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Preprint / Version 1

Modeling the Relationship between Root Color, Root Shape, and Weight of Radish using Machine Learning

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DOI:

https://doi.org/10.51094/jxiv.454

Keywords:

Random Forests, Root color, Root shape, Quality Monitoring, Raphanus sativus L. var. sativus

Abstract

The quality of radish depends on the production environment and postharvest management during a supply chain. Quality monitoring is therefore important for post-harvest management in the supply chain. This study aims to estimate the quality of radish non-destructively based on color and shape information using random forests as a tool of predictive data-driven modelling. The explanatory variables, namely color and shape information, were obtained by capturing images of radish under a controlled shooting environment. Color information was converted from RGB to HSL or HSV for minimizing potential effects of light conditions on an object surface. Model performance was assessed using Pearson’s correlation coefficient (COR), Nash-Sutcliffe efficiency (NSE), and root mean squared error (RMSE). Experimental results indicated high model performance, supporting the applicability of nondestructive weight estimation using color and shape information of radish. Among the models using different color components, the HSV model exhibited the best performance in all performance measures in this study (i.e., COR, NSE, and RMSE were 0.889, 0.776, and 1.55, respectively). Since the radish variety in this study was red, the R value was the most important variable among the color information. Partial dependence plots further visualized the relationships between each pair of the color component and the radish weight. Further study is needed for the application of this method for other photographic environments specifically where sunlight is present. Assessment of internal conditions of fresh radish such as vitamin C and anthocyanin could be useful for consumer-oriented quality assessment.

Conflicts of Interest Disclosure

The authors have no financial conflicts of interest disclosed concerning the study.

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Author Biography

Yuto Kamiwaki, United Graduate School of Agricultural Science, Tokyo University of Agriculture and Technology

After graduating from the Department of Electrical Engineering, Salesian Polytechnic, Department of Electrical, Electronics and Information Engineering, Nagaoka University of Technology, and the Graduate School of Food and Agriculture Informatics Engineering Program, Department of Agricultural Engineering, Tokyo University of Agriculture and Technology.
He is currently a third-year Ph.D. student in the Department of Agricultural and Environmental Engineering, Graduate School of Agricultural Engineering, Tokyo University of Agriculture and Technology.

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Submitted: 2023-07-12 08:28:53 UTC

Published: 2023-07-13 06:24:39 UTC

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Agriculture & Food Sciences