Next Article in Journal
Preferred Placement and Usability of a Smart Textile System vs. Inertial Measurement Units for Activity Monitoring
Next Article in Special Issue
Machine Learning in Agriculture: A Review
Previous Article in Journal
Analytical Model for the Duty Cycle in Solar-Based EH-WSN for Environmental Monitoring
Previous Article in Special Issue
An Ultra-Wideband Frequency System for Non-Destructive Root Imaging
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(8), 2500; https://doi.org/10.3390/s18082500

Non-Destructive Classification of Diversely Stained Capsicum annuum Seed Specimens of Different Cultivars Using Near-Infrared Imaging Based Optical Intensity Detection

1
Department of Electronics and Communication, Faculty of Engineering, Christ (Deemed to be University), Bangalore 560029, India
2
Kyungpook National University, College of IT Engineering, School of Electronics Engineering, 80, Daehak-ro, Buk-gu, Daegu 41566, Korea
3
Department of Biomedical Engineering, College of Engineering, Kyungil University, 50, Gamasil-gil, Hayang-eup, Gyeongsan-si, Gyeongsangbuk-do 38428, Korea
4
Center of Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
5
School of Applied Biosciences, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, Korea
6
Institute of Biomedical Engineering, Kyungpook National University, 680, Gukchaebosang-ro, Jung-gu, Daegu 41944, Korea
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Received: 23 May 2018 / Revised: 21 July 2018 / Accepted: 27 July 2018 / Published: 1 August 2018
(This article belongs to the Special Issue Sensors in Agriculture 2018)
View Full-Text   |   Download PDF [3662 KB, uploaded 1 August 2018]   |  

Abstract

The non-destructive classification of plant materials using optical inspection techniques has been gaining much recent attention in the field of agriculture research. Among them, a near-infrared (NIR) imaging method called optical coherence tomography (OCT) has become a well-known agricultural inspection tool since the last decade. Here we investigated the non-destructive identification capability of OCT to classify diversely stained (with various staining agents) Capsicum annuum seed specimens of different cultivars. A swept source (SS-OCT) system with a spectral band of 1310 nm was used to image unstained control C. annuum seeds along with diversely stained Capsicum seeds, belonging to different cultivar varieties, such as C. annuum cv. PR Ppareum, C. annuum cv. PR Yeol, and C. annuum cv. Asia Jeombo. The obtained cross-sectional images were further analyzed for the changes in the intensity of back-scattered light (resulting due to dye pigment material and internal morphological variations) using a depth scan profiling technique to identify the difference among each seed category. The graphically acquired depth scan profiling results revealed that the control specimens exhibit less back-scattered light intensity in depth scan profiles when compared to the stained seed specimens. Furthermore, a significant back-scattered light intensity difference among each different cultivar group can be identified as well. Thus, the potential capability of OCT based depth scan profiling technique for non-destructive classification of diversely stained C. annum seed specimens of different cultivars can be sufficiently confirmed through the proposed scheme. Hence, when compared to conventional seed sorting techniques, OCT can offer multipurpose advantages by performing sorting of seeds in respective to the dye staining and provides internal structural images non-destructively. View Full-Text
Keywords: swept-source OCT; Capsicum annuum; dye staining; depth scan analysis swept-source OCT; Capsicum annuum; dye staining; depth scan analysis
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Manattayil, J.K.; Ravichandran, N.K.; Wijesinghe, R.E.; Shirazi, M.F.; Lee, S.-Y.; Kim, P.; Jung, H.-Y.; Jeon, M.; Kim, J. Non-Destructive Classification of Diversely Stained Capsicum annuum Seed Specimens of Different Cultivars Using Near-Infrared Imaging Based Optical Intensity Detection. Sensors 2018, 18, 2500.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top