The week's pick
Reseach Article
An Optimized Computer Vision Approach to Precise Well-Bloomed Flower Yielding Prediction using Image Segmentation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 119 - Number 23 |
| Year of Publication: 2015 |
| Authors: Rupinder Kaur, Shrusti Porwal |
10.5120/21376-4038
|
Rupinder Kaur, Shrusti Porwal . An Optimized Computer Vision Approach to Precise Well-Bloomed Flower Yielding Prediction using Image Segmentation. International Journal of Computer Applications. 119, 23 ( June 2015), 15-20. DOI=10.5120/21376-4038
Abstract
The objective of this paper is to explore the various red colored Rose flowers recognition and yielding through prediction precision using segmentation. The yield concludes an excellent impression with the proper information for image prediction precision for a flower evacuation. The main concern is to detect and yield the blossom roses grown in cultivated land is estimated to amount and the terms; conditions, Luminescence and rose species produce flowers without changing any natural abnormality is to confirm. In dynamic technology, efficient cultivation requires a wide usage in yielding process where segmentation carries basic module in image extraction. The current study use the computerization techniques through thresholding to extract flower and Hue's color code Segmentation through Otsu Algorithm along with Morphological Filters to acquire the fine yielding of highly bloomed rose flowers from an digital snapshot . The procedure of recognition carried out for 230 images. This technique approaches a precise the recognizing, yielding and counting of rose flower at about 83. 33% with overall accuracy.
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