Abstract
The number of buds is an important index for the classification of cut lily flowers. Since manual counting is time-consuming and laborious, the cut flowers can be easily damaged, and the cut flowers’ quality can be greatly affected. To build an efficient and non-destructive automatic counting of the lily cut flower grading system, we proposed a method for counting lily buds based on machine vision. However, in the images, the color of immature buds, stems, and leaves is similar. The buds are obscured by each other and by leaves, which may affect bud counting accuracy. In this paper, the threshold segmentation of color space transformation is applied to rough segmentation. Then the SVM is used for the second segmentation to extract the complete flower buds. Aiming at the flower buds shaded by each other and by the leaves, the ellipse fitting, and bud counting were was performed by arcs combination and direct least square method in the end. A total of 80 cut lily images (292 flower buds) were counted by the method, and the counting accuracy rate is 81.2% and 91.4% of flower buds were successfully fitted. The fitting accuracy of 91 flower buds was analyzed, and the mean relative errors of the long-axis and the short-axis of the fitting ellipses were less than 5%. When counting an image, the algorithm took a time of 2.371 s. The experimental results show that the proposed algorithm can count and fit flower buds better than other algorithms, which lays a foundation for the automatic classification of cut lily flowers to save labor costs and provides ideas and methods for ellipse fitting of ellipsoid-like objects shaded by each other.