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 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.

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