Detection of early bruises in apples using hyperspectral data and thermal imaging

► Detection of early bruises in apples of five cultivars. ► Hyperspectral and thermal imaging of bruises in apples. ► Supervised classification of bruises with the use of LDA, SVM and SIMCA. The early detection of bruises in apples was studied using a system that included hyperspectral cameras equipped with sensors working in the visible and near-infrared (400–1000nm), short wavelength infrared (1000–2500nm) and thermal imaging camera in mid-wavelength infrared (3500–5000nm) ranges. The principal components analysis (PCA) and minimum noise fraction (MNF) analyses of the images that were captured in particular ranges made it possible to distinguish between areas with defects in the tissue and the sound ones. The fast Fourier analysis of the image sequences after pulse heating of the fruit surface provided additional information not only about the position of the area of damaged tissue but also about its depth. The comparison of the results obtained with supervised classification methods, including soft independent modelling of class analogy (SIMCA), linear discriminant analysis (LDA) and support vector machines (SVM) confirmed that broad spectrum range (400–5000nm) of fruit surface imaging can improve the detection of early bruises with varying depths.