Algorithms for detecting cherry pits on the basis of transmittance mode hyperspectral data

The suitability of the hyperspectral transmittance imaging technique was assessed in terms of detecting the internal intrusions (pits and their fragments) in cherries. Herein, hyperspectral transmission images were acquired in the visible and near-infrared range (450-1000 nm) from pitted and intact...

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Bibliographic Details
Published in:International Agrophysics 2017-10, Vol.31 (4), p.539-549
Main Authors: Siedliska, Anna, Zubik, Monika, Baranowski, Piotr, Mazurek, Wojciech
Format: Article
Language:English
Subjects:
Back propagation
Cherries
cherry
Classifiers
Cultivars
hyperspectral transmittance
I.R. radiation
Image acquisition
Image transmission
Mathematical models
Neural networks
pit detection
Pits
Predictions
Pretreatment
Principal components analysis
Satellites
soluble solid content
Tissues
Transmittance
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Summary:The suitability of the hyperspectral transmittance imaging technique was assessed in terms of detecting the internal intrusions (pits and their fragments) in cherries. Herein, hyperspectral transmission images were acquired in the visible and near-infrared range (450-1000 nm) from pitted and intact cherries of three popular cultivars: ‘Łutówka’, ‘Pandy 103’, and ‘Groniasta’, differing by soluble solid content. The hyperspectral transmittance data of fresh cherries were used to determine the influence of differing soluble solid content in fruit tissues on pit detection effectiveness. Models for predicting the soluble solid content of cherries were also developed. The principal component analysis and the second derivative pre-treatment of the hyperspectral data were used to construct the supervised classification models. In this study, five classifiers were tested for pit detection. From all the classifiers studied, the best prediction accuracies for the whole pit or pit fragment detection were obtained the backpropagation neural networks model (87.6% of correctly classified instances for the training/test set and 81.4% for the validation set). The accuracy of distinguishing between drilled and intact cherries was close to 96%. These results showed that the hyperspectral transmittance imaging technique is feasible and useful for the non-destructive detection of pits in cherries.
ISSN:2300-8725
0236-8722
2300-8725
DOI:10.1515/intag-2016-0075