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

Published: 5 April 2022| Version 4 | DOI: 10.17632/crb5tkbvpb.4
ankita gupta,


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.



Punjabi University


Image Processing, Segmentation, Water Stress, Whole Wheat Bread