{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/measuring-the-ripeness-of-fruit-with","title":"Measuring the Ripeness of Fruit with Hyperspectral Imaging and Deep Learning","arxiv_id":"2104.09808","date":"2021-04-20","proceeding":null,"authors":["Leon Amadeus Varga","Jan Makowski","Andreas Zell"],"abstract":"We present a system to measure the ripeness of fruit with a hyperspectral camera and a suitable deep neural network architecture. This architecture did outperform competitive baseline models on the prediction of the ripeness state of fruit. For this, we recorded a data set of ripening avocados and kiwis, which we make public. We also describe the process of data collection in a manner that the adaption for other fruit is easy. The trained network is validated empirically, and we investigate the trained features. Furthermore, a technique is introduced to visualize the ripening process.","url_abs":"https://arxiv.org/abs/2104.09808v1","url_pdf":"https://arxiv.org/pdf/2104.09808v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"measuring-the-ripeness-of-fruit-with","repo_url":"https://github.com/cogsys-tuebingen/deephs_fruit","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"hyperspectral-image-based-fruit-ripeness","task_name":"Hyperspectral Image-Based Fruit Ripeness Prediction"}],"methods":[],"datasets_introduced":[{"slug":"deephs-fruit-v2","name":"DeepHS Fruit v2","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/hyperspectral-image-based-fruit-ripeness","task":"Hyperspectral Image-Based Fruit Ripeness Prediction","dataset":"DeepHS Fruit v2","model":"DeepHS-Net","rank_in_archive_order":2,"of":3,"metrics":{"Overall Classification Accuracy":"58.28 %"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}