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The identification and classification of weeds are of major technical and economical importance in the agricultural industry. To automate these activities, like in shape, color and texture, weed control system is feasible. The goal of this paper is to build a real-time, machine vision weed control system that can detect weed locations. The algorithm is developed to classify images into broad and narrow class for real-time selective herbicide application. The developed algorithm based on Edge Link Detector has been tested on weeds at various locations, which have shown that the algorithm to be very effectiveness in weed identification. Further the results show a very reliable performance on weeds under varying field conditions. The analysis of the results shows over 93% classification accuracy over 240 sample images (broad, narrow and no or little weeds) with 100 samples from broad weeds, 100 samples from narrow weeds and the remaining 40 from no or little weeds.