Ropelewska, E., Rady, A.M. and Watson, N.J. orcid.org/0000-0001-5216-4873 (2023) Apricot Stone Classification Using Image Analysis and Machine Learning. Sustainability, 15 (12). 9259. ISSN 2071-1050
Abstract
Apricot stones have high commercial value and can be used for manufacturing functional foods, cosmetic products, active carbon, and biodiesel. The optimal processing of the stones is dependent on the cultivar and there is a need for methods to sort among different cultivars (which are often mixed in processing facilities). This study investigates the effectiveness of two low-cost colour imaging systems coupled with supervised learning to develop classification models to determine the cultivar of different stones. Apricot stones of the cultivars ‘Bella’, ‘Early Orange’, ‘Harcot’, ‘Skierniewicka Słodka’, and ‘Taja’ were used. The RGB images were acquired using a flatbed scanner or a digital camera; and 2172 image texture features were extracted within the R, G, B; L, a, b; X, Y, Z; U, and V colour coordinates. The most influential features were determined and resulted in 103 and 89 selected features for the digital camera and the flatbed scanner, respectively. Linear and nonlinear classifiers were applied including Linear Discriminant Analysis (LDA), Decision Trees (DT), k-Nearest Neighbour (kNN), Support Vector Machines (SVM), and Naive Bayes (NB). The models resulting from the flatbed scanner and using selected features achieved an accuracy of 100% via either quadratic diagonal LDA or kNN classifiers. The models developed using images from the digital camera and all or selected features had an accuracy of up to 96.77% using the SVM classifier. This study presents novel and simple-to-implement at-line (flatbed scanner) and online (digital camera) methodologies for apricot stone sorting. The developed procedure combining colour imaging and machine learning may be used for the authentication of apricot stone cultivars and quality evaluation of apricot from sustainable production.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 by the authors. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | apricot stone sorting; flatbed scanner; digital camera; classification models; machine learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Food Science and Nutrition (Leeds) > FSN Nutrition and Public Health (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 10 Jul 2024 16:27 |
Last Modified: | 10 Jul 2024 16:27 |
Status: | Published |
Publisher: | MDPI |
Identification Number: | 10.3390/su15129259 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:214605 |