Radon Transform Based Real-Time Weed Classifier

Abstract

A machine vision system to detect and discriminate crop and weed plants in a commercial agricultural environment was developed and tested. Images are acquired in agricultural fields under natural illumination were studied extensively, and a weed classifier based on Radon transform is developed. This classifier is specifically developed to classify images into broad (having broad leaves) and narrow (having narrow leaves) classes for real-time selective herbicide application. The developed system has been tested on weeds in the lab; the results shows reliable performance and significantly less computational efforts on images of weeds taken under varying field conditions. The analysis of the results shows over 93.5% classification accuracy over a database of 200 sample images with 100 samples from each category of weeds.