Exploring Affective Methods of Seafood Catching in Gujarat: A Review

Year : 2024 | Volume : | : | Page : –
By
vector

Zalak Thakrar,

vector

Yash Bharda J,

vector

Harish Baleja M,

vector

Yash Chauhan G,

vector

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 document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_127512’);});Edit Abstract & Keyword

This review explores the affective 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 socio-economic 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

How to cite this article:
Zalak Thakrar, Yash Bharda J, Harish Baleja M, Yash Chauhan G, Chhasiya Rakesh D.. Exploring Affective Methods of Seafood Catching in Gujarat: A Review. Research & Reviews : Journal of Ecology. 2024; ():-.
How to cite this URL:
Zalak Thakrar, Yash Bharda J, Harish Baleja M, Yash Chauhan G, Chhasiya Rakesh D.. Exploring Affective Methods of Seafood Catching in Gujarat: A Review. Research & Reviews : Journal of Ecology. 2024; ():-. Available from: https://journals.stmjournals.com/rrjoe/article=2024/view=0

Full Text PDF

Full Text PDF

References
document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_127512’);});Edit

  1. Thakrar Z, Gonsai A. Combined Study of Oceanography and Indigenous Method for Effective Fishing. InProceedings of Second International Conference in Mechanical and Energy Technology: ICMET 2021, India 2022 Jun 27 (pp. 147-155). Singapore: Springer Nature Singapore.
  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. InInternational Conference on Deep Learning and Visual Artificial Intelligence 2024 Mar 15 (pp. 205-213). Singapore: Springer Nature Singapore.
  3. Owusu-Boadu B, Nti IK, Nyarko-Boateng O, Aning J, Boafo V. Academic Performance Modelling with Machine Learning Based on Cognitive and Non-Cognitive.
  4. 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.
  5. Pandya V. Role of E-Learning based higher education in sustainable development. E Commerce for future & Trends. 2023 Jun 5;7(2):20-3.
  6. Pamfilie R, Onete B, Maiorescu I, Pleşea D. E-learning as an alternative solution for sustainable lifelong education. Procedia-Social and Behavioral Sciences. 2012 Jan 1;46:4026-30.
  7. Azeiteiro U, Leal Filho W, Caeiro S. E-learning and Education for Sustainability. Peter Lang; 2014.
  8. Natarajan SK, Elangovan E, Elavarasan RM, Balaraman A, Sundaram S. Review on solar dryers for drying fish, fruits, and vegetables. Environmental Science and Pollution 2022 Jun;29(27):40478-506.
  9. 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 Jul 4;12(4):25-30.
  10. Gündogdu S, Rathod N, Hassoun A, Jamroz E, Kulawik P, Gokbulut C, Aït-Kaddour A, Özogul F. The impact of nano/micro-plastics toxicity on seafood quality and human health: facts and gaps. Critical Reviews in Food Science and Nutrition. 2023 Sep 10;63(23):6445-63.
  11. Garrido Gamarro E, Ryder J, Elvevoll EO, Olsen RL. Microplastics in fish and shellfish–a threat to seafood safety?. Journal of Aquatic Food Product Technology. 2020 Apr 20;29(4):417-25.
  12. Nowland SJ, O’Connor WA, Osborne MW, Southgate PC. Current status and potential of tropical rock oyster aquaculture. Reviews in Fisheries Science & Aquaculture. 2020 Jan 2;28(1):57-70.
  13. Zaukuu JL, Bazar G, Gillay Z, Kovacs Z. Emerging trends of advanced sensor based instruments for meat, poultry and fish quality–a review. Critical reviews in food science  and nutrition. 2020 Nov 12;60(20):3443-60..
  14. Thakrar Z, Gonsai A. Design and Development of a Boundary Alert System for Fishermen. Journal of Mobile Computing, Communications & Mobile Networks. 2024;11(1):31-6p.
  15. Thakrar Z, Buddhadev KJ, Bhatt HD, Bhadrecha NH, Bhogayata MD. Swimmer Safety Alert System for Encounters with Unidentified Marine Aquatic Animals. International Journal of Innovative Research in Computer Science & Technology. 2024 Jul 12;12(4):47-51.
  16. Yu P, Liu H, Wang Z, Fu J, Zhang H, Wang J, Yang Q. Development of urban underground space in coastal cities in China: A review. Deep Underground Science and Engineering. 2023 Jun;2(2):148-72.
  17. Thakrar Z, Gonsai A. Predicting Fishing Effort: Data Collection for Machine Learning Model Using Scientific and Indigenous Method. InInternational Conference on Information and Communication Technology for Intelligent Systems 2023 Apr 27 (pp. 207-215). Singapore: Springer Nature Singapore.
  18. Yang L, Liu Y, Yu H, Fang X, Song L, Li D, Chen Y. Computer vision models in intelligent aquaculture with emphasis on fish detection and behavior analysis: a review. Archives of Computational Methods in Engineering. 2021 Jun;28:2785-816.
  19. Wikström J. Evaluating supervised machine learning algorithms to predict recreational fishing success: A multiple species, multiple algorithms approach.
  20. Goikoetxea N, Goienetxea I, Fernandes-Salvador JA, Goñi N, Granado I, Quincoces I, Ibaibarriaga L, Ruiz J, Murua H, Caballero A. Machine-learning aiding sustainable Indian Ocean tuna purse seine fishery. Ecological Informatics. 2024 Jul 1;81:102577.
  21. 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. Journal of Informatics Education and Research. 2024 Jun 15;4(2).
  22. Thakrar Z, Gonsai A. Comparing Fish Finding Techniques using Satellite and Indigenous Data based on Different Machine Learning Algorithms. InAdvances in Information Communication Technology and Computing: Proceedings of AICTC 2022 2023 May 30 (pp. 329-340). Singapore: Springer Nature Singapore.
  23. 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. InInternational Conference on Information and Communication Technology for Intelligent Systems 2023 Apr 27 (pp. 271-281). Singapore: Springer Nature Singapore.
Ahead of Print Subscription Review Article
Volume
Received 21/11/2024
Accepted 03/12/2024
Published 12/12/2024
153