Classification of cucumber green mottle mosaic virus (CGMMV) infected watermelon seeds using Raman spectroscopy

Lee, Hoonsoo; Lim, Hyoun–Sub; Cho, Byoung‐Kwan

doi:10.1117/12.2228264

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…In a recent study, a machine vision system was used to detect diseases and insects for the purpose of quality sorting of areca nuts with an accuracy of 90.9% (Huang, 2012). Spectroscopy-based methods have also been used to detect and classify fungus-infected maize (Giacomo and Stefania, 2013), wheat (Soto-Cámara et al ., 2012) and soybeans (Wang et al ., 2004), to determine the percentage of fungal infection in rice (Sirisomboon et al ., 2013) and to identify the green mottle mosaic virus in cucumber (Lee et al ., 2016). However, this technique has yielded unsatisfactory results for fungal infection determination in rice because the moisture and starch contents in rice affect the overall extent of fungal infection (Sirisomboon et al ., 2013).…”

Section: Quality Detection Of Seeds Using Non-destructive Techniquesmentioning

confidence: 99%

Assessment of seed quality using non-destructive measurement techniques: a review

2016

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Seed quality is of great importance in optimizing the cost of crop establishment. Rapid and non-destructive seed quality detection methods must therefore be developed for agriculture and the seed production industry. This review focuses primarily on non-destructive techniques, namely machine vision, spectroscopy, hyperspectral imaging, soft X-ray imaging, thermal imaging and electronic nose techniques, for assessing the quality of agricultural seeds. The fundamentals of these techniques are introduced. Seed quality, including chemical composition, variety identification and classification, insect damage and disease assessment as well as seed viability and germinability of various seeds are discussed. We conclude that non-destructive techniques are accurate detection methods with great potential for seed quality assessment.

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Section: Quality Detection Of Seeds Using Non-destructive Techniquesmentioning

confidence: 99%

Assessment of seed quality using non-destructive measurement techniques: a review

2016

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Raman‐based genetic purity identification of soybean seed production on line

Zhao

Che

et al. 2023

J Raman Spectroscopy

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Raman spectroscopy can identify differences between a variety of seeds, but the difference between the hybrid seed, which is masked by significant signals such as internal components of the seed and external environment disturbance, and its parents is too small, and there is no prominent presentation showing variety differences in the spectral data but only slight changes in the local spectral bands. Therefore, we decomposed soybean spectra based on the empirical mode decomposition method and found that the Hilbert envelope spectrum of the decomposed intrinsic mode components presented local features that the raw spectrum could not detect. In this work, we tested the genetic and physical purity of eight varieties based on the combination method of empirical mode decomposition and Hilbert envelope spectrum transformation. The results demonstrated that the back propagation network identification model based on empirical mode decomposition and Hilbert envelope spectrum transformation had better identification accuracy, precision, recall, and F1 score (94.74, 95.83, 95.83, and 95.83) and the shortest iteration time (56.6 s) than traditional methods. This identification model realized fast and accurate identification of the genetic purity with its parents, as well as the physical purity, which meets the requirements of online production. Consequently, the Hilbert envelope spectrum transform of intrinsic mode components can not only highlight local features but also significantly compress spectral data at different time scales, which provides a promising tool for detecting minor local differences.

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Classification of cucumber green mottle mosaic virus (CGMMV) infected watermelon seeds using Raman spectroscopy

Cited by 2 publications

References 28 publications

Assessment of seed quality using non-destructive measurement techniques: a review

Assessment of seed quality using non-destructive measurement techniques: a review

Raman‐based genetic purity identification of soybean seed production on line

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