SpectroFood dataset: A comprehensive fruit and vegetable hyperspectral meta-dataset for dry matter estimation - Ioannis Malounas; Wout Vierbergen; Sezer Kutluk; Manuela Zude-Sasse; Kai Yang; Ming Zhao; Dimitrios Argyropoulos; Jonathan Van Beek; Eva Ampe; Spyros Fountas (Data in Brief, 2024)
In the dataset presented in this article, samples belonging to one of the following crops, apple, broccoli, leek, and mushroom, were measured by hyperspectral cameras in the visible/near-infrared spectral domain (430-900 nm). The dataset was compiled by putting together measurements from different calibrated hyperspectral imaging cameras and crops to facilitate the training of artificial intelligence models, helping to overcome the generalization problem of hyperspectral models. In particular, this dataset focuses on estimating dry matter content across various crops by a single model in a non-destructive way using hyperspectral measurements. This dataset contains extracted mean reflectance spectra for each sample (n=1028) and their respective dry matter content (%).