SpringerLink
Log in

Shrimp classification for white spot syndrome detection through enhanced gated recurrent unit-based wild geese migration optimization algorithm

  • Original Paper
  • Published:
Virus Genes Aims and scope Submit manuscript

Abstract

The major dangerous viral infection for cultivated shrimps is WSSV. The virus is extremely dangerous, spreads swiftly, and may result in up to 100% mortality in 3–10 days. The vast wrapped double stranded DNA virus known as WSSV describes a member of the Nimaviridae viral family’s species Whispovirus. It impacts a variety of crustacean hosts but predominantly marine shrimp species that are raised for commercial purposes. The entire age groups are affected by the virus, which leads to widespread mortality. Mesodermal and ectodermal tissues, like the lymph nodes, gills, and cuticular epithelium, represents the centres of infection. Complete genome sequencing related to the WSSV strains from Thailand, China, and Taiwan has identified minute genetic variations amongst them. There exist conflicting findings on the causes of WSSV pathogenicity, which involve variations in the size associated with the genome, the count of tandem repeats, and the availability or lack of certain proteins. Hence, this paper plans to perform the shrimp classification for the WSSV on the basis of novel deep learning methodology. Initially, the data is gathered from the farms as well as internet sources. Next, the pre-processing of the gathered shrimp images is accomplished using the LBP technique. These pre-processed images undergo the segmentation process utilizing the TGVFCMS approach. The extraction of the features from these segmented images is performed by the PLDA technique. In the final step, the classification of the shrimp into healthy shrimp and WSSV affected shrimp is done by the EGRU, in which the parameter tuning is accomplished by the wild GMO algorithm with the consideration of accuracy maximization as the major objective function. Performance indicators for accuracy have been compared with those of various conventional methods, and the results show that the methodology is capable of accurately identifying the shrimp WSSV illness.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data Availability

The data’s used to support the findings of this study are included within this article.

Abbreviations

WSSV:

White spot syndrome virus

NBR:

Negative binomial regression

LBP:

Local binary pattern

IRR:

Incident rate ratio

TGVFCMS:

Total generalized variation fuzzy C means

WSD:

White spot disease

PLDA:

Probabilistic linear discriminant analysis

TLGM:

Threshold logistic generalized model

EGRU:

Enhanced gated recurrent unit

LMM:

Linear mixed model

GMO:

Geese migration optimization

qPCR:

Quantitative-real time PCR

YHV:

Yellow-headed virus

SVM:

Support vector machine

IMNV:

Infectious myonecrosis virus

KNN:

K nearest neighbor

NHB:

Necrotizing hepatopancreatitis bacterium

DICNN:

Dense inception convolutional neural network

ANN:

Artificial neural network

References

  1. Hoa TT, Zwart MP, Phuong NT, Oanh DT, Mart CM, Vlak JM (2011) Mixed-genotype white spot syndrome virus infections of shrimp are inversely correlated with disease outbreaks in ponds. J Gen Virol 92:675–680

    Article  CAS  PubMed  Google Scholar 

  2. Panichareon B, Khawsak P, Deesukon W, Sukhumsirichart W (2011) Multiplex real-time PCR and high-resolution melting analysis for detection of white spot syndrome virus, yellow-head virus, and penaeus monodon densovirus in penaeid shrimp. J Virol Methods 178:16–21

    Article  CAS  PubMed  Google Scholar 

  3. Samanman S, Kanatharana P, Chotigeat W, Deachamag P, Thavarungkul P (2011) Highly sensitive capacitive biosensor for detecting white spot syndrome virus in shrimp pond water. J Virol Methods 173:75–84

    Article  CAS  PubMed  Google Scholar 

  4. Shekhar MS, Gopikrishna G, DilliKumar (2010) Development of immunodot blot assay for the detection of white spot syndrome virus infection in shrimps (Penaeus monodon). Aquac Res 41(11):1683–1690

    Article  CAS  Google Scholar 

  5. Sithigorngula P, Rukpratanporn S, Chaivisuthangkura P, Sridulyakul P, Longyant S (2011) Simultaneous and rapid detection of white spot syndrome virus and yellow head virus infection in shrimp with a dual immune chromatographic strip test. J Virol Methods 173:85–91

    Article  Google Scholar 

  6. Walker PJ, Winton JR (2010) Emerging viral diseases of fish and shrimp. Vet Res. https://doi.org/10.1051/vetres/2010022

    Article  PubMed  PubMed Central  Google Scholar 

  7. Walker PJ, Gudkovs N, Mohan CV, Stalin Raj V, Pradeep B, Sergeant E, Chandra Mohan AB, Ravibabu G, Karunasagur I, Santiago TC (2011) Longitudinal disease studies in small-holder black tiger shrimp (Penaeus monodon) farms in Andhra Pradesh, India. II. Multiple WSSV genotypes associated with disease outbreaks in ponds seeded with uninfected postlarvae. Aquaculture 319:18–24

    Article  Google Scholar 

  8. **aoli X, **uzhen S, Wenbin Z (2011) Development and application of antibody microarray for white spot syndrome virus detection in shrimp. Chin J Oceanol Limnol 29:930–941

    Article  Google Scholar 

  9. Flegel TW (2012) Historic emergence, impact and current status of shrimp pathogens in Asia. J Invertebr Pathol 110(2):166–173

    Article  PubMed  Google Scholar 

  10. Gao H, Kong J, Li Z, **ao G, Meng X (2011) Quantitative analysis of temperature, salinity and pH on WSSV proliferation in Chinese shrimp Fenneropenaeus chinensis by real-time PCR. Aquaculture 312:26–31

    Article  CAS  Google Scholar 

  11. Hossain A, Nandi SP, Siddique MA, Sanyal SK, Sultana M, Hossain MA (2015) Prevalence and distribution of white spot syndrome virus in cultured shrimp. Lett Appl Microbiol 60:128–134

    Article  CAS  PubMed  Google Scholar 

  12. Iqbal M, Kabir M, Alan C, Mamun M, Hossain M (2011) Seasonal status of white spot syndrome virus in broodstocks, nauplii and postlarvae of black tiger shrimp (Penaeus monodon) in Bangladesh. Int J Nat Sci 1:56–61

    Article  Google Scholar 

  13. Jang IK, Qiao G, Kim SK (2014) Effect of multiple infections with white spot syndrome virus (WSSV) and vibrio anguillarum on Pacific white shrimp, Litopenaeus vannamei (L.): mortality and viral replication. J Fish Dis 37(10):911–920

    Article  CAS  PubMed  Google Scholar 

  14. Jia X, Wang F, Lu Y, Zhang D, Dong S (2014) Immune responses of Litopenaeus vannamei to thermal stress: a comparative study of shrimp in freshwater and seawater conditions. Mar Freshwat Behav Physiol 47:79–92

    Article  CAS  Google Scholar 

  15. Xue S, Wei J, Li J, Geng X, Sun J (2015) Effects of total ammonia, temperature and salinity on the mortality and viral replication of WSSV-infected Chinese shrimp (Fenneropenaeus chinensis). Aquac Res 48(1):236–245

    Article  Google Scholar 

  16. Nagalakshmi G, Jyothi S (2013) White spot syndrome virus detection in shrimp images using image segmentation techniques. Int J Adv Res Comput Sci Softw Eng 3(9):1

    Google Scholar 

  17. Pradeep B, Rai P, Mohan SA, Shekhar SM, Karunasagar I (2012) Biology, host range, pathogenesis and diagnosis of white spot syndrome virus. Indian J Virol 23(2):161–174

    Article  PubMed  PubMed Central  Google Scholar 

  18. Prayitno SB, Verdegem MCJ, Verreth JAJ, Vlak JM (2022) White spot syndrome virus host range and impact on transmission. Rev Aquac. https://doi.org/10.1111/raq.12676

    Article  Google Scholar 

  19. Bipul Dey K, Girsha Dugassa H, Sheban Hinzano M, Peter Bossier (2019) Causative agent, diagnosis and management of white spot disease in shrimp: a review. Rev Aquac 12(2):822–865

    Google Scholar 

  20. Piamsomboon P, Inchaisri C, Wongtavatchai J (2016) Climate factors influence the occurrence of white spot disease in cultured penaeid shrimp in Chanthaburi province, Thailand. Aquac Environ Interact 8:331–337

    Article  Google Scholar 

  21. Tingting X, **ujuan S, Yingxia L, Tao Y, Guangliang T, Qiang W, Chong W, Tang KFJ, Qingli Z, **anshi J (2020) Investigation of white spot syndrome virus (WSSV) infection in wild crustaceans in the Bohai Sea

  22. Gayathri AAD, Amarakoon U, Wijegoonawardane PKM, Wicraamasinghe WAL (2016) Detection and prevalence of white spot syndrome disease (WSSV) in shrimp farms. World Sci News 56:239–251

    Google Scholar 

  23. Mohamed ME (2019) A comparison of the severity of white spot disease in cultured shrimp (Fenneropenaeus indicus) at a farm level in Egypt I-molecular, histopathological and field observations. Egypt J Aquatic Biol Fisher 23(2):613–637

    Google Scholar 

  24. Oakey J, Smith C, Underwood D, Afsharnasab M, Alday-Sanz V, Dhar A, Sivakumar S, Sahul AS (2019) Global distribution of white spot syndrome virus genotypes determined using a novel genoty** assay. Arch Virol 164(8):2061–2082

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Trang TT, Hung NH, Ninh NH, Knibb W, Nguyen NH (2019) Genetic variation in disease resistance against white spot syndrome virus (WSSV) in Liptopenaeus vannamei. Front Genet. https://doi.org/10.3389/fgene.2019.00264

    Article  PubMed  PubMed Central  Google Scholar 

  26. Hu WC, Wu HT, Zhang YF, Zhang SH, Lo CH (2020) Shrimp recognition using ShrimpNet based on convolutional neural network. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-01727-3

    Article  Google Scholar 

  27. de Tailly JBD, Keitel J, Owen MAG, Alcaraz-Calero JM, Alexander ME, Sloman KA (2021) Monitoring methods of feeding behaviour to answer key questions in penaeid shrimp feeding. Rev Aquac 13(4):1–16

    Google Scholar 

  28. Lakshmanan R, Veerasamy M (2022) A novel neural network model for shrimp segmentation to detect white spot syndrome. J Intell Fuzzy Syst 43:1453–1466

    Article  Google Scholar 

  29. Ramachandran L, Mohan V, Senthilkumar S, Ganesh J (2022) Early Detection and identification of white spot syndrome in shrimp using an improved deep convolutional neural network. J Intell Fuzzy Syst. https://doi.org/10.3233/JIFS-232687

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamilnadu, 611002, India

    L. Ramachandran & S. Senthilkumar

  2. Department of Electrical and Electronics Engineering, University College of Engineering, Panruti Campus, Panruti, Tamilnadu, 607106, India

    S. P. Mangaiyarkarasi

  3. Srinivasa Ramanujan Centre, SASTRA Deemed to Be University, Kumbakonam, Tamilnadu, 612001, India

    A. Subramanian

Authors
  1. L. Ramachandran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. P. Mangaiyarkarasi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Subramanian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. Senthilkumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Ramachandran and Senthilkumar wrote the main manuscript text, Mangaiyarkarasi and Subramaniyan.prepared literature review. All authors reviewed the manuscript.

Corresponding author

Correspondence to L. Ramachandran.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Edited by Zhen Fu.

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

Ramachandran, L., Mangaiyarkarasi, S.P., Subramanian, A. et al. Shrimp classification for white spot syndrome detection through enhanced gated recurrent unit-based wild geese migration optimization algorithm. Virus Genes 60, 134–147 (2024). https://doi.org/10.1007/s11262-023-02049-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11262-023-02049-0

Keywords

Search

Navigation