In the leather industry, the mammalian skins of buffalo, cow, goat, and sheep are the permissible materials for leather-making. They serve the trade of quality leather products; hence, the knowledge of animal species in leather is inevitable. The traditional identification techniques are prone to ambiguous predictions due to insufficient reference studies. Indeed, leather image analysis with big data can pave the way for automatic and objective analysis with accurate prediction. Therefore, a novel and unique leather image data, LeaData, is created to facilitate automatic species identification in leather from grain surface analysis. A simple, cheaper, handheld digital microscope with the magnifying parameter 47x is chosen to capture the species-unique grain patterns distributed over the leather surface. Currently, the novel LeaData encloses 38,172 leather images of four species from 137 leather samples. This big data spans leather images with theoretically ideal and practically non-ideal characteristics of grain patterns. It also includes images of grain patterns varying over different body parts. Thus, the LeaData is an adequately larger pool of leather images with diverse behavior. It is also divided into three versions, LeaData-V1, LeaData-V2, and LeaData-V3, based on leather image characteristics, such as ideal, non-ideal, and body part-specific. Presenting LeaData and its versions to the research community of digital image processing and computer vision can help establish a smart leather species identification technique that can be easily accessible by leather specialists, customs officials, and leather product manufacturers. This digitized source of permissible leather species helps enable digitization in leather technology for species identification. In turn, in maintaining biodiversity preservation and consumer protection.

The leather image acquisition process is executed and assisted by the concerned leather experts of the Central Leather Research Institute (CLRI), Chennai, India. They provided 137 leather samples of four animal species, buffalo, cow, goat, and sheep, with diverse behaviors for acquisition. Celestron handheld digital microscope pro is used to capture species-specific grain patterns. The acquisition process is initiated with 47x magnification and 1024 x 1280 image resolution. Each image captured is saved in JPG format utilizing not more than 1 MB size. The images are grouped into four folders respective to four species.