Exploring Effective Methods of Seafood Catching in Gujarat: A Review

Year : 2025 | Volume : 14 | Issue : 01 | Page : 1 5
    By

    Zalak Thakrar,

  • Yash Bharda J,

  • Harish Baleja M,

  • Yash Chauhan G,

  • Chhasiya Rakesh D.,

  1. Assistant Professor, School of Open Learning, National Forensic Sciences University Gandhinagar, Gujarat, India
  2. Student, School of Open Learning, National Forensic Sciences University Gandhinagar, Gujarat, India
  3. Student, School of Open Learning, National Forensic Sciences University Gandhinagar, Gujarat, India
  4. Student, School of Open Learning, National Forensic Sciences University Gandhinagar, Gujarat, India
  5. Student, School of Open Learning, National Forensic Sciences University Gandhinagar, Gujarat, India

Abstract

This review explores the effective methods of catching seafood in Gujarat, India, a region renowned for its rich coastal heritage and thriving fishing communities. Drawing on traditional techniques, community participation, innovation, and socioeconomic impact, the paper provides a comprehensive overview of the intricacies surrounding seafood catching in Gujarat. Traditional fishing methods, deeply rooted in cultural practices, coexist with modern technologies and sustainable initiatives, reflecting the adaptability and resilience of coastal livelihoods. While seafood catching sustains local economies and provides essential nutrition, it also faces challenges, such as overfishing and environmental degradation. Moving forward, addressing these challenges requires a holistic approach encompassing sustainable resource management, community empowerment, and policy interventions to ensure the continued prosperity of Gujarat’s coastal communities and their invaluable seafood heritage.

Keywords: Seafood catching, Coastal livelihoods, Coastal communities, Seafood heritage

[This article belongs to Research & Reviews : Journal of Ecology ]

How to cite this article:
Zalak Thakrar, Yash Bharda J, Harish Baleja M, Yash Chauhan G, Chhasiya Rakesh D.. Exploring Effective Methods of Seafood Catching in Gujarat: A Review. Research & Reviews : Journal of Ecology. 2024; 14(01):1-5.
How to cite this URL:
Zalak Thakrar, Yash Bharda J, Harish Baleja M, Yash Chauhan G, Chhasiya Rakesh D.. Exploring Effective Methods of Seafood Catching in Gujarat: A Review. Research & Reviews : Journal of Ecology. 2024; 14(01):1-5. Available from: https://journals.stmjournals.com/rrjoe/article=2024/view=189011


References

  1. Thakrar Z, Gonsai A. Combined study of oceanography and indigenous method for effective fishing. In: Proceedings of Second International Conference in Mechanical and Energy Technology: ICMET 2021. Singapore: Springer Nature Singapore; 2022. 147–155.
  2. Kishorchandra PV, Pandya Rajnikant A. A critical analysis using data mining techniques to predict students’ educational performance: Analyzing the impact of non-intellectual parameters. In: International Conference on Deep Learning and Visual Artificial Intelligence. Singapore: Springer Nature Singapore; pp. 205–213.
    1. Owusu-Boadu B, Nti IK, Nyarko-Boateng O, Aning J, Boafo V. Academic performance modelling with machine learning based on cognitive and non-cognitive. Appl Comput Syst. 2021;26(2):122–131. doi: 10.2478/acss-2021-0015.
  3. Kishorchandra PV, Rajnikant AP. A Critical Analysis Using Data Mining Techniques to Predict Students’. Deep Learning and Visual Artificial Intelligence: Proceedings of ICDLAI 2024. 2024. 205.
  4. Pandya V. Role of E-Learning based higher education in sustainable development. E Commerce for future & Trends. 2023;7(2):20–2
  5. Pamfilie R, Onete B, Maiorescu I, Pleşea D. E-learning as an alternative solution for sustainable lifelong education. Procedia-Social and Behavioral Sciences. 2012;46:4026–40
  6. Azeiteiro U, Leal Filho W, Caeiro S. E-learning and Education for Sustainability. Peter Lang; 2014.
  7. Natarajan SK, Elangovan E, Elavarasan RM, Balaraman A, Sundaram S. Review on solar dryers for drying fish, fruits, and vegetables. Environmental Science and Pollution Research. 2022;29(27):40478–40
  8. Kishorchandra PV, Vadher B, Meghnathi R, Raychura M, Keshwala K. A comprehensive review-building a secure social media environment for kids-automated content filtering with biometric feedback. International Journal of Innovative Research in Computer Science & Technology. 2024;12(4):25–
  9. Gündogdu S, Rathod N, Hassoun A, Jamroz E, Kulawik P, Gokbulut C, et al. The impact of nano/micro-plastics toxicity on seafood quality and human health: facts and gaps. Critical Reviews in Food Science and Nutrition. 2023;63(23):6445–64
  10. Garrido Gamarro E, Ryder J, Elvevoll EO, Olsen RL. Microplastics in fish and shellfish–a threat to seafood safety? J Aquat Food Prod Technol. 2020;29(4):417–4
  11. Nowland SJ, O’Connor WA, Osborne MW, Southgate PC. Current status and potential of tropical rock oyster aquaculture. Rev Fish Sci Aquac. 2020;28(1):57–
  12. Zaukuu JL, Bazar G, Gillay Z, Kovacs Z. Emerging trends of advanced sensor based instruments for meat, poultry and fish quality–A Critical reviews in food science and nutrition. 2020;60(20):3443–3460.
  13. Thakrar Z, Gonsai A. Design and development of a boundary alert system for fishermen. J Mob Comput Commun Mob 2024;11(1):31–36p.
  14. Thakrar Z, Buddhadev KJ, Bhatt HD, Bhadrecha NH, Bhogayata MD. Swimmer safety alert system for encounters with unidentified marine aquatic animals. Int J Innov Res Comput Sci Technol. 2024;12(4):47–
  15. Yu P, Liu H, Wang Z, Fu J, Zhang H, Wang J, et al. Development of urban underground space in coastal cities in China: A review. Deep Underground Science and Engineering. 2023;2(2):148–1
  16. Thakrar Z, Gonsai A. Predicting fishing effort: Data collection for machine learning model using scientific and indigenous method. In: International Conference on Information and Communication Technology for Intelligent Systems. Singapore: Springer Nature Singapore; 2023. 207–215.
  17. Yang L, Liu Y, Yu H, Fang X, Song L, Li D, et al. Computer vision models in intelligent aquaculture with emphasis on fish detection and behavior analysis: A Arch Comput Methods Eng. 2021;28:2785–2816.
  18. Wikström J. Evaluating supervised machine learning algorithms to predict recreational fishing success: A multiple species, multiple algorithms approach.
  19. Goikoetxea N, Goienetxea I, Fernandes-Salvador JA, Goñi N, Granado I, Quincoces I, et al. Machine-learning aiding sustainable Indian Ocean tuna purse seine fishery. Ecol Inform. 2024;81:102577.
  20. Kumar KS, Kumar TC, Rajan MS, Thakrar ZT, Cheepurupalli NR, Mungekar PR. Based on 5G Internet of Things Technology (Iot), the integrity of agricultural products and the sustainability of the origin’s ecological environment. J Inform Educ Res. 2024;4(2).
  21. Thakrar Z, Gonsai A. Comparing fish finding techniques using satellite and indigenous data based on different machine learning algorithms. In: Advances in Information Communication Technology and Computing: Proceedings of AICTC 2022. Singapore: Springer Nature Singapore; 2023. 329–340.
  22. Mehta N, Thaker H. Data collection for a machine learning model to suggest Gujarati recipes to cardiac patients using gujarati food and fruit with nutritive values. In: International Conference on Information and Communication Technology for Intelligent Systems.. Singapore: Springer Nature Singapore; 2023. 271–281.
Regular Issue Subscription Review Article
Volume 14
Issue 01
Received 21/11/2024
Accepted 03/12/2024
Published 12/12/2024
Publication Time 21 Days
451

Login


My IP

PlumX Metrics