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Journal of Biosystems Engineering

, Volume 44, Issue 2, pp 95–102 | Cite as

Optimized Multivariate Analysis for the Discrimination of Cucumber Green Mosaic Mottle Virus-Infected Watermelon Seeds Based on Spectral Imaging

  • Youngwook Seo
  • Hoonsoo LeeEmail author
  • Hyung-Jin Bae
  • Eunsoo Park
  • Hyoun-Sub Lim
  • Moon S. Kim
  • Byoung-Kwan ChoEmail author
Original Article

Abstract

This study proposes a nondestructive sorting method based on the short-wave infrared hyperspectral imaging technique (SWIR-HIT) to detect and classify watermelon seeds infected with the cucumber green mosaic mottle virus (CGMMV). Virus-infected watermelon seeds were collected from virus-infected watermelon plants. Five plates each with 81 seeds were scanned. A total of 304 mean reflectance spectra were used to develop and evaluate virus-infected seed classification models with multivariate analysis methods such as partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and least squares support vector machine (LS-SVM). To determine the optimal preprocessing method, three preprocessing methods were employed: multivariate scatter correct (MSC) as well as first- and second-derivative preprocessing with the Savitzky–Golay algorithm. Among these methods, second-derivative preprocessing with the LS-SVM method showed an approximately 75% accuracy with a 0.57 kappa coefficient for all three classification classes (infected, infection suspected, and sound seeds). Binary classification between infected and sound seeds by LS-SVM with second-derivative preprocessing showed an approximately 92% accuracy with a 0.75 kappa coefficient. To improve the classification accuracy, the genetic algorithm was implemented, and 9 bands were selected. The selected wavelengths were applied to develop and compare classification models with full wavelengths. The three-class classification with the selected bands showed an approximately 80% accuracy, whereas binary classification in infected and sound seeds showed a more than 93% accuracy with a 0.78 kappa coefficient. These results indicate that SWIR-HIT is a valuable nondestructive tool for rapidly classifying CGMMV-infected watermelon seeds using LS-SVM with raw spectra.

Keywords

Short-wave infrared hyperspectral imaging Watermelon seed Cucumber green mottle mosaic virus Partial least squares discriminant analysis Support vector machine 

Notes

Funding

This research was supported by the Export Strategy Technology Development Program, Ministry of Agriculture, Food and Rural Affairs (MAFRA), Republic of Korea.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

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Copyright information

© The Korean Society for Agricultural Machinery 2019

Authors and Affiliations

  1. 1.Department of Biosystems Machinery Engineering, College of Agricultural and Life ScienceChungnam National UniversityDaejeonRepublic of Korea
  2. 2.Department of Biosystems Engineering, College of Agriculture, Life & Environment ScienceChungbuk National UniversityCheongjuRepublic of Korea
  3. 3.Department of Applied Biology, College of Agricultural and Life ScienceChungnam National UniversityDaejeonRepublic of Korea
  4. 4.Environmental Microbial and Food Safety Laboratory, Agricultural Research ServiceUnited States Department of AgricultureBeltsvilleUSA

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