English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Background  Physical dormancy (hard seed) occurs in most species of Leguminosae family and has great consequences not only for ecological adaptation but also for agricultural practice of these species. A rapid, nondestructive and on-site screening method to detect hard seed within species is fundamental important for maintaining seed vigor and germplasm storage as well as understanding seed adaptation to various environment. In this study, the potential of multispectral imaging with object-wise multivariate image analysis was evaluated as a way to identify hard and soft seeds in  Acacia seyal ,  Galega orientulis ,  Glycyrrhiza glabra ,  Medicago sativa ,  Melilotus officinalis , and  Thermopsis lanceolata . Principal component analysis (PCA), linear discrimination analysis (LDA), and support vector machines (SVM) methods were applied to classify hard and soft seeds according to their morphological features and spectral traits.    Results  The performance of discrimination model via multispectral imaging analysis was varied with species. For  M. officinalis ,  M. sativa , and  G. orientulis , an excellent classification could be achieved in an independent validation data set. LDA model had the best calibration and validation abilities with the accuracy up to 90% for  M. sativa . SVM got excellent seed discrimination results with classification accuracy of 91.67% and 87.5% for  M. officinalis  and  G. orientulis , respectively. However, both LDA and SVM model failed to discriminate hard and soft seeds in  A. seyal ,  G. glabra , and  T. lanceolate .    Conclusions  Multispectral imaging together with multivariate analysis could be a promising technique to identify single hard seed in some legume species with high efficiency. More legume species with physical dormancy need to be studied in future research to extend the use of multispectral imaging techniques.

Non-destructive identification of single hard seed via multispectral imaging analysis in six legume species