Kukushkin, MaksimEnders, MatthiasKaschuba, ReinhardBogdan, MartinSchmid, ThomasKlein, MaikeKrupka, DanielWinter, CorneliaWohlgemuth, Volker2023-11-292023-11-292023978-3-88579-731-9https://dl.gi.de/handle/20.500.12116/43095Analysing harvested seeds is a time-consuming task in the seed-producing industry. Automating this process has the potential to enhance and expedite agricultural seed production. In our study, we focus on differentiating Canola seeds from visually similar non-Canola seeds using computer vision techniques. Our approach utilises both RGB and hyperspectral images, captured by a specialised camera, to train separate autoencoder neural networks. By leveraging the high spatial resolution of RGB data and the high spectral resolution of hyperspectral data, we develop distinct models for Canola seed analysis, ensuring a comprehensive and robust assessment. The autoencoder networks are trained on a dataset of Canola seeds, allowing for the extraction of latent representations from both RGB and hyperspectral data. This enables efficient compression of input data and effective discrimination between Canola and non-Canola seeds. Our proposed approach demonstrates promising results in detecting non-Canola seeds in unseen test data.anomaly detectionseed productionhyperspectral imagingautoencoderText/Conference Paper10.18420/inf2023_1691617-5468