The Effect of Color Spaces and Spectra on a Strawberry Prediction Model

Bachelor thesis (2022)

Authors

J.M. Rosenberg Electrical Engineering, Mathematics and Computer Science

Contributors

J. Wen Algorithmics - EEMCS (supervisor 1)
T.E.P.M.F. Abeel Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
A. Nadeem Cyber Security - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Jaron Rosenberg
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Published Date

21-06-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

The purpose of this research is to reduce food waste by monitoring the ripening process of strawberries in order to optimize the harvesting time. To improve the moment of harvest, we need to know the ripeness of a strawberry. Using data from different color ranges and spaces we should be able to predict the ripeness of a strawberry on a 1-10 scale. We want to answer the question whether data from multiple color spaces can improve such a prediction model.

The prediction is performed on strawberry segments, using linear regression. The regression is performed based on the ripeness and the red and green pixel values in a segment. Each color space uses a slightly different metric.

We are able to show that the YCbCr and CIELab color space outperform RGB in such a linear regression. This is likely to come from the fact that these color spaces separate the luminance and chrominance. For the near-infrared range however, we do not have enough data to make such a conclusion as the available data only has ripeness levels 7-9.