LeaData a novel reference data of leather images for automatic species identification
Abstract 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 pr...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88040-1 |
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| author | Anjli Varghese Malathy Jawahar A. Amalin Prince |
| author_facet | Anjli Varghese Malathy Jawahar A. Amalin Prince |
| author_sort | Anjli Varghese |
| collection | DOAJ |
| description | Abstract 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. This study focuses on creating novel and unique leather image data, LeaData. The objective is to automatically determine species from grain surface analysis. Hence, it employs a simple, cheaper, handheld digital microscope for leather image acquisition. The magnifying parameter 47 $$\times$$ captures the species-unique grain patterns distributed over the leather surface. In total, the LeaData encloses 38,172 images of four species from 137 leather samples. This big data spans leather images with theoretically ideal and practically non-ideal grain patterns. It also includes images of grain patterns varying over different body parts. Thus, the novel LeaData is an adequately larger pool of leather images with diverse behavior. The motive is to establish a smart leather species identification technique that can be easily accessible by leather specialists, customs officials, and leather product manufacturers. Hence, this paper solely creates the bigger LeaData and presents its different versions to the digital image processing and computer vision research community. 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. |
| format | Article |
| id | doaj-art-4cdb486f354243bfb5b0ff65d5a028d0 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4cdb486f354243bfb5b0ff65d5a028d02025-02-09T12:36:06ZengNature PortfolioScientific Reports2045-23222025-02-0115111710.1038/s41598-025-88040-1LeaData a novel reference data of leather images for automatic species identificationAnjli Varghese0Malathy Jawahar1A. Amalin Prince2Department of Electrical and Electronics Engineering, BITS Pilani, K K Birla Goa CampusCentral Leather Research InstituteDepartment of Electrical and Electronics Engineering, BITS Pilani, K K Birla Goa CampusAbstract 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. This study focuses on creating novel and unique leather image data, LeaData. The objective is to automatically determine species from grain surface analysis. Hence, it employs a simple, cheaper, handheld digital microscope for leather image acquisition. The magnifying parameter 47 $$\times$$ captures the species-unique grain patterns distributed over the leather surface. In total, the LeaData encloses 38,172 images of four species from 137 leather samples. This big data spans leather images with theoretically ideal and practically non-ideal grain patterns. It also includes images of grain patterns varying over different body parts. Thus, the novel LeaData is an adequately larger pool of leather images with diverse behavior. The motive is to establish a smart leather species identification technique that can be easily accessible by leather specialists, customs officials, and leather product manufacturers. Hence, this paper solely creates the bigger LeaData and presents its different versions to the digital image processing and computer vision research community. 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.https://doi.org/10.1038/s41598-025-88040-1Digital microscopeGrain patternsImage analysisLeaDataLeather imagesSpecies identification |
| spellingShingle | Anjli Varghese Malathy Jawahar A. Amalin Prince LeaData a novel reference data of leather images for automatic species identification Scientific Reports Digital microscope Grain patterns Image analysis LeaData Leather images Species identification |
| title | LeaData a novel reference data of leather images for automatic species identification |
| title_full | LeaData a novel reference data of leather images for automatic species identification |
| title_fullStr | LeaData a novel reference data of leather images for automatic species identification |
| title_full_unstemmed | LeaData a novel reference data of leather images for automatic species identification |
| title_short | LeaData a novel reference data of leather images for automatic species identification |
| title_sort | leadata a novel reference data of leather images for automatic species identification |
| topic | Digital microscope Grain patterns Image analysis LeaData Leather images Species identification |
| url | https://doi.org/10.1038/s41598-025-88040-1 |
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