Computer Vision Classification of Barley Flour Based on Spatial Pyramid Partition Ensemble
Imaging sensors are largely employed in the food processing industry for quality control. Flour from malting barley varieties is a valuable ingredient in the food industry, but its use is restricted due to quality aspects such as color variations and the presence of husk fragments. On the other hand...
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MDPI AG
2019-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/13/2953 |
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| author | Jessica Fernandes Lopes Leniza Ludwig Douglas Fernandes Barbin Maria Victória Eiras Grossmann Sylvio Barbon |
| author_facet | Jessica Fernandes Lopes Leniza Ludwig Douglas Fernandes Barbin Maria Victória Eiras Grossmann Sylvio Barbon |
| author_sort | Jessica Fernandes Lopes |
| collection | DOAJ |
| description | Imaging sensors are largely employed in the food processing industry for quality control. Flour from malting barley varieties is a valuable ingredient in the food industry, but its use is restricted due to quality aspects such as color variations and the presence of husk fragments. On the other hand, naked varieties present superior quality with better visual appearance and nutritional composition for human consumption. Computer Vision Systems (CVS) can provide an automatic and precise classification of samples, but identification of grain and flour characteristics require more specialized methods. In this paper, we propose CVS combined with the Spatial Pyramid Partition ensemble (SPPe) technique to distinguish between naked and malting types of twenty-two flour varieties using image features and machine learning. SPPe leverages the analysis of patterns from different spatial regions, providing more reliable classification. Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), J48 decision tree, and Random Forest (RF) were compared for samples’ classification. Machine learning algorithms embedded in the CVS were induced based on 55 image features. The results ranged from 75.00% (k-NN) to 100.00% (J48) accuracy, showing that sample assessment by CVS with SPPe was highly accurate, representing a potential technique for automatic barley flour classification. |
| first_indexed | 2024-04-11T14:12:12Z |
| format | Article |
| id | doaj.art-1b359914a56a41c6af41efc279d030a4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T14:12:12Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
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| spelling | doaj.art-1b359914a56a41c6af41efc279d030a42022-12-22T04:19:41ZengMDPI AGSensors1424-82202019-07-011913295310.3390/s19132953s19132953Computer Vision Classification of Barley Flour Based on Spatial Pyramid Partition EnsembleJessica Fernandes Lopes0Leniza Ludwig1Douglas Fernandes Barbin2Maria Victória Eiras Grossmann3Sylvio Barbon4Department of Computer Science, Londrina State University (UEL), Londrina 86057-970, BrazilDepartment of Food Sciences, Londrina State University (UEL), Londrina 86057-970, BrazilDepartment of Food Engineering, University of Campinas, Campinas 13083-970, BrazilDepartment of Food Sciences, Londrina State University (UEL), Londrina 86057-970, BrazilDepartment of Computer Science, Londrina State University (UEL), Londrina 86057-970, BrazilImaging sensors are largely employed in the food processing industry for quality control. Flour from malting barley varieties is a valuable ingredient in the food industry, but its use is restricted due to quality aspects such as color variations and the presence of husk fragments. On the other hand, naked varieties present superior quality with better visual appearance and nutritional composition for human consumption. Computer Vision Systems (CVS) can provide an automatic and precise classification of samples, but identification of grain and flour characteristics require more specialized methods. In this paper, we propose CVS combined with the Spatial Pyramid Partition ensemble (SPPe) technique to distinguish between naked and malting types of twenty-two flour varieties using image features and machine learning. SPPe leverages the analysis of patterns from different spatial regions, providing more reliable classification. Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), J48 decision tree, and Random Forest (RF) were compared for samples’ classification. Machine learning algorithms embedded in the CVS were induced based on 55 image features. The results ranged from 75.00% (k-NN) to 100.00% (J48) accuracy, showing that sample assessment by CVS with SPPe was highly accurate, representing a potential technique for automatic barley flour classification.https://www.mdpi.com/1424-8220/19/13/2953machine learningimage processingfood qualitycomputer intelligence |
| spellingShingle | Jessica Fernandes Lopes Leniza Ludwig Douglas Fernandes Barbin Maria Victória Eiras Grossmann Sylvio Barbon Computer Vision Classification of Barley Flour Based on Spatial Pyramid Partition Ensemble Sensors machine learning image processing food quality computer intelligence |
| title | Computer Vision Classification of Barley Flour Based on Spatial Pyramid Partition Ensemble |
| title_full | Computer Vision Classification of Barley Flour Based on Spatial Pyramid Partition Ensemble |
| title_fullStr | Computer Vision Classification of Barley Flour Based on Spatial Pyramid Partition Ensemble |
| title_full_unstemmed | Computer Vision Classification of Barley Flour Based on Spatial Pyramid Partition Ensemble |
| title_short | Computer Vision Classification of Barley Flour Based on Spatial Pyramid Partition Ensemble |
| title_sort | computer vision classification of barley flour based on spatial pyramid partition ensemble |
| topic | machine learning image processing food quality computer intelligence |
| url | https://www.mdpi.com/1424-8220/19/13/2953 |
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