A Comparative Analysis of Machine Learning Algorithms for Detection of Organic and Nonorganic Cotton Diseases
Table 12
Advantages and disadvantages of segmentation techniques.
Segmentation techniques
Advantage
Disadvantage
K-means clustering
If a colossal number of pictures are present in the dataset, then k-implies are significant for the division; it places near pixels in one group and assorted ones in the other group
Tedious; we need to choose which cluster gives a better outcome physically
Otsu thresholding
If one or more classes (closer view and establishment) are in the picture, then the Otsu method is fitting; moreover, it is found that Otsu produces better results that appeared differently concerning k-implies gathering for picture division
As a matter of course, the grey thrush capacity of MATLAB takes a limit estimation of 0.5; in any case, this worth may not be ideal for various situations, trouble in choice of edge esteem
Canny and Sobel
This method furnishes finer edge recognition, whereas the Sobel method gives precise corners and edges
For our dataset, canny edge discovery does not discover edges and corners; moreover, Sobel edge recognition does not function admirably when there are dainty and smooth lines in the images in the case of nitrogen deficiency
