Navigation in crops has been one of the most important challenges of precision agriculture, especially in countries with diverse geographies, where the variety of the terrain is a problem for autonomous devices that can carry out different activities on plants, such as harvesting, fumigation, and supervision. For this reason, a methodology was developed in this paper for the identification of lines in the furrows of potato crops using RGB images taken by a UAV (Unmanned Aerial Vehicle) at different heights. These results will be used as the basis for the furrow modeling and later as a guide in drone navigation and robots set to explore the crops. In this paper, a methodology for processing aerial images taken with a UAV (a drone) was developed to automatically obtain the lines that form the furrows in a potato crop. The results were successful after using indexes adapted over images of potato crops.