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The leaf analysis in a crop can present the need of a nutrient determined in the plant. The macronutri ents deficiency in the cotton crop can be identified by specific type of colors variation by leaves images. Early identification of macronutrients deficiency can hel p in the growing suitable of the crop and reduce th e use of agricultural inputs. This study investigates the image segmentation of the cotton leaves with defic iency of the phosphor. The segmentation is performed by diff erence of leaf pigmentation, according with the pat tern related to macronutrient type in deficit and the cu ltivate. The image segmentation is made by an artif icial neural network and the Otsu method. The results sho w satisfactory values with an optimized artificial neural network and better than the Otsu method. The result s are presented by images and distinct parameters o f quality analysis in the segmentation.

IMAGE SEGMENTATION WITH ARTIFICIAL NEURAL NETWORK FOR NUTRIENT DEFICIENCY IN COTTON CROP