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In this paper, we introduce a novel and efficient image-based weed recognition system for the weed control problem of Broad-leaved Dock (Rumex obtusifolius L.). Our proposed weed recognition system is developed using a framework, that allows the examination of the affects for various image resolutions in detection and recognition accuracy. Moreover, it includes state-of-the-art object/image categorization processes such as feature detection and extraction, codebook learning, feature encoding, image representation and classification. The efficiency of those processes have been improved and optimized by introducing methodologies, techniques and system parameters specially tailored for the goal of weed recognition. Through an exhaustive optimization process, which is presented as our experimental evaluation, we conclude to a weed recognition system that uses an image input resolution of 200 …