Strachan NJC (2006) Automated measurement of species and length of fish by computer vision. Fish Res 80:203–310

    Article  Google Scholar 

  17. Huang PX, Boom BJ, Fisher RB (2015) Hierarchical classification with reject option for live fish recognition. Mach Vis Appl 26(1):89–102

    Article  Google Scholar 

  18. Sébastien Villon, G, Mouillot D, Chaumont M, Darling ES, Subsol G, Claverie T, Villéger S (2018) A deep learning method for accurate and fast identification of coral reef fishes in underwater images. Ecol Inform 48:238–244.

  19. Villon S, Iovan C, Mangeas M, Claverie T, Mouillot D, Villeger S, Vigliola L (2021) Automatic underwater fish species classification with limited data using few-shot learning. Ecol Inform, vol. 63.

  20. Qiu C, Zhang S, Wang C, Yu Z, Zheng H, Zheng B (2018) Improving transfer learning and squeeze- and-excitation networks for small-scale fine-grained fish image classification. IEEE Access 6:78503–78512

    Article  Google Scholar 

  21. Mathur M, Vasudev D, Sahoo S, Jain D, Goel N (2020) Crosspooled FishNet: transfer learning based fish species classification model. Multimedia Tools Appl 79:31625–31643

    Article  Google Scholar 

  22. Iqbal MA, Wang Z, Anwar Ali Z, Riaz S (2021) Automatic fish species classification using deep convolutional neural networks. Wireless Pers Commun 116:1043–1053

    Article  Google Scholar 

  23. Prasetyo E, Suciati N, Fatichah C (2021) Multi-level residual network VGGNet for fish species classification. J King Saud Univ Comp Inform Sci 34(12)

  24. Jalal A, Salman A, Mian A, Shortis M, Shafait F (2020) Fish detection and species classification in underwater environments using deep learning with temporal information. Ecol Inform, Vol. 57.

  25. Jing Hu, Li D, Duan Q, Han Y, Chen G, Si X (2012) Fish species classification by color, texture and multi-class support vector machine using computer vision. Comput Electron Agric 88:133–140

    Article  Google Scholar 

  26. Khojasteh P, Aliahmad B, Arjunan SP, Kumar DK (2018) Introducing a novel layer in convolutional neural network for automatic identification of diabetic retinopathy. In: 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC), 2018, pp 5938–5941.

  27. Radhiyah A, Harsono T, Sigit R (2016) Comparison study of Gaussian and histogram equalization filter on dental radiograph segmentation for labelling dental radiograph. In: 2016 International Conference on Knowledge Creation and Intelligent Computing (KCIC), pp. 253–258.

  28. Ghatwary N, Ye X, Zolgharni M (2019) Esophageal abnormality detection using DenseNet based faster R-CNN with gabor features. IEEE Access 7:84374–84385.

  29. Olivares-Mercado J, Toscano-Medina K, Sanchez-Perez G, Portillo-Portillo J, Perez-Meana H, Benitez-Garcia G (2019) Analysis of hand-crafted and learned feature extraction methods for real-time facial expression recognition. In: 2019 7th International Workshop on Biometrics and Forensics (IWBF), pp. 1–6.

  30. Xiong J, Yu D, Liu S, Shu L, Wang X, Liu Z (2021) A review of plant phenotypic image recognition technology based on deep learning. Electronics 10(1):81

  31. Theckedath D, Sedamkar RR (2020) Detecting affect states using VGG16, ResNet50 and SE‑ResNet50 Networks. SN Computer Science 1

  32. Kaveh A, Dadras Eslamlou A (2020) Water strider algorithm: a new meta-heuristic and applications. Structures 25:520–541.

  33. Vallabhajosyula S, Sistla V, Kolli VKK (2022) Transfer learning-based deep ensemble neural network for plant leaf disease detection. J Plant Dis Prot 129:545–558

  34. Seo J-H, Im C-H, Heo C-G, Kim J-K, Jung H-K, Lee C-G (2006) Multimodal function optimization based on particle swarm optimization. IEEE Trans Magn 42(4):1095–1098

    Article  ADS  Google Scholar 

  35. Zhang Q, Liu L (2019) Whale optimization algorithm based on Lamarckian learning for global optimization problems. IEEE Access 7:36642–36666.

  36. Mishra S, Ray PK (2016) Power quality improvement using photovoltaic fed DSTATCOM Based on JAYA optimization. IEEE Trans Sustain Energy 7(4):1672–1680

    Article  ADS  Google Scholar 

  37. Ren L, Tian Ye, Yang X, Wang Qi, Wang L, Geng X, Wang K, Zengfeng Du, Li Y, Lin H (2023) Rapid identification of fish species by laser-induced breakdown spectroscopy and Raman spectroscopy coupled with machine learning methods. Food Chem 400:134043

    Article  CAS  PubMed  Google Scholar 

  38. Prasenan P, Suriyakala CD (2023) Novel modified convolutional neural network and FFA algorithm for fish species classification. J Combinat Optim 45

  39. De Graeve M, Birse N, Hong Y, Elliott CT, Hemeryck LY, Vanhaecke L (2023) Multivariate versus machine learning-based classification of rapid evaporative Ionisation mass spectrometry spectra towards industry based large-scale fish speciation. Food Chem 404:134632

    Article  PubMed  Google Scholar 

  40. Zitek A, Oehm J, Schober M, Tchaikovsky A, Irrgeher J, Retzmann A, Thalinger B, Traugott M, Prohaska T (2023) Evaluating 87Sr/86Sr and Sr/Ca ratios in otoliths of different European freshwater fish species as fishery management tool in an Alpine foreland with limited geological variability. Fish Res 260:106586

    Article  Google Scholar 

  41. https://www.kaggle.com/datasets/sripaadsrinivasan/fish-species-image-data

  42. https://alzayats.github.io/DeepFish

  43. Volkan K, Akgül İ, Tanir ÖZ (2023) IsVoNet8: a proposed deep learning model for classification of some fish species. J Agric Sci 29(1):298–307

    Google Scholar 

  44. Raveendranadh B, Tamilselvan S (2023) An accurate attack detection framework based on exponential polynomial kernel-centered deep neural networks in the wireless sensor network. Emerg Telecommun Technol, e4726.

  45. Shang Y, Li J (2018) Study on echo features and classification methods of fish species. In: 2018 10th international conference on wireless communications and signal processing (WCSP), 45

  46. Jose JA, Sathish Kumar C, Sureshkumar S (2021) Tuna classification using super learner ensemble of region-based CNN-grouped 2D-LBP models. Inform Process Agric 9(1)

  47. Rachel JL, Varalakshmi JP (2019) Classification of breeding fish using deep learning from the captured video. In: 2019 11th international conference on advanced computing (ICoAC), pp 48–55.

Download references

Author information

Authors and Affiliations

  1. Department of Computing Technologies, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Chennai, 603203, Tamilnadu, India

    M. Bhanumathi

  2. Department of Computing Technologies, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Chennai, 603203, Tamilnadu, India

    B. Arthi

Authors
  1. M. Bhanumathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. Arthi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors have made substantial contributions to conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Corresponding author

Correspondence to M. Bhanumathi.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bhanumathi, M., Arthi, B. FishRNFuseNET: development of heuristic-derived recurrent neural network with feature fusion strategy for fish species classification. Knowl Inf Syst 66, 1997–2038 (2024). https://doi.org/10.1007/s10115-023-01987-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-023-01987-3

Keywords

Search

Navigation