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: 10.4121/96a1cb2d-9f16-470f-8c46-744d5985a140

Wen, Junhan; Camiel Verschoor; Stijn Jochems; Schuddebeurs, Lisanne; Vera Theelen et. al. (2025): Data underlying the research of strawberry quality prediction with infield data. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/96a1cb2d-9f16-470f-8c46-744d5985a140.v1

Dataset

• Artificial Intelligence and Image Processing
• Horticultural Production
• Other Information and Computing Sciences
• Agricultural and Veterinary Sciences
• Information and Communication Services
• Information and Computing Sciences

Climate Computer Data, Computer Vision, Destructive Measurements, Fruit Quality, Image Processing, In-Field Data, Machine Learning, Machine Learning as a Service, Non-Destructive Analysis, Strawberry Quality Attributes

2021.04.19-2024.06.27

CC BY-NC-ND 4.0

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

by Junhan Wen , Camiel Verschoor , Stijn Jochems , Lisanne Schuddebeurs , Vera Theelen , Klaas Walraven , Brigit den Bakker , Joost Scholten , Thomas Abeel , M.M.(Mathijs) de Weerdt

This dataset supports predictive models for assessing strawberry quality using infield monitoring data. It includes physical and biochemical indicators of ripening, as well as subjective and objective supplier criteria for market categorization. Quality attributes are determined through human assessment and device measurements.

Infield data includes RGB and OCN images of strawberries, with larger strawberries segmented using automated and manual methods. Hourly micro-climate data, such as temperature, humidity, CO2_22​ density, illumination, and plant nutrition, is also provided. For on-shelf studies, monitoring images tracking weight loss during storage are available.

The dataset has two subsets: "Individual Quality Evaluations" for measurements linked to visible strawberries in images, and "Aggregated Quality Evaluations" containing all measurements, including those without image links. This resource is ideal for exploring factors affecting strawberry quality.

• 2025-01-15 first online, published, posted

4TU.ResearchData

.csv, .png, .jpg

“How sweet are your strawberries?”: Predicting sugariness using non-destructive and affordable hardware

• https://doi.org/10.4121/21864590

• Topsector Tuinbouw & Uitgangsmaterialen
• Innovatiefonds Hagelunie
• Interpolis

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