IMAGE ANALYSIS OF SOYBEAN LEAF DISEASES USING METADATA

Autores

  • Thiago Evaristo Wenceslau Moreno State University of Ponta Grossa
  • Albino Szesz Junior State University of Ponta Grossa
  • Marcos Monteiro Júnior State University of Ponta Grossa
  • Diulhio Candido de Oliveira State University of Ponta Grossa
  • Maria Salete Marcon Gomes Vaz Universidade Estadual de Ponta Grossa - UEPG
  • Lucélia De Souza State University of Ponta Grossa
  • Tatiana Montes Celinski State University of Ponta Grossa
  • Thalita Scharr Rodrigues State University of West Center

Palavras-chave:

leaf disease, image processing, metadata.

Resumo

The aim of this paper was to create an integrated environment with routines in C Language to image analysis of foliar diseases of soybean and extraction of metadata to describe aspects of the image and also verify if the leaf is sick or not. For this we have defined one standard of metadata, it was utilized the Canny Algorithm to analyze the image and created a prototype interface to see the results. It was possible to detect the edge of images submitted to the algorithm, however there was a necessity to better filtering of noise.

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Publicado

2012-03-09

Edição

v. 1 n. 2 (2011)

Seção

Papers