Glare based apple sorting and iterative algorithm for bruise region detection using shortwave infrared hyperspectral imaging

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%.