Journal
POSTHARVEST BIOLOGY AND TECHNOLOGY
Volume 130, Issue -, Pages 103-115Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.postharvbio.2017.04.005
Keywords
SWIR hyperspectral imaging; Apple bruise detection; Glare; Specular reflection; Fruit sorting; Kanzi; Joly Red; Jonagold
Categories
Funding
- Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT-Flanders) through the Chameleon [SB-100021]
- European Unions Seventh Framework Program for research, technological development and demonstration PicknPack project [311987]
- Flanders Centre of Postharvest Technology (VCBT)
Ask authors/readers for more resources
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%.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available