Mango (Mangifera indica cv. Sein Ta Lone) ripeness level prediction using color and textural features of combined reflectance-fluorescence images
Sein Ta lone mango of different maturity level images has been obtained using reflectance and fluorescence imaging systems. It has been found that fluorescence images show interesting patterns in correlation with the accumulation of bluish fluorescence compounds in the lenticel spots on the mango su...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | Journal of Agriculture and Food Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666154322002101 |
| _version_ | 1797894668231376896 |
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| author | Dimas Firmanda Al Riza Chen Rulin Naw Thwe Thwe Tun Phyu Phyu Lei Yi Aye Aye Thwe Khin Thida Myint Naoshi Kondo |
| author_facet | Dimas Firmanda Al Riza Chen Rulin Naw Thwe Thwe Tun Phyu Phyu Lei Yi Aye Aye Thwe Khin Thida Myint Naoshi Kondo |
| author_sort | Dimas Firmanda Al Riza |
| collection | DOAJ |
| description | Sein Ta lone mango of different maturity level images has been obtained using reflectance and fluorescence imaging systems. It has been found that fluorescence images show interesting patterns in correlation with the accumulation of bluish fluorescence compounds in the lenticel spots on the mango surface. Color and textural features of both reflectance and fluorescence images have been evaluated to develop a ripeness prediction model. The results show that combining color features of reflectance image and textural features could increase the R2 of the Partial Least Square Regression (PLSR) model up to 0.97 for Brix prediction and 0.99 for pH prediction with Root Mean Square Error (RMSE) of 0.5 for both. These results show the potential of the combined reflectance-fluorescence imaging system for mango ripeness assessment. |
| first_indexed | 2024-04-10T07:14:07Z |
| format | Article |
| id | doaj.art-7418450efd504106a7f217982192340f |
| institution | Directory Open Access Journal |
| issn | 2666-1543 |
| language | English |
| last_indexed | 2024-04-10T07:14:07Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Agriculture and Food Research |
| spelling | doaj.art-7418450efd504106a7f217982192340f2023-02-26T04:28:00ZengElsevierJournal of Agriculture and Food Research2666-15432023-03-0111100477Mango (Mangifera indica cv. Sein Ta Lone) ripeness level prediction using color and textural features of combined reflectance-fluorescence imagesDimas Firmanda Al Riza0Chen Rulin1Naw Thwe Thwe Tun2Phyu Phyu Lei Yi3Aye Aye Thwe4Khin Thida Myint5Naoshi Kondo6Laboratory of Biosensing Engineering, Graduate School of Agriculture, Kyoto University, Kitashirakawa, 6068267, Kyoto, Japan; Department of Biosystems Engineering, Faculty of Agricultural Technology, University of Brawijaya, Jl. Veteran, 65145, Malang, Indonesia; Corresponding author. Laboratory of Biosensing Engineering, Graduate School of Agriculture, Kyoto University, Kitashirakawa, 6068267, Kyoto, Japan.Laboratory of Biosensing Engineering, Graduate School of Agriculture, Kyoto University, Kitashirakawa, 6068267, Kyoto, JapanDepartment of Horticulture, Yezin Agricultural University, 15013, Yezin, Naypyitaw, MyanmarDepartment of Horticulture, Yezin Agricultural University, 15013, Yezin, Naypyitaw, MyanmarDepartment of Horticulture, Yezin Agricultural University, 15013, Yezin, Naypyitaw, MyanmarDepartment of Horticulture, Yezin Agricultural University, 15013, Yezin, Naypyitaw, MyanmarLaboratory of Biosensing Engineering, Graduate School of Agriculture, Kyoto University, Kitashirakawa, 6068267, Kyoto, JapanSein Ta lone mango of different maturity level images has been obtained using reflectance and fluorescence imaging systems. It has been found that fluorescence images show interesting patterns in correlation with the accumulation of bluish fluorescence compounds in the lenticel spots on the mango surface. Color and textural features of both reflectance and fluorescence images have been evaluated to develop a ripeness prediction model. The results show that combining color features of reflectance image and textural features could increase the R2 of the Partial Least Square Regression (PLSR) model up to 0.97 for Brix prediction and 0.99 for pH prediction with Root Mean Square Error (RMSE) of 0.5 for both. These results show the potential of the combined reflectance-fluorescence imaging system for mango ripeness assessment.http://www.sciencedirect.com/science/article/pii/S2666154322002101Machine visionSpectroscopyTextural featuresHaralickRipeness |
| spellingShingle | Dimas Firmanda Al Riza Chen Rulin Naw Thwe Thwe Tun Phyu Phyu Lei Yi Aye Aye Thwe Khin Thida Myint Naoshi Kondo Mango (Mangifera indica cv. Sein Ta Lone) ripeness level prediction using color and textural features of combined reflectance-fluorescence images Journal of Agriculture and Food Research Machine vision Spectroscopy Textural features Haralick Ripeness |
| title | Mango (Mangifera indica cv. Sein Ta Lone) ripeness level prediction using color and textural features of combined reflectance-fluorescence images |
| title_full | Mango (Mangifera indica cv. Sein Ta Lone) ripeness level prediction using color and textural features of combined reflectance-fluorescence images |
| title_fullStr | Mango (Mangifera indica cv. Sein Ta Lone) ripeness level prediction using color and textural features of combined reflectance-fluorescence images |
| title_full_unstemmed | Mango (Mangifera indica cv. Sein Ta Lone) ripeness level prediction using color and textural features of combined reflectance-fluorescence images |
| title_short | Mango (Mangifera indica cv. Sein Ta Lone) ripeness level prediction using color and textural features of combined reflectance-fluorescence images |
| title_sort | mango mangifera indica cv sein ta lone ripeness level prediction using color and textural features of combined reflectance fluorescence images |
| topic | Machine vision Spectroscopy Textural features Haralick Ripeness |
| url | http://www.sciencedirect.com/science/article/pii/S2666154322002101 |
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