The use of computer systems in Precision Agriculture (PA) promotes the processes’ automation and its applied tasks, specifically the inspection and analysis of agricultural crops, and guided/autonomous locomotion of mobile robots. In this context, this research aims the application of computer vision techniques for agricultural mobile robot locomotion, settled through an architecture for the acquisition, image processing and analysis, in order to segment, classify and recognize patterns of planting rows, as guiding references for steering the mobile robot. Also, the process includes: filtering in the spatial domain for acquired images; pre-processing in RGB and HSV color spaces; JSEG unsupervised segmentation algorithm, applied to color quantization in non-homogeneous regions; normalization and histograms feature extraction of preprocessed images for training and test sets, fulfilled by the principal components analysis (PCA); pattern recognition and statistical classification. The developed methodology includes sets of 700 and 900 images’ databases for each approach of natural scenes under different conditions of acquisition, providing great results on the segmentation algorithm. Statistical classification (Bayes/Naive Bayes) was applied, proving the efficiency in recognizing distinct patterns and classes for images in several characteristics inherent to the robot locomotion environment.