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- W2612558730 abstract "Abstract Bruises in apples is one of the most important quality factors during postharvest, which needs to be detected early and efficiently during sorting processes. In this study, a step-wise pixel based apple bruise detection system based on line scan hyperspectral imaging (HSI) in the shortwave infrared (SWIR) is demonstrated for three apple cultivars: ‘Jonagold’, ‘Kanzi’ and ‘Joly Red’. The SWIR HSI system performance was tested on apples from the different cultivars bruised at five different impact levels, and monitored from 1 to 36 h after bruising. While glare regions are commonly considered as anomalies and discarded from further analysis, their spectral signatures enabled in this work to distinguish between cultivars with a prediction accuracy up to 96%. Different partial least squares-discriminant analysis (PLS-DA) models were trained to discriminate cultivars and then to discriminate between sound, bruised, glossy and stem regions. Spectral area normalization pre-processing was found to be the most effective for pixel based bruise prediction, resulting in a prediction accuracy up to 90.1%. Post-processing of the binary images by exploiting spatial information further improved the bruise detection accuracy to 94.4%." @default.
- W2612558730 created "2017-05-19" @default.
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- W2612558730 date "2017-08-01" @default.
- W2612558730 modified "2023-10-01" @default.
- W2612558730 title "Glare based apple sorting and iterative algorithm for bruise region detection using shortwave infrared hyperspectral imaging" @default.
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- W2612558730 doi "https://doi.org/10.1016/j.postharvbio.2017.04.005" @default.
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