Species identification for Indian seafood markets: A machine learning approach with a fish dataset.

The Indian sea fish market is a dynamic and significant sector, contributing to both the domestic economy and the global seafood trade. This fish dataset is specifically curated for machine learning applications in the Indian seafood industry. It includes a comprehensive collection of images representing eight commercially significant fish species native to Indian waters, comprising a total of 8488 images. This dataset serves as a valuable resource for developing and refining machine learning models in the domain. It focuses on the eight most widely consumed fish species in India: Pomfret (scientific name: Pampus argeneus, local name: Paplet), Mackerel (scientific name: Scomber scombrus, local name: Bangda), Black Snapper (scientific name: Apsilus dentatus, local name: Tilapia), Indian Carp (scientific name: Cyprinus carpio, local name: Rohu), Prawn (scientific name: Dendrobranchiata, local name: Kolambi), Pink Perch (scientific name: Zalembius rosaceus, local name: Rani Fish), Indian flathead (scientific name: Lates calcarifer, local name: Bhetki Fish) and Black Pomfret (scientific name: Parastromateus niger, local name: Black Paplet). Article details the data acquisition process, and metadata structure, ensuring accessibility and usability for researchers and industry professionals. This resource facilitates the development of machine learning models for tasks such as species identification within the Indian seafood industry.