Journal of Spectral Imaging,   Volume 10   Article ID a3   (2021)

Peer reviewed Paper

Estimation of strawberry firmness using hyperspectral imaging: a comparison of regression models

  • Binu Melit Devassy  
  • Sony George
Department of Computer Science, Norwegian University of Science and Technology, Gjøvik 2802, Norway
[email protected]
 https://orcid.org/0000-0001-8436-3164
 Search for papers by this author
 Corresponding Author
Department of Computer Science, Norwegian University of Science and Technology, Gjøvik 2802, Norway
[email protected]
 https://orcid.org/0000-0003-1860-9749
 Search for papers by this author

Firmness is one of the most important quality measures of strawberries, and is related to other aspects of the fruit, such as flavour, ripeness and internal characteristics. The most popular method for measuring firmness is puncturing with a penetrometer, which is destructive and time-consuming. In the present study, we make an attempt to predict the firmness of strawberries in a fast, non-destructive and non-contact way using hyperspectral imaging (HSI) and data analysis with various regression techniques. The primary goal of this research is to investigate and compare the firmness prediction capability of seven prominent regression techniques. We have performed HSI data acquisition of 150 strawberries and optimised seven regression models using the spectral information to predict strawberry firmness. These models are linear, ridge, lasso, k-neighbours, random forest, support vector and partial least square regression. The results show that HSI data with regression models has the potential to predict firmness in a rapid, non-destructive manner. Out of these seven regression models, the k-neighbours regression model outperformed all other methods with a standard error of prediction of 0.14, which is better than that of the state-of-the-art results.

Keywords: hyperspectral imaging, non-destructive firmness measurement, strawberry firmness, regression models

Metrics

Downloads:

237

Full-Text HTML Views:

1

Abstract Views:

1,231