Computer Graphics, Imaging and Visualisation (CGIV 2007) Radon Transform Based Real-Time Weed Classifier Bangkok, Thailand August 14-August 17 ISBN: 0-7695-2928-3
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CGIV.2007.69
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.
Index Terms:
Ecology, Image Processing, Radon Transform, Real-Time Recognition, Weed detection.
Citation:
Muhammad Inam ul Haq, Abdul Muhamin Naeem, Irshad Ahmad, Muhammad Islam, "Radon Transform Based Real-Time Weed Classifier," cgiv, pp.245-249, Computer Graphics, Imaging and Visualisation (CGIV 2007), 2007 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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