Research Article Open Access

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

Maicon A. Sartin1, Alexandre C.R. Da Silva1 and Claudinei Kappes1
  • 1 , Brazil

Abstract

The leaf analysis in a crop can present the need of a nutrient determined in the plant. The macronutrients deficiency in the cotton crop can be identified by specific type of colors variation by leaves images. Early identification of macronutrients deficiency can help in the growing suitable of the crop and reduce the use of agricultural inputs. This study investigates the image segmentation of the cotton leaves with deficiency of the phosphor. The segmentation is performed by difference of leaf pigmentation, according with the pattern related to macronutrient type in deficit and the cultivate. The image segmentation is made by an artificial neural network and the Otsu method. The results show satisfactory values with an optimized artificial neural network and better than the Otsu method. The results are presented by images and distinct parameters of quality analysis in the segmentation.

Journal of Computer Science
Volume 10 No. 6, 2014, 1084-1093

DOI: https://doi.org/10.3844/jcssp.2014.1084.1093

Submitted On: 4 January 2014 Published On: 4 February 2014

How to Cite: Sartin, M. A., Silva, A. C. D. & Kappes, C. (2014). IMAGE SEGMENTATION WITH ARTIFICIAL NEURAL NETWORK FOR NUTRIENT DEFICIENCY IN COTTON CROP. Journal of Computer Science, 10(6), 1084-1093. https://doi.org/10.3844/jcssp.2014.1084.1093

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Keywords

  • Image Segmentation
  • Artificial Neural Network
  • Otsu Method
  • Precision Agriculture
  • Cotton