Schwarze, Jan-PhilippHerrmann, LenaIgelbrink, FelixHertzberg, JoachimDörr, JörgSteckel, Thilo2025-02-042025-02-042025978-3-88579-802-6https://dl.gi.de/handle/20.500.12116/45706In the food industry, natural products that fail to meet quality standards must be removed during processing. To automate this resource-intensive and error-prone process, this work presents a system for AI-supported quality detection of natural products based on hyperspectral images. The implemented system consists of three components: background filter, dimension reduction using autoencoders, and CNN classifier for quality assessment. By training multiple autoencoders for different spectral segments, the proposed architecture can extract the essential spectral information from a given input image, selecting the most informative spectral bands. The system was evaluated on datasets containing chicken legs and potatoes, recorded by a hyperspectral sensor with 224 spectral bands. The results show that the system enables efficient processing of relatively large hyperspectral datasets. Furthermore, the dimension reduction carried out is suitable for the robust classification of defective natural products.enautoencoderautomationfood processinghyperspectral imagingMLOpsText/Conference Paper10.18420/giljt2025_462944-76822944-7682