Automatic grading of apples based on multi-features and weighted K-means clustering algorithm
In this paper, a fast and effective method based on multiple image features and a weighted K-means clustering algorithm is proposed to achieve the automatic grading of apples. The method provides a novel way of using four images (top, bottom and two sides) and average gray values for each apple to d...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-12-01
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| Series: | Information Processing in Agriculture |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214317319300794 |
| _version_ | 1797707257465536512 |
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| author | Yang Yu Sergio A. Velastin Fei Yin |
| author_facet | Yang Yu Sergio A. Velastin Fei Yin |
| author_sort | Yang Yu |
| collection | DOAJ |
| description | In this paper, a fast and effective method based on multiple image features and a weighted K-means clustering algorithm is proposed to achieve the automatic grading of apples. The method provides a novel way of using four images (top, bottom and two sides) and average gray values for each apple to distinguish between the apple defects, stem and calyx. Furthermore, weighted features (MCSAD (maximum cross-sectional average diameter), circularity, PRA (proportion of red area) and defect regions) were carefully selected according to the requirements of the national apple grading standard, which improves the practicality of the proposed method. Finally, qualitative and quantitative evaluation results demonstrate that the total accuracy of the proposed multi-feature grading method is greater than 96%, which provides encouragement for the additional research and implementation of multi-feature automatic grading for the fruit industry. |
| first_indexed | 2024-03-12T06:03:46Z |
| format | Article |
| id | doaj.art-6c50ad0800c04625a3d5db881cb384ab |
| institution | Directory Open Access Journal |
| issn | 2214-3173 |
| language | English |
| last_indexed | 2024-03-12T06:03:46Z |
| publishDate | 2020-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Information Processing in Agriculture |
| spelling | doaj.art-6c50ad0800c04625a3d5db881cb384ab2023-09-03T03:50:46ZengElsevierInformation Processing in Agriculture2214-31732020-12-0174556565Automatic grading of apples based on multi-features and weighted K-means clustering algorithmYang Yu0Sergio A. Velastin1Fei Yin2College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, PR ChinaQueen Mary University of London, Mile End, London E1 4NS, UKCollege of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, PR China; Collaborative Innovation Center of Henan Grain Crops, Zhengzhou, Henan 450002, PR China; Corresponding author at: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, PR China.In this paper, a fast and effective method based on multiple image features and a weighted K-means clustering algorithm is proposed to achieve the automatic grading of apples. The method provides a novel way of using four images (top, bottom and two sides) and average gray values for each apple to distinguish between the apple defects, stem and calyx. Furthermore, weighted features (MCSAD (maximum cross-sectional average diameter), circularity, PRA (proportion of red area) and defect regions) were carefully selected according to the requirements of the national apple grading standard, which improves the practicality of the proposed method. Finally, qualitative and quantitative evaluation results demonstrate that the total accuracy of the proposed multi-feature grading method is greater than 96%, which provides encouragement for the additional research and implementation of multi-feature automatic grading for the fruit industry.http://www.sciencedirect.com/science/article/pii/S2214317319300794Apple gradingMulti-featuresThreshold segmentationK-means |
| spellingShingle | Yang Yu Sergio A. Velastin Fei Yin Automatic grading of apples based on multi-features and weighted K-means clustering algorithm Information Processing in Agriculture Apple grading Multi-features Threshold segmentation K-means |
| title | Automatic grading of apples based on multi-features and weighted K-means clustering algorithm |
| title_full | Automatic grading of apples based on multi-features and weighted K-means clustering algorithm |
| title_fullStr | Automatic grading of apples based on multi-features and weighted K-means clustering algorithm |
| title_full_unstemmed | Automatic grading of apples based on multi-features and weighted K-means clustering algorithm |
| title_short | Automatic grading of apples based on multi-features and weighted K-means clustering algorithm |
| title_sort | automatic grading of apples based on multi features and weighted k means clustering algorithm |
| topic | Apple grading Multi-features Threshold segmentation K-means |
| url | http://www.sciencedirect.com/science/article/pii/S2214317319300794 |
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