Carcass grading is a process used to classify meat based on the quality and the yield, focusing on maturity, marbling, fat thickness, and muscling. In the EU, the EUROP system originally designed for beef is used [1]. This system has been automated using Video Image Analysis (VIA). While significant development has been made for beef grading, this is not the case for reindeer meat, due to the smaller scale and limited geographical distribution of production. However, this industry is very important in Nordic countries, due to its cultural significance and economic value for indigenous communities. 

Currently, reindeer carcass grading adopts the EUROP system and is performed manually by a reindeer meat inspector, which is errorprone. In this study, the development of an automated reindeer-specific carcass grading system is investigated using both classical and contemporary methods, including an AI-based data pipeline. Hyperspectral imaging (HSI) is explored for this purpose due to its potential [2]. HSI data of reindeer carcasses are acquired and analyzed. Camera parameters, lighting Fig. 1. Reindeer dressed carcass images in RGB (L), HSI (M) and HSI-osavi (R) formats. conditions, and carcass positioning are systematically evaluated to obtain HSI data suitable for grading purposes. The results are discussed, and recommendations are provided to enable effective reindeer carcass grading using this technology.