%0 Generic
%A Wen, Junhan
%A Verschoor, Camiel
%A Jochems, Stijn
%A Schuddebeurs, Lisanne
%A Theelen, Vera
%A Walraven, Klaas
%A den Bakker, Brigit
%A Scholten, Joost
%A Abeel, Thomas
%A de Weerdt, M.M.(Mathijs)
%D 2025
%T Data underlying the research of strawberry quality prediction with infield data
%U 
%R 10.4121/96a1cb2d-9f16-470f-8c46-744d5985a140.v1
%K Machine Learning
%K Image Processing
%K Computer Vision
%K Non-Destructive Analysis
%K Machine Learning as a Service
%K In-Field Data
%K Climate Computer Data
%K Destructive Measurements
%K Fruit Quality
%K Strawberry Quality Attributes
%X <p>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.</p><p>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.</p><p>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.</p>
%I 4TU.ResearchData