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
Affiliations
Ioannis Malounas
Agricultural University of Athens (AUA), Iera Odos 75, 11855 Athens, Greece; Corresponding author.
Wout Vierbergen
Technology and Food Science Unit, Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Burgemeester Van Gansberghelaan 115 bus 1, 9820 Merelbeke, Belgium
Sezer Kutluk
Department of Datascience in Bioeconomy, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469 Potsdam-Bornim, Germany
Manuela Zude-Sasse
Department of Agromechatronic, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469 Potsdam-Bornim, Germany
Kai Yang
School of Biosystems and Food Engineering, University College Dublin (UCD), Stillorgan Rd, Belfield, Dublin 4, Ireland
Ming Zhao
School of Biosystems and Food Engineering, University College Dublin (UCD), Stillorgan Rd, Belfield, Dublin 4, Ireland
Dimitrios Argyropoulos
School of Biosystems and Food Engineering, University College Dublin (UCD), Stillorgan Rd, Belfield, Dublin 4, Ireland
Jonathan Van Beek
Technology and Food Science Unit, Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Burgemeester Van Gansberghelaan 115 bus 1, 9820 Merelbeke, Belgium
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 (%).
