HSI-AgriFoodAnomaly, a hyperspectral dataset for foreign object detection in agri-food inspection

Ensuring food safety in agri-food production requires reliable inspection data for detecting foreign objects and other contamination risks. Hyperspectral imaging (HSI) is a relevant non-destructive modality for this purpose because it combines spatial information with detailed spectral responses. This Data Descriptor presents HSI-AgriFoodAnomaly, an open hyperspectral image dataset acquired under conveyor-based industrial-like conditions for foreign object annotation in an oat, white-chocolate and dark-chocolate mixture. The dataset contains 147 calibrated hyperspectral cubes, associated red-green-blue (RGB) renderings, pixel-level binary masks, and polygon annotations. Each hyperspectral cube was acquired in the visible to near-infrared range, with 300 contiguous spectral bands covering approximately 381-1016 nm. The annotated foreign objects (FOs) include textile and fibre-based materials, plastics, paper-based materials, metals, wood and plant residues, minerals, glass, mixed-object scenes, and anomaly-free scenes. The dataset is organised into training, validation and test subsets at the cube level, in order to prevent data leakage between splits. HSI-AgriFoodAnomaly can be reused for hyperspectral image analysis, foreign object localisation, binary classification, object detection, semantic segmentation, and benchmarking of data processing pipelines. The dataset and the accompanying code are publicly available.