Non-destructive identification of single hard seed via multispectral imaging analysis in six legume species
Abstract 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...
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| Format: | Article |
| Language: | English |
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BMC
2020-08-01
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| Series: | Plant Methods |
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| Online Access: | http://link.springer.com/article/10.1186/s13007-020-00659-5 |
| _version_ | 1818260208497983488 |
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| author | Xiaowen Hu Lingjie Yang Zuxin Zhang |
| author_facet | Xiaowen Hu Lingjie Yang Zuxin Zhang |
| author_sort | Xiaowen Hu |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-12T18:27:41Z |
| format | Article |
| id | doaj.art-22e02cdf15a049c586eeab4d38586f96 |
| institution | Directory Open Access Journal |
| issn | 1746-4811 |
| language | English |
| last_indexed | 2024-12-12T18:27:41Z |
| publishDate | 2020-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj.art-22e02cdf15a049c586eeab4d38586f962022-12-22T00:16:00ZengBMCPlant Methods1746-48112020-08-0116111310.1186/s13007-020-00659-5Non-destructive identification of single hard seed via multispectral imaging analysis in six legume speciesXiaowen Hu0Lingjie Yang1Zuxin Zhang2State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou UniversityState Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou UniversityState Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou UniversityAbstract 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.http://link.springer.com/article/10.1186/s13007-020-00659-5Hard seedLegume speciesMultispectral imagingMultivariate analysis |
| spellingShingle | Xiaowen Hu Lingjie Yang Zuxin Zhang Non-destructive identification of single hard seed via multispectral imaging analysis in six legume species Plant Methods Hard seed Legume species Multispectral imaging Multivariate analysis |
| title | Non-destructive identification of single hard seed via multispectral imaging analysis in six legume species |
| title_full | Non-destructive identification of single hard seed via multispectral imaging analysis in six legume species |
| title_fullStr | Non-destructive identification of single hard seed via multispectral imaging analysis in six legume species |
| title_full_unstemmed | Non-destructive identification of single hard seed via multispectral imaging analysis in six legume species |
| title_short | Non-destructive identification of single hard seed via multispectral imaging analysis in six legume species |
| title_sort | non destructive identification of single hard seed via multispectral imaging analysis in six legume species |
| topic | Hard seed Legume species Multispectral imaging Multivariate analysis |
| url | http://link.springer.com/article/10.1186/s13007-020-00659-5 |
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