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A weed can be thought of as any plant growing in the wrong place at the wrong time and doing more harm than good. Weeds compete with the crop for water, light, nutrients and space, and therefore reduce crop yields and also affect the efficient use of machinery. The most widely used method for weed control is to use agricultural chemicals (herbicides and fertilizer products). This heavy reliance on chemicals raises many environmental and economic concerns, causing many farmers to seek alternatives for weed control in order to reduce chemical use in farming. Since hand labor is costly, an automated weed control system may be economically feasible. A real-time precision automated weed control system could also reduce or eliminate the need for chemicals. In this research, an intelligent real-time automatic weed control system using image processing has been developed to identify and discriminate the weed types namely as narrow and broad. The core component of vision technology is the image processing to recognize type of weeds. Two techniques of image processing, GLCM and FFT have been used and compared to find the best solution of weed recognition for classification. The developed machine vision system consists of a mechanical structure which includes a sprayer, a Logitech web-digital camera, 12v motor coupled with a pump system and a small size CPU as a processor. Offline images and recorded video has been tested to the system and classification result of weed shows the successful rate is above 80%.