Predicting the quality of pumpkin (Cucurbita moschata) during drying using combined computer vision and backscattering and imaging technique
Computer imaging techniques have increasingly been considered as a preferred method of inspecting the quality of fruits and vegetables during postharvest processing. This study investigated the potential of combining RGB based computer imaging and laser light backscattering imaging parameters for pr...
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| Format: | Conference or Workshop Item |
| Language: | English |
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Penerbit MARDI
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/66909/ http://psasir.upm.edu.my/id/eprint/66909/1/NCAFM%20Oral-4.pdf |
| _version_ | 1848855701007368192 |
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| author | Chikwendu, Onwude Daniel Iroemeha Hashim, Norhashila Dana, A. |
| author_facet | Chikwendu, Onwude Daniel Iroemeha Hashim, Norhashila Dana, A. |
| author_sort | Chikwendu, Onwude Daniel Iroemeha |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Computer imaging techniques have increasingly been considered as a preferred method of inspecting the quality of fruits and vegetables during postharvest processing. This study investigated the potential of combining RGB based computer imaging and laser light backscattering imaging parameters for predicting the quality of pumpkin during drying. Sliced pumpkin samples with 4 mm thickness were oven dried at temperatures of 60°C, 70°C and 80°C. A CCD camera and a laser light emitting at wavelength of 648 nm were used to capture images of pumpkin slices after every 1h of drying. Quality properties of moisture content, lightness (L*), redness (a*), and yellowness (b*) color coordinates were measured after every 1h under the same drying conditions, using conventional methods. The results revealed that the combined RGB and backscattering imaging parameters showed strong correlation with the measured quality properties, with R2> 0.9 for all the drying conditions. The finding of this study shows that the combination of RGB and backscattering imaging system has a great potential for monitoring and predicting the quality changes of pumpkin during drying. |
| first_indexed | 2025-11-15T11:29:56Z |
| format | Conference or Workshop Item |
| id | upm-66909 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:29:56Z |
| publishDate | 2018 |
| publisher | Penerbit MARDI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-669092019-03-06T05:26:09Z http://psasir.upm.edu.my/id/eprint/66909/ Predicting the quality of pumpkin (Cucurbita moschata) during drying using combined computer vision and backscattering and imaging technique Chikwendu, Onwude Daniel Iroemeha Hashim, Norhashila Dana, A. Computer imaging techniques have increasingly been considered as a preferred method of inspecting the quality of fruits and vegetables during postharvest processing. This study investigated the potential of combining RGB based computer imaging and laser light backscattering imaging parameters for predicting the quality of pumpkin during drying. Sliced pumpkin samples with 4 mm thickness were oven dried at temperatures of 60°C, 70°C and 80°C. A CCD camera and a laser light emitting at wavelength of 648 nm were used to capture images of pumpkin slices after every 1h of drying. Quality properties of moisture content, lightness (L*), redness (a*), and yellowness (b*) color coordinates were measured after every 1h under the same drying conditions, using conventional methods. The results revealed that the combined RGB and backscattering imaging parameters showed strong correlation with the measured quality properties, with R2> 0.9 for all the drying conditions. The finding of this study shows that the combination of RGB and backscattering imaging system has a great potential for monitoring and predicting the quality changes of pumpkin during drying. Penerbit MARDI 2018 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/66909/1/NCAFM%20Oral-4.pdf Chikwendu, Onwude Daniel Iroemeha and Hashim, Norhashila and Dana, A. (2018) Predicting the quality of pumpkin (Cucurbita moschata) during drying using combined computer vision and backscattering and imaging technique. In: National Conference on Agricultural and Food Mechanization 2018 (NCAFM 2018), 17-19 Apr. 2018, Pullman Kuching, Sarawak. (pp. 100-104). |
| spellingShingle | Chikwendu, Onwude Daniel Iroemeha Hashim, Norhashila Dana, A. Predicting the quality of pumpkin (Cucurbita moschata) during drying using combined computer vision and backscattering and imaging technique |
| title | Predicting the quality of pumpkin (Cucurbita moschata) during drying using combined computer vision and backscattering and imaging technique |
| title_full | Predicting the quality of pumpkin (Cucurbita moschata) during drying using combined computer vision and backscattering and imaging technique |
| title_fullStr | Predicting the quality of pumpkin (Cucurbita moschata) during drying using combined computer vision and backscattering and imaging technique |
| title_full_unstemmed | Predicting the quality of pumpkin (Cucurbita moschata) during drying using combined computer vision and backscattering and imaging technique |
| title_short | Predicting the quality of pumpkin (Cucurbita moschata) during drying using combined computer vision and backscattering and imaging technique |
| title_sort | predicting the quality of pumpkin (cucurbita moschata) during drying using combined computer vision and backscattering and imaging technique |
| url | http://psasir.upm.edu.my/id/eprint/66909/ http://psasir.upm.edu.my/id/eprint/66909/1/NCAFM%20Oral-4.pdf |