SpectroFood dataset: A comprehensive fruit and vegetable hyperspectral meta-dataset for dry matter estimation
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 c...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-02-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340924000143 |
| _version_ | 1797317111808262144 |
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| author | Ioannis Malounas Wout Vierbergen Sezer Kutluk Manuela Zude-Sasse Kai Yang Ming Zhao Dimitrios Argyropoulos Jonathan Van Beek Eva Ampe Spyros Fountas |
| author_facet | Ioannis Malounas Wout Vierbergen Sezer Kutluk Manuela Zude-Sasse Kai Yang Ming Zhao Dimitrios Argyropoulos Jonathan Van Beek Eva Ampe Spyros Fountas |
| author_sort | Ioannis Malounas |
| collection | DOAJ |
| description | 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 (%). |
| first_indexed | 2024-03-08T03:30:08Z |
| format | Article |
| id | doaj.art-3748c7c75a8a424aa8cd4230c39bbbd9 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-03-08T03:30:08Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-3748c7c75a8a424aa8cd4230c39bbbd92024-02-11T05:11:03ZengElsevierData in Brief2352-34092024-02-0152110040SpectroFood dataset: A comprehensive fruit and vegetable hyperspectral meta-dataset for dry matter estimationIoannis Malounas0Wout Vierbergen1Sezer Kutluk2Manuela Zude-Sasse3Kai Yang4Ming Zhao5Dimitrios Argyropoulos6Jonathan Van Beek7Eva Ampe8Spyros Fountas9Agricultural University of Athens (AUA), Iera Odos 75, 11855 Athens, Greece; Corresponding author.Technology and Food Science Unit, Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Burgemeester Van Gansberghelaan 115 bus 1, 9820 Merelbeke, BelgiumDepartment of Datascience in Bioeconomy, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469 Potsdam-Bornim, GermanyDepartment of Agromechatronic, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469 Potsdam-Bornim, GermanySchool of Biosystems and Food Engineering, University College Dublin (UCD), Stillorgan Rd, Belfield, Dublin 4, IrelandSchool of Biosystems and Food Engineering, University College Dublin (UCD), Stillorgan Rd, Belfield, Dublin 4, IrelandSchool of Biosystems and Food Engineering, University College Dublin (UCD), Stillorgan Rd, Belfield, Dublin 4, IrelandTechnology and Food Science Unit, Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Burgemeester Van Gansberghelaan 115 bus 1, 9820 Merelbeke, BelgiumINAGRO VZW, Ieperseweg 87, 8800 Rumbeke-Beitem, BelgiumAgricultural University of Athens (AUA), Iera Odos 75, 11855 Athens, GreeceIn 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 (%).http://www.sciencedirect.com/science/article/pii/S2352340924000143Hyperspectral imagingArtificial intelligenceAppleBroccoliLeekMushroom |
| spellingShingle | Ioannis Malounas Wout Vierbergen Sezer Kutluk Manuela Zude-Sasse Kai Yang Ming Zhao Dimitrios Argyropoulos Jonathan Van Beek Eva Ampe Spyros Fountas SpectroFood dataset: A comprehensive fruit and vegetable hyperspectral meta-dataset for dry matter estimation Data in Brief Hyperspectral imaging Artificial intelligence Apple Broccoli Leek Mushroom |
| title | SpectroFood dataset: A comprehensive fruit and vegetable hyperspectral meta-dataset for dry matter estimation |
| title_full | SpectroFood dataset: A comprehensive fruit and vegetable hyperspectral meta-dataset for dry matter estimation |
| title_fullStr | SpectroFood dataset: A comprehensive fruit and vegetable hyperspectral meta-dataset for dry matter estimation |
| title_full_unstemmed | SpectroFood dataset: A comprehensive fruit and vegetable hyperspectral meta-dataset for dry matter estimation |
| title_short | SpectroFood dataset: A comprehensive fruit and vegetable hyperspectral meta-dataset for dry matter estimation |
| title_sort | spectrofood dataset a comprehensive fruit and vegetable hyperspectral meta dataset for dry matter estimation |
| topic | Hyperspectral imaging Artificial intelligence Apple Broccoli Leek Mushroom |
| url | http://www.sciencedirect.com/science/article/pii/S2352340924000143 |
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