Protein-Based Fish Species Identification: Dataset, Models, and Insights from Native Bangladeshi Fish
arXiv:2606.18302v1 Announce Type: cross Abstract: Correct identification of fish species is highly significant for food security, economic development, and climate resilience in Bangladesh. Protein sequences directly reflect functional and evolutionary constraints which are important for species authentication and biodiversity monitoring. Yet there exists no benchmark for native Bangladeshi fish species identification from protein sequence. In this study, we addressed this gap by introducing the first curated dataset for nine native Bangladeshi fish species of 2845 high quality protein sequenc
The increasing availability of genomic and proteomic sequencing technologies, combined with advancements in AI/ML, makes protein-based identification feasible and necessary for addressing food security and biodiversity challenges.
This development offers a precise, data-driven approach to species identification, which is crucial for sustainable fisheries management, combating illegal fishing, and supporting economic development in regions like Bangladesh.
The introduction of a benchmark dataset for protein-based fish species identification establishes a new method for biodiversity monitoring and opens avenues for AI applications in areas previously lacking specific data.
- · Bangladesh aquaculture
- · Food security researchers
- · Bioinformatics companies
- · Fish conservation efforts
- · Illegal fishing operations
- · Inaccurate traditional identification methods
Improved accuracy in fish species identification using protein sequences will enhance monitoring and regulatory compliance.
This methodology could be expanded to identify other species crucial for agriculture, forestry, or ecosystems, fostering broader biodiversity applications.
The development of robust protein-based identification systems may lead to new trade standards and authentication protocols for biological products globally.
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Read at arXiv cs.LG