Fuzzy excess green (ExG) crisp indices and clustering algorithms such as the Gustafson-Kessel (GK) have been
successfully used for unsupervised classification of hidden and prominent regions of interest (ROI’s), namely green
plants in crop color images against bare clay soil, corn residue and wheat residue, typical of the Great Plains. Each
process can be enhanced with Zadeh (Z) and Gath-Geva (GG) fuzzy enhancement techniques. Enhanced indices and
clusters can be then sorted by final degree of fuzziness, and recombined into labeled, false-color class images, which
can be used as templates for further shape and textural analyses. ROI’s with the lowest degree of fuzziness were
consistently found to be plant clusters according to foveated or prominence of the region size within the image.
Clustering performance according to partition densities and hyper volume was also evaluated. These latter measures can
be used to select the number of clusters and evaluate the computational time needed to find plant ROI’s with complex
backgrounds under different lighting conditions. Enhanced GK clustering methods have performed very well and have
identified plants in bare soil, corn residue plants , and wheat straw plants, well into the high 90 percentages, depending
on plant age category and the relative proportion of plant size within the image. Improved clustering algorithms with
textural finger printing could be potentially useful for unsupervised remote sensing, mapping, crop management, weed,
and pest control for precision agriculture.