Classification models of bruise and cultivar detection on the basis of hyperspectral imaging data

• Hyperspectral imaging enabled to detect bruise damage and 5 apple cultivars.
• Spectral characteristics of bruised and non-bruised apples varied.
• The performance of 10 supervised classification models was compared.
• CFS algorithm was used to reduce hyperspectral data.

The aim of this paper is to create supervised classification models of bruise detection and cultivar detection for five apple cultivars with the use of hyperspectral imaging system in the VNIR (Visible and Near-Infrared) and SWIR (short wavelength infrared) spectral regions. The Correlation-based Feature Selection (CFS) algorithm and 2nd derivative pre-treatments of the hyperspectral data were used when constructing supervised classification models of bruise and cultivar detection. The best prediction accuracy for the bruise detection models was obtained for the Support Vector Machines (SVM), Simple Logistic (SLOG) and Sequential Minimal Optimization (SMO) classifiers (more than 95% of the success rate for the training/test set and 90% for the validation set). Even higher prediction accuracies were obtained for the cultivar detection models. The percentage of correctly classified instances was very high in these models and ranged from 98.2% to 100% for the training/test set and up to 93% for the validation set. The performance of the studied models was presented as Receiver Operating Characteristics (ROC) for the bruise detection models and confusion matrices for the cultivar classification models.