Variation was of root shape in Japanese radish, due to genotypes, soil types and growth stages, were quantitatively evaluated by principal components scores based on elliptic Fourier descriptors. Photographic images of sampled roots on 35mm color reversal films were converted into digital images. After image processing, the contour of each root was expressed as chain-code and then described by 77 coefficients of elliptic Fourier descriptors. After normalization about size, rotation, and starting point of the contour, two groups of the coefficients, which are related to the symmetrical and asymmetrical variations of shape, were analyzed separately, since artificially determined direction of curvature of the root may influence the results. Principal component analysis of the coefficients showed that the major part of the symmetrical (A) and asymmetrical (B) variations were summarized by at most 5 components. The cumulative contribution was 95.2% and 97.1%, respectively. Analysis of variance of each component indicated that the variety effect was highly significant for the 1st, 2nd and 3rd principal components derived from group A coefficients, which were related to the aspect ratio, bluntness of the distal part of the root, and swelling of the middle part, respectively. This suggests that these traits are heritable and can be effectively selected through quantified measures based on elliptic Fourier descriptors presented in this report. Direction and degree of curvature of root could be analyzed independently of the symmetrical variation.
Japanese radish root shape elliptic Fourier descriptors image analysis principal component analysis