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Categorization of Silkworm Based on Chitin Glands using Image Processing
Shreyas S.1, Simhadri Govindappa2, C. G. Raghavendra3, Vinayak Shastri4, Yathin Patil5

1Shreyas S., Department of Electronics and Communication Engineering, Ramaiah Institute of Technology, Bangalore (Karnataka), India.

2Simhadri, Department of Electronics and Communication Engineering, Ramaiah Institute of Technology, Bangalore (Karnataka), India.

3C. G. Raghavendra, Assosciate Professor, Department of E & C, Ramaiah Institute of Technology, Bangalore (Karnataka), India.

4Vinayak Shastri, Department of Electronics and Communication Engineering, Ramaiah Institute of Technology, Bangalore (Karnataka), India.

5Yathin V. Patil, Department of Electronics and Communication Engineering, Ramaiah Institute of Technology, Bangalore (Karnataka), India.

Manuscript received on 08 December 2019 | Revised Manuscript received on 16 December 2019 | Manuscript Published on 31 December 2019 | PP: 638-641 | Volume-9 Issue-2S December 2019 | Retrieval Number: B11021292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1102.1292S19

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This paper demonstrates a prototype for highly accurate identification of the silkworm pupa (Bombyx mori) gender using optical property. The methodology is to optical beam in the near infrared spectrum that can effectively and safely penetrate the body of a silkworm pupa. After the illumination, some of the basic operations of image processing like image thresholding, contour detection, blob filtering and image inversion processes are applied to remove the unwanted image noises and at the same time highlighted the gland that distinguishes the gender in silkworm. The proof of concept is experimentally done using three 633 nm wavelength Light emitting diodes (LED’s), a pi camera, and a computer. Some of the key features of this method include ease of implementation with cost reduction and high accuracy.

Keywords: Chitin Gland, Gaussian Blurring, Thresholding, Pupa.
Scope of the Article: Image Processing and Pattern Recognition