ROI extraction of chlorophyll Fluorescence wheat canopy images using novel Curve Fit Based K- means segmentation Algorithm for automatic drought stress detection using machine learning

The data consist of a file system-based data of Raj 3765 variety of wheat. There are twenty-four chlorophyll fluorescence images every day for each (Control and Drought) have been captured for a period of sixty days. A total of (1440 x 2) images are in used for this research work.

Created dataset is subjected to a novel segmentation algorithms called "Cfit k-means " to extract appropriate ROI.
Experimentation include analysis of seven segementation algorithms as follows:
1. Global Static Thresholding
2. Global K-means Thesholding
3. Otsu Thresholding
4. K-means
5. Meanshift
6. Watershed
7. Cfit K-means Segmenetation

Results of the segmentation Process shared below for both control and drought experiments.