Laser-induced backscattering imaging for classification of seeded and seedless watermelons
This paper evaluates the feasibility of laser-induced backscattering imaging for the classification of seeded and seedless watermelons during storage. Backscattering images were obtained from seeded and seedless watermelon samples through a laser diode emitting at 658 nm using a backscattering imagi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2017
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| Online Access: | http://psasir.upm.edu.my/id/eprint/62286/1/Laser-induced%20backscattering%20imaging%20for%20classification%20of%20seeded%20and%20seedless%20watermelons.pdf |
| _version_ | 1825932468612497408 |
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| author | Mohd Ali, Maimunah Hashim, Norhashila Bejo, Siti Khairunniza Shamsudin, Rosnah |
| author_facet | Mohd Ali, Maimunah Hashim, Norhashila Bejo, Siti Khairunniza Shamsudin, Rosnah |
| author_sort | Mohd Ali, Maimunah |
| collection | UPM |
| description | This paper evaluates the feasibility of laser-induced backscattering imaging for the classification of seeded and seedless watermelons during storage. Backscattering images were obtained from seeded and seedless watermelon samples through a laser diode emitting at 658 nm using a backscattering imaging system developed for the purpose. The pre-processed datasets extracted from the backscattering images were analysed using principal component analysis (PCA). The datasets were separated into training (75%) and testing (25%) datasets as the inputs in the classification algorithms. Three multivariate pattern recognition algorithms were used including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and k-nearest neighbour (kNN). The QDA-based algorithms obtained the highest overall average classification accuracies (100%) for both the seeded and seedless watermelons. The LDA and kNN-based algorithms also obtained quite high classification accuracies with all the accuracies above 90%. The laser-induced backscattering imaging technique is potentially useful for classification of seeded and seedless watermelons. |
| first_indexed | 2024-03-06T09:42:30Z |
| format | Article |
| id | upm.eprints-62286 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T09:42:30Z |
| publishDate | 2017 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | upm.eprints-622862019-10-30T06:08:27Z http://psasir.upm.edu.my/id/eprint/62286/ Laser-induced backscattering imaging for classification of seeded and seedless watermelons Mohd Ali, Maimunah Hashim, Norhashila Bejo, Siti Khairunniza Shamsudin, Rosnah This paper evaluates the feasibility of laser-induced backscattering imaging for the classification of seeded and seedless watermelons during storage. Backscattering images were obtained from seeded and seedless watermelon samples through a laser diode emitting at 658 nm using a backscattering imaging system developed for the purpose. The pre-processed datasets extracted from the backscattering images were analysed using principal component analysis (PCA). The datasets were separated into training (75%) and testing (25%) datasets as the inputs in the classification algorithms. Three multivariate pattern recognition algorithms were used including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and k-nearest neighbour (kNN). The QDA-based algorithms obtained the highest overall average classification accuracies (100%) for both the seeded and seedless watermelons. The LDA and kNN-based algorithms also obtained quite high classification accuracies with all the accuracies above 90%. The laser-induced backscattering imaging technique is potentially useful for classification of seeded and seedless watermelons. Elsevier 2017-08 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/62286/1/Laser-induced%20backscattering%20imaging%20for%20classification%20of%20seeded%20and%20seedless%20watermelons.pdf Mohd Ali, Maimunah and Hashim, Norhashila and Bejo, Siti Khairunniza and Shamsudin, Rosnah (2017) Laser-induced backscattering imaging for classification of seeded and seedless watermelons. Computers and Electronics in Agriculture, 140. 311 - 316. ISSN 0168-1699; ESSN: 1872-7107 https://www.sciencedirect.com/science/article/pii/S0168169916309577 10.1016/j.compag.2017.06.010 |
| spellingShingle | Mohd Ali, Maimunah Hashim, Norhashila Bejo, Siti Khairunniza Shamsudin, Rosnah Laser-induced backscattering imaging for classification of seeded and seedless watermelons |
| title | Laser-induced backscattering imaging for classification of seeded and seedless watermelons |
| title_full | Laser-induced backscattering imaging for classification of seeded and seedless watermelons |
| title_fullStr | Laser-induced backscattering imaging for classification of seeded and seedless watermelons |
| title_full_unstemmed | Laser-induced backscattering imaging for classification of seeded and seedless watermelons |
| title_short | Laser-induced backscattering imaging for classification of seeded and seedless watermelons |
| title_sort | laser induced backscattering imaging for classification of seeded and seedless watermelons |
| url | http://psasir.upm.edu.my/id/eprint/62286/1/Laser-induced%20backscattering%20imaging%20for%20classification%20of%20seeded%20and%20seedless%20watermelons.pdf |
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