Machine vision systems onboard robots have become increasingly important in precision agriculture in order to fully automate some in-field agricultural tasks; like automatic navigation of an agricultural vehicle. In addition, machine vision systems in precision agriculture are also used to gather data from the field in an automated manner at minimal cost; like crop mapping. Several researches have achieved good results performing either crop navigation or crop mapping separately. However, current machine vision systems methods have limitations trying to perform both navigation and mapping processes at the same time. The objective of this research is to develop a machine vision method capable of mapping several crop rows while performing simultaneous navigation with high accuracy. The method is intended for use in automatic guidance systems of agricultural machinery. To achieve this goal, this research implemented a new camera developed by Fujifilm Corporation. This camera can shoot High Definition Wide Angle and Telephoto Images simultaneously. The camera was mounted in the top of a test vehicle, focused on the field surface from an inclined angle in order to obtain Wide Angle Images that cover up to eleven crop rows. At the same time, the camera provides accurate detection of the central crop row using Telephoto Images. The test vehicle used is a Kubota MD-77 conventional tractor, equipped with an RTK-GPS and a Fiber Optic Gyroscope (FOG). An on-board computer process the data from the camera, RTK-GPS and FOG. For accurate crop row detection this research used an image analysis method without segmentation instead of the Hough transformation method in order to reduce the computational burden of the image processing software. Results show that the machine vision method introduced in this research displays increased accuracy and noise reduction for crop row detection used in automatic navigation. In addition, the resulting map covers up to eleven crop rows, compared to other mapping methods that cover up to five crop rows. These results imply that the method is ideal for practical applications like spraying, avoiding to travel additional paths along the field in order to build a crop map; while providing crop row detection with increased accuracy.