{"url":"/dataset/deephs-fruit-v2","name":"DeepHS Fruit v2","full_name":null,"description_markdown":"The data set covers recordings of ripening fruit with labels of destructive measurements (fruit flesh firmness, sugar content and overall ripeness).  The labels are provided within three categories (firmness, sweetness and overall ripeness).\r\nFour measurement series were performed.  Besides 1018 labeled recordings, the data set contains 4671 recordings without ripeness label.  \r\n\r\nThe data set contains recordings of:\r\nAvocados, Kiwis, Persimmons, Papayas, Mango\r\n\r\nThree different hyperspectral cameras were used:\r\nSpecim FX 10,  INNO-SPEC Redeye 1.7, Corning microHSI 410 Vis-NIR Hyperspectral Sensor","description_withheld":null,"homepage":"https://github.com/cogsys-tuebingen/deephs_fruit","introduced_date":"2021-04-20","introduced_date_note":null,"introduced_by":{"paper":"/paper/measuring-the-ripeness-of-fruit-with","title":"Measuring the Ripeness of Fruit with Hyperspectral Imaging and Deep Learning","first_author":"Leon Amadeus Varga","url":null},"license":null,"modalities":[{"name":"Hyperspectral images","url":"/datasets/modality/hyperspectral-images"}],"tasks":[{"name":"Classification","url":"/task/classification-1","datasets_with_task":"/datasets/task/classification-1"},{"name":"Hyperspectral Image-Based Fruit Ripeness Prediction","url":"/task/hyperspectral-image-based-fruit-ripeness","datasets_with_task":"/datasets/task/hyperspectral-image-based-fruit-ripeness"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["DeepHS Fruit v2"],"data_loaders":[{"repo":"https://github.com/cogsys-tuebingen/deephs_fruit","url":"https://github.com/cogsys-tuebingen/deephs_fruit","frameworks":["pytorch"]}],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/hyperspectral-image-based-fruit-ripeness","task":"Hyperspectral Image-Based Fruit Ripeness Prediction","dataset_variant":"DeepHS Fruit v2","rows":3,"metrics":["Overall Classification Accuracy"],"first_row_in_archive_order":{"model":"Fruit-HSNet","paper":"/paper/fruit-hsnet-a-machine-learning-approach-for","metrics":{"Overall Classification Accuracy":"70.73 %"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/fruit-hsnet-a-machine-learning-approach-for","title":"Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction","date":"2025-02-28","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/wavelength-aware-2d-convolutions-for","title":"Wavelength-aware 2D Convolutions for Hyperspectral Imaging","date":"2022-09-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/measuring-the-ripeness-of-fruit-with","title":"Measuring the Ripeness of Fruit with Hyperspectral Imaging and Deep Learning","date":"2021-04-20","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}