Wavelet-based spectral descriptors for detection of damage in sunflower seeds

Abstract

Analysis of seeds is essential for determining seed lot quality and hence its sowing value. Although not fully objective, assessing seed quality with radiographic images has been used as alternative to standard laboratory testing. Here, we applied 2D scale-mixing non-decimated wavelet transform for automatic processing of radiographic images of sunflower seeds. From the transformed images several spectral indices are derived. These descriptors involve spectral slopes which are directly connected with the degree of image regularity. A methodology paradigm was developed to analyze the images and classify each seed as damaged or undamaged (slight, full). By considering binary and multinomial supervised classification, the rate of correct classification was found to be 82% for damaged and full seeds, and 57% for damaged, slightly damaged, and full seeds. Although in principle many different transforms can serve as a basis in deriving spectral indices, this particular transform proved out to be sensitive to image anisotropy (by its scale-mixing nature), and stable in computation (by its non-decimated nature).