Anisotropic diffusion based weed classifier

This paper presents a new approach of anisotropic diffusion to classify the weed images into broad and narrow class for real time selective herbicide application. The classifier we proposed based on Perona and Malik equation. Its low computational complexity and fast runtimes makes this method well suited for real-time vision applications. The developed system has been tested on weeds in the lab; the results show a very reliable performance and drastically less computational effort on images of weeds taken under varying field conditions. The analysis of the results shows over 97.6% classification accuracy over 200 sample images.