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In this approach, cognitive and statistical classifiers were implemented in order to verify the estimated and chosen regions on unstructured environments images. As inspection of crops for natural scenes demands and requires complex analysis of image processing and segmen‐ tation algorithms, since these computational methods evaluate and predict environment physical characteristics, such as color elements, complex objects composition, shadows, brightness and inhomogeneous region colors for texture, JSEG segmentation algorithm was approached to segment these ones, and ANN and Bayes recognition models to classify im‐ ages into predetermined classes (e.g. fruits, plants and general crops). The intended ap‐ proach to segment classification deploys a customized MLP topology to classify and characterize the segments, which deals with a supervised learning by error correction – propagation of pattern inputs with changes in synaptic weights in a cyclic processing, with accurate recognition as well as easy parameter adjustment, as an enhancement of iRPROP algorithm (improved resilient back-propagation) (Igel and Husken, 2003) derived from Backpropagation algorithm, which has a faster identification mapping process, that verifies what region maps have similar matches through the explored environment. Bayes statistical mod‐ els had the addiction of process variable as set parameters of predictive error correction.

Cognitive and Statistical Pattern Recognition Applied in Color and Texture Segmentation for Natural Scenes