%0 Journal Article
	%A R.S.Sabeenian and  V.Palanisamy
	%D 2009
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 28, 2009
	%T Texture Based Weed Detection Using Multi Resolution Combined Statistical and Spatial Frequency (MRCSF)
	%U https://publications.waset.org/pdf/6789
	%V 28
	%X Texture classification is a trendy and a catchy
technology in the field of texture analysis. Textures, the repeated
patterns, have different frequency components along different
orientations. Our work is based on Texture Classification and its
applications. It finds its applications in various fields like Medical
Image Classification, Computer Vision, Remote Sensing,
Agricultural Field, and Textile Industry. Weed control has a major
effect on agriculture. A large amount of herbicide has been used for
controlling weeds in agriculture fields, lawns, golf courses, sport
fields, etc. Random spraying of herbicides does not meet the exact
requirement of the field. Certain areas in field have more weed
patches than estimated. So, we need a visual system that can
discriminate weeds from the field image which will reduce or even
eliminate the amount of herbicide used. This would allow farmers to
not use any herbicides or only apply them where they are needed. A
machine vision precision automated weed control system could
reduce the usage of chemicals in crop fields. In this paper, an
intelligent system for automatic weeding strategy Multi Resolution
Combined Statistical & spatial Frequency is used to discriminate the
weeds from the crops and to classify them as narrow, little and broad
weeds.
	%P 1008 - 1012