Dataset: Sensing Potential in the Food Supply Chain - Mango

DOI:10.4121/fb26fd3f-ba3c-4cf0-8926-14768a256933.v1
The DOI displayed 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/fb26fd3f-ba3c-4cf0-8926-14768a256933

Datacite citation style

Mishra, P.; Offermans, P.; Mensink, M.G.J.; van de Zedde, H.J.; Chauhan, A. et. al. (2025): Dataset: Sensing Potential in the Food Supply Chain - Mango. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/fb26fd3f-ba3c-4cf0-8926-14768a256933.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

The overall aim of this research is to explore the feasibility of sensing technology to measure non-destructively fruit quality properties on a batch and an individual product level.


The specific objective for this dataset was to utilize various sensor data inputs for AI models aimed at quantifying the brix (sugar content) and firmness of mangoes. AI models were developed using data from individual sensors, as well as models based on the fusion of all sensor data. The brix and firmness were modeled using a multi-sensor approach, incorporating ultrasound and spectroscopy signals. Refractometer measurements for brix and puncture measurements for firmness served as the reference standards.

History

  • 2025-03-04 first online, published, posted

Publisher

4TU.ResearchData

Format

.csv, .txt

Organizations

Wageningen Food and Biobased Research, Wageningen University & Research
Wageningen Plant Research, Wageningen University & Research
OnePlanet Research Center

DATA

Files (5)