  Precision agriculture is area with lack of cheap technology. The refinement
of the production system brings large advantages to the producer and the use of
images makes the monitoring a more cheap methodology. Macronutrients monitoring
can to determine the health and vulnerability of the plant in specific stages.
In this paper is analyzed the method based on computational intelligence to
work with image segmentation in the identification of symptoms of plant
nutrient deficiency. Artificial neural networks are evaluated for image
segmentation and filtering, several variations of parameters and insertion
impulsive noise were evaluated too. Satisfactory results are achieved with
artificial neural for segmentation same with high noise levels.
