The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/21864590

Wen, Junhan; de Weerdt, Mathijs; Thomas Abeel; Camiel Verschoor; LIsanne Schuddebeurs et. al. (2023): Data underlying the research of Quality prediction of strawberries with RGB image segments. Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/21864590.v2

Dataset

x choose version: version 1 - 2023-03-14

• Artificial Intelligence and Image Processing
• Veterinary Sciences
• Agricultural and Veterinary Sciences
• Information and Computing Sciences

Artificial Intelligence and image processing, climate computer data, computer vision technique, crop management, destructive measurements, fruit quality attributes, Machine Learning approach, Machine Learning as a Service, Non-destructive analysis, plant load, Soft fruit

CC BY-NC-ND 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Junhan Wen , Mathijs de Weerdt , Thomas Abeel , Camiel Verschoor , LIsanne Schuddebeurs , Klaas Walraven , Stijn Jochems , Vera Theelen

In this research, we validate our hypothesis of using in-field data that are acquirable via commodity hardware to obtain acceptable accuracies in predicting the quality attribute of strawberries. This dataset consists of images and segments of strawberries in the wild, their quality measurements, and climate data during cultivation.

• 2023-03-14 first online

• 2023-07-14 published, posted

4TU.ResearchData

.jpg, .png, .csv, .xlsx

``How sweet are your strawberries?": predicting sugariness using non-destructive and affordable hardware

• Topsector Tuinbouw & Uitgangsmaterialen

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Algorithmics group