Computer image analysis and classification of giant ragweed seeds

Abstract

Giant ragweed exhibits a high degree of polymorphism among individual plants in seed size, shape, spininess, and color. These features may play an important role in giant ragweed seed survival and predation avoidance; however, they are difficult to evaluate because of lack of quantification methods. A computer imaging technique was developed for describing and classifying giant ragweed seeds using digital images of the seed top and side views. Seed samples collected from 20 different giant ragweed plants (classes) were mounted and digitally scanned. Quantitative features were extracted from the seed images, including color, width, height, area, and seed perimeter. A polygon (convex hull) of the seed image based on the seed outline was constructed, from which spininess indices were developed. Fisher's linear discriminant with normalized nearest neighbor classification was used to classify randomly selected images of individual seeds according to class (maternal origin), using the extracted features as a database. The best classification rate achieved was 99%, with 138 out of 140 seeds correctly matched using data from both the top and side views. Seed features were easily extracted and varied from 1.2- to 4.5-fold among classes. Area and perimeter measurements varied least within classes but varied most among classes, suggesting that these features discriminate effectively among seeds from different plants in giant ragweed. Convex hull area : seed area ratio, using the seed top view images, was the best index of seed spininess, aligning well with visual assessment and providing greatest discrimination among classes. This experiment shows that in the case of giant ragweed, seeds from different plants are distinguishable in an objective and quantitative manner. This imaging technique can be applied to identification of seeds from different species and to studies on variable seed morphology within a species.