Shape-Based Descriptor for Sunn Pest Damaged Wheat Kernel Detection

This study mainly focuses on the effect of sunn pest on wheat. The most prominent feature among the high quality wheat with sun-drenched wheat is the shape differences. For this reason, in this study, a shape recognition method based on the angle information of Fourier transformation is proposed. The performance of the proposed descriptor tested in data sets such as Mpeg-7, leaf, caltech 101, animal. In addition to these datasets, experimental studies were conducted in order to recognize non - classified wheat. Experimental results show that our proposed descriptor provides good accuracies indicating that Fourier Transform based local descriptor captures important characteristics of images that are useful for classification.

• Publication year

  2019

• Venue

  Signal Processing and Communications Applications Conference

• Publication date

  2019-04-01

• Fields of study

  Agricultural and Food Sciences, Computer Science

• Identifiers
  DOI 10.1109/SIU.2019.8806333
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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