Weed Classification Using Angular Cross Sectional Intensities for Real-Time Selective Herbicide Applications

The environmental impact of herbicide utilization has stimulated research into new methods of weed control, such as selective herbicide application on highly infested crop areas. This paper deals with the development of an algorithm which calculates angular cross sectional intensity of an image that is used for the weed classification. This algorithm is specifically developed to classify images into broad and narrow class for real-time selective herbicide application. The developed system has been tested on weeds in the lab, which have shown that the system to be very effective in weed identification especially to reduce the air and light effects of natural open air environments. Further the results show a very reliable performance on images of weeds taken under varying field conditions. The analysis of the results shows over 97% classification accuracy over 140 sample images (broad and narrow) with 70 samples from each category of weeds