TY - DATA
T1 - Data underlying the research of strawberry quality prediction with infield data
PY - 2025/01/15
AU - Junhan Wen
AU - Camiel Verschoor
AU - Stijn Jochems
AU - Lisanne Schuddebeurs
AU - Vera Theelen
AU - Klaas Walraven
AU - Brigit den Bakker
AU - Joost Scholten
AU - Thomas Abeel
AU - M.M.(Mathijs) de Weerdt
UR - 
DO - 10.4121/96a1cb2d-9f16-470f-8c46-744d5985a140.v1
KW - Machine Learning
KW - Image Processing
KW - Computer Vision
KW - Non-Destructive Analysis
KW - Machine Learning as a Service
KW - In-Field Data
KW - Climate Computer Data
KW - Destructive Measurements
KW - Fruit Quality
KW - Strawberry Quality Attributes
N2 - <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>
ER -