This paper has an impressive related work section. It describes work done in image processing related to segmentation and Neural Networks. From the context I believe that the paper only tries to detect one type of flower (The title also points to this). But this is never made explicit inside the paper.
The process they followed was based on flower segmentation. Not really sure how the segmentation was done. The paper does not go to great lengths to describe their procedures. They mention a Otsu’s method to compute the global thresholding after which a morphology process is applied (none of them are described, they are cited). In general, they convert the image to grey-scale, segment the background from the foreground (flower) by thresholding which results in a binary image and finally select the “flower” pixels with the positive part of the binary image.
The feature extraction was as vague. They say that they used two features: Color and Texture. For the colour they say that they use HSV colour space, but don’t specify how the features were calculated. For the texture they state that they used gray-level co-occurance matrix (GLCM). From this they only used the contrast, correlation, energy and homogeneity as features.