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This article presents a framework for characterizing GPS errors typically found in agricultural applications of autonomous vehicle guidance. A fuzzy logic model was proposed to combine sensor data from a differential GPS receiver and a machine vision perception engine. The objective was to correct GPS tracking errors with local positioning information provided by a monocular camera. The fusion philosophy was based on estimating the quality of each sensor output and obtaining the positioning corrections according to a quality-based weight. Field experiments demonstrated the adequacy of using a local positioning sensor to correct a global positioning receivers error through sensor fusion. The fuzzy logic model was implemented to guide a tractor successfully at various speeds and under diverse field and sensor signal quality conditions.