Co-occurrence patterns based fruit quality detection for hierarchical fruit image annotation
Automatic image annotation is a method of assigning caption to images that provide some convenient way to index, retrieve and handle a large amount of data objects. It focuses on recent agricultural automation applications; it finds potential in classification along with contextual labeling of the i...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-07-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157820305747 |
| _version_ | 1811335021721550848 |
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| author | Sangita B. Nemade Shefali P. Sonavane |
| author_facet | Sangita B. Nemade Shefali P. Sonavane |
| author_sort | Sangita B. Nemade |
| collection | DOAJ |
| description | Automatic image annotation is a method of assigning caption to images that provide some convenient way to index, retrieve and handle a large amount of data objects. It focuses on recent agricultural automation applications; it finds potential in classification along with contextual labeling of the involved objects or detailing based on its statistical properties on fruit categories. However, producing hierarchical labels provide details of a particular fruit subcategory. This paper proposes fruit annotation in a broad sense along with its hierarchical features that can be narrowed down to inherit, further achieving fruit classification into binary or multiple classes indicating subcategories of that fruit. The fruit objects within images are measured to its actual size in the required units. The classification is also used for identifying true color, texture, size, deep features and shape based on the ratio of major to minor axis helpful for fruit gradations. The co-occurrence patterns are obtained based on the visual features of the selected fruit. This is useful for finding the fruit quality categories and combined properties that are used to form the co-occurrence patterns. These patterns are further used by the classifier for fruit annotation. The evaluation of the performance is carried out using the F1 score, accuracy, precision, recall and G-measure. The results show that the co-occurrence pattern with SVM provides an overall accuracy of 97.3% and 97.2% for grape and mango fruit subcategories. The comparative results are obtained to cross-check with the subjective evaluation of gradation validated by local farmers. |
| first_indexed | 2024-04-13T17:17:45Z |
| format | Article |
| id | doaj.art-93eee2ee6eef4dc49bbf97fa1a987c70 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-13T17:17:45Z |
| publishDate | 2022-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-93eee2ee6eef4dc49bbf97fa1a987c702022-12-22T02:38:05ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-07-0134745924606Co-occurrence patterns based fruit quality detection for hierarchical fruit image annotationSangita B. Nemade0Shefali P. Sonavane1Department of Computer Science & Engineering, Walchand College of Engineering, Sangli, Shivaji University Kolhapur, India; Department of Information Technology, Government College of Engineering, Aurangabad; Corresponding author.Department of Information Technology, Walchand College of Engineering, Sangli, Shivaji University Kolhapur, IndiaAutomatic image annotation is a method of assigning caption to images that provide some convenient way to index, retrieve and handle a large amount of data objects. It focuses on recent agricultural automation applications; it finds potential in classification along with contextual labeling of the involved objects or detailing based on its statistical properties on fruit categories. However, producing hierarchical labels provide details of a particular fruit subcategory. This paper proposes fruit annotation in a broad sense along with its hierarchical features that can be narrowed down to inherit, further achieving fruit classification into binary or multiple classes indicating subcategories of that fruit. The fruit objects within images are measured to its actual size in the required units. The classification is also used for identifying true color, texture, size, deep features and shape based on the ratio of major to minor axis helpful for fruit gradations. The co-occurrence patterns are obtained based on the visual features of the selected fruit. This is useful for finding the fruit quality categories and combined properties that are used to form the co-occurrence patterns. These patterns are further used by the classifier for fruit annotation. The evaluation of the performance is carried out using the F1 score, accuracy, precision, recall and G-measure. The results show that the co-occurrence pattern with SVM provides an overall accuracy of 97.3% and 97.2% for grape and mango fruit subcategories. The comparative results are obtained to cross-check with the subjective evaluation of gradation validated by local farmers.http://www.sciencedirect.com/science/article/pii/S1319157820305747Machine learningCo-occurrence patternFruit annotationHierarchical labels |
| spellingShingle | Sangita B. Nemade Shefali P. Sonavane Co-occurrence patterns based fruit quality detection for hierarchical fruit image annotation Journal of King Saud University: Computer and Information Sciences Machine learning Co-occurrence pattern Fruit annotation Hierarchical labels |
| title | Co-occurrence patterns based fruit quality detection for hierarchical fruit image annotation |
| title_full | Co-occurrence patterns based fruit quality detection for hierarchical fruit image annotation |
| title_fullStr | Co-occurrence patterns based fruit quality detection for hierarchical fruit image annotation |
| title_full_unstemmed | Co-occurrence patterns based fruit quality detection for hierarchical fruit image annotation |
| title_short | Co-occurrence patterns based fruit quality detection for hierarchical fruit image annotation |
| title_sort | co occurrence patterns based fruit quality detection for hierarchical fruit image annotation |
| topic | Machine learning Co-occurrence pattern Fruit annotation Hierarchical labels |
| url | http://www.sciencedirect.com/science/article/pii/S1319157820305747 |
| work_keys_str_mv | AT sangitabnemade cooccurrencepatternsbasedfruitqualitydetectionforhierarchicalfruitimageannotation AT shefalipsonavane cooccurrencepatternsbasedfruitqualitydetectionforhierarchicalfruitimageannotation |