Varietal Discrimination of Guava (Psidium Guajava) Leaves Using Multi Features Analysis
ABSTRACTThe purpose of this study was to examine the potential of Machine Vision (MV) approaches for the classification and identification of 12 varieties of guava. There are leaf images of the 12 local varieties of guava (Psidium guajava) that include Bangkok Red, China Surahi, Moti Surahi, Choti S...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-09-01
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| Series: | International Journal of Food Properties |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10942912.2022.2158863 |
| _version_ | 1797202466365767680 |
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| author | Muhammad Asim Saleem Ullah Abdul Razzaq Salman Qadri |
| author_facet | Muhammad Asim Saleem Ullah Abdul Razzaq Salman Qadri |
| author_sort | Muhammad Asim |
| collection | DOAJ |
| description | ABSTRACTThe purpose of this study was to examine the potential of Machine Vision (MV) approaches for the classification and identification of 12 varieties of guava. There are leaf images of the 12 local varieties of guava (Psidium guajava) that include Bangkok Red, China Surahi, Moti Surahi, Choti Surahi, Golden Gola, China Gola, Multani Sada Gola, Sadda Bahar Gola, Larkana Surahi, Black Guava, Hyderabadi Safeeda, Strawberry Pink Gola. A digital camera captured these images of guava varieties in a natural environment. Multi-features were extracted from these images. It was a composite of histograms, binary features, textures, rotational, spectral, and translational features (RST). Total 47 multi-features were collected for each non-overlapping guava leaf image, i.e., [Formula: see text] and [Formula: see text] more, the supervised correlation-based feature selection (CFS) method with the best search algorithm was used to optimize 18 features instead of 47 multi-features. Several ML classifiers, including Instant base Identifier (IBI), Random Forest (RF), and Meta Bagging (MB), using 10-fold cross-validation, were applied to the optimized multi-features. IBI results performed better than other classifiers with an average overall accuracy of 93.01% on AOIs,[Formula: see text]. In addition, IBI detected 90.5%, 89.5%, 94%, 97%, 95.5%, 97%, 99%, 96.5%, 99%, 80.5%, 88%, and 81.5% accuracy values for the 12 varieties of guava leaves, namely Bangkok Red, China Surahi, Moti Surahi, Choti Surahi, Golden Gola, China Gola, Multani Sada Gola, Sadda Bahar Gola, Larkana Surahi, Black Guava, Hyderabadi Safeeda, Strawberry Pink Gola. The proposed study could play a significant role for the early and accurate identification of Guava varieties, and it would also be helpful for export quality measures for the national economy of the country. |
| first_indexed | 2024-03-11T18:20:10Z |
| format | Article |
| id | doaj.art-76f37d4e45ab49a08a6f06ffe652a05e |
| institution | Directory Open Access Journal |
| issn | 1094-2912 1532-2386 |
| language | English |
| last_indexed | 2024-04-24T08:03:53Z |
| publishDate | 2023-09-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Food Properties |
| spelling | doaj.art-76f37d4e45ab49a08a6f06ffe652a05e2024-04-17T13:20:13ZengTaylor & Francis GroupInternational Journal of Food Properties1094-29121532-23862023-09-0126117919610.1080/10942912.2022.2158863Varietal Discrimination of Guava (Psidium Guajava) Leaves Using Multi Features AnalysisMuhammad Asim0Saleem Ullah1Abdul Razzaq2Salman Qadri3Department of Computer Science, Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, PakistanDepartment of Computer Science, Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, PakistanDepartment of Computer Science, MNS University of Agriculture, Multan, PakistanDepartment of Computer Science, MNS University of Agriculture, Multan, PakistanABSTRACTThe purpose of this study was to examine the potential of Machine Vision (MV) approaches for the classification and identification of 12 varieties of guava. There are leaf images of the 12 local varieties of guava (Psidium guajava) that include Bangkok Red, China Surahi, Moti Surahi, Choti Surahi, Golden Gola, China Gola, Multani Sada Gola, Sadda Bahar Gola, Larkana Surahi, Black Guava, Hyderabadi Safeeda, Strawberry Pink Gola. A digital camera captured these images of guava varieties in a natural environment. Multi-features were extracted from these images. It was a composite of histograms, binary features, textures, rotational, spectral, and translational features (RST). Total 47 multi-features were collected for each non-overlapping guava leaf image, i.e., [Formula: see text] and [Formula: see text] more, the supervised correlation-based feature selection (CFS) method with the best search algorithm was used to optimize 18 features instead of 47 multi-features. Several ML classifiers, including Instant base Identifier (IBI), Random Forest (RF), and Meta Bagging (MB), using 10-fold cross-validation, were applied to the optimized multi-features. IBI results performed better than other classifiers with an average overall accuracy of 93.01% on AOIs,[Formula: see text]. In addition, IBI detected 90.5%, 89.5%, 94%, 97%, 95.5%, 97%, 99%, 96.5%, 99%, 80.5%, 88%, and 81.5% accuracy values for the 12 varieties of guava leaves, namely Bangkok Red, China Surahi, Moti Surahi, Choti Surahi, Golden Gola, China Gola, Multani Sada Gola, Sadda Bahar Gola, Larkana Surahi, Black Guava, Hyderabadi Safeeda, Strawberry Pink Gola. The proposed study could play a significant role for the early and accurate identification of Guava varieties, and it would also be helpful for export quality measures for the national economy of the country.https://www.tandfonline.com/doi/10.1080/10942912.2022.2158863Computer visionIBIGuavaPre-processingClassificationMachine learning |
| spellingShingle | Muhammad Asim Saleem Ullah Abdul Razzaq Salman Qadri Varietal Discrimination of Guava (Psidium Guajava) Leaves Using Multi Features Analysis International Journal of Food Properties Computer vision IBI Guava Pre-processing Classification Machine learning |
| title | Varietal Discrimination of Guava (Psidium Guajava) Leaves Using Multi Features Analysis |
| title_full | Varietal Discrimination of Guava (Psidium Guajava) Leaves Using Multi Features Analysis |
| title_fullStr | Varietal Discrimination of Guava (Psidium Guajava) Leaves Using Multi Features Analysis |
| title_full_unstemmed | Varietal Discrimination of Guava (Psidium Guajava) Leaves Using Multi Features Analysis |
| title_short | Varietal Discrimination of Guava (Psidium Guajava) Leaves Using Multi Features Analysis |
| title_sort | varietal discrimination of guava psidium guajava leaves using multi features analysis |
| topic | Computer vision IBI Guava Pre-processing Classification Machine learning |
| url | https://www.tandfonline.com/doi/10.1080/10942912.2022.2158863 |
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