Evaluations of fractal geometry and invariant moments for shape classification of corn germplasm

Abstract

Abstract Computer vision-based techniques were developed and evaluated for classifying different shapes of germplasms (ear of corn). An algorithm was developed to discriminate round-shaped germplasms based on two features, i.e. circularity and dimensional ratio. Two different approaches based on fractal geometry and higher order invariant moments were used for classification of non-round shaped germplasms. In the fractal-based approach, two additional fractal geometry-based features (i.e. fractal-shape factor and fractal perimeter) were developed and used with fractal dimension and aspect ratio to represent the shape features of the germplasms. Classifications rules based on modified Euclidean measures and distance weighted K -nearest neighborhood were used for classifying the germplasms into one of three non-round-shape classes (cylindrical, cylindrical-conical and conical). Though the overall correspondence for classifying non-round germplasms was 60% (based on 80 samples), a maximum correspondence of 80% could be obtained for classifying cylindrical germplasms (based on 18 samples). Neither method could provide similar classification correspondence for cylindrical-conical germplasms. On the other hand, these methods, however, showed a correspondence of 82.5% for classifying non-round corn germplasms into cylindrical and non-cylindrical (conical and cylindrical-conical) shapes.