Wheat Seed Phenotype Detection Device and Its Application
To address the problem of low efficiency and automatically sense the phenotypic characteristics of wheat seeds, a wheat seed phenotype detection device was designed to predict thousand seed weight. Five commonly used varieties of wheat seeds were selected for the study, and a wheat seed phenotype de...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/13/3/706 |
| _version_ | 1797614073687310336 |
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| author | Haolei Zhang Jiangtao Ji Hao Ma Hao Guo Nan Liu Hongwei Cui |
| author_facet | Haolei Zhang Jiangtao Ji Hao Ma Hao Guo Nan Liu Hongwei Cui |
| author_sort | Haolei Zhang |
| collection | DOAJ |
| description | To address the problem of low efficiency and automatically sense the phenotypic characteristics of wheat seeds, a wheat seed phenotype detection device was designed to predict thousand seed weight. Five commonly used varieties of wheat seeds were selected for the study, and a wheat seed phenotype detection system was built with a 2 mm sampling hole plate. Grayscale, image segmentation, area filtering and other methods were used to process the image in order to extract and analyse the correlation between thousand seed weight and seven phenotypic characteristics: wheat seed area, perimeter, long axis, short axis, ellipticity, rectangularity, and elongation. The results showed that different varieties of wheat seeds were significantly correlated with different phenotypic characteristics. Among them, the area and short axis for Luomai 26; the area, long axis, short axis, perimeter, and rectangularity for Jinqiang 11; the area and perimeter for Zhoumai 22; the area of Luomai 42; the area, short axis, and perimeter for Bainong 207 were significantly correlated with the thousand seed weight. A multiple linear regression model of thousand seed weight prediction was developed by selecting the significantly correlated phenotypic characteristic. The models showed that the <i>R</i><sup>2</sup> values of the thousand seed weight prediction models for Jinqiang 11 and Bainong 207 were 0.853 and 0.757, respectively; and the <i>R</i><sup>2</sup> values for Luomai 26, Zhoumai 22, and Luomai 42 were less than 0.5. Subsequently, PCA-MLR was used to build a thousand seed weight prediction model, and K-fold cross-validation was used for comparative analysis. Afterwards, three kinds of wheat seeds with 40–50 g thousand seed weight were selected to validate the model. The validation results showed that the more significantly correlated the phenotypic parameters were, the higher the accuracy of the thousand seed weight prediction model. The study provided a set of detection devices and methods for the rapid acquisition of the phenotypic characteristics of wheat seeds and thousand seed weight prediction. |
| first_indexed | 2024-03-11T07:04:20Z |
| format | Article |
| id | doaj.art-6eefcfd92b5d4968b97aa4b7d76d9318 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-11T07:04:20Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-6eefcfd92b5d4968b97aa4b7d76d93182023-11-17T09:02:22ZengMDPI AGAgriculture2077-04722023-03-0113370610.3390/agriculture13030706Wheat Seed Phenotype Detection Device and Its ApplicationHaolei Zhang0Jiangtao Ji1Hao Ma2Hao Guo3Nan Liu4Hongwei Cui5College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, ChinaTo address the problem of low efficiency and automatically sense the phenotypic characteristics of wheat seeds, a wheat seed phenotype detection device was designed to predict thousand seed weight. Five commonly used varieties of wheat seeds were selected for the study, and a wheat seed phenotype detection system was built with a 2 mm sampling hole plate. Grayscale, image segmentation, area filtering and other methods were used to process the image in order to extract and analyse the correlation between thousand seed weight and seven phenotypic characteristics: wheat seed area, perimeter, long axis, short axis, ellipticity, rectangularity, and elongation. The results showed that different varieties of wheat seeds were significantly correlated with different phenotypic characteristics. Among them, the area and short axis for Luomai 26; the area, long axis, short axis, perimeter, and rectangularity for Jinqiang 11; the area and perimeter for Zhoumai 22; the area of Luomai 42; the area, short axis, and perimeter for Bainong 207 were significantly correlated with the thousand seed weight. A multiple linear regression model of thousand seed weight prediction was developed by selecting the significantly correlated phenotypic characteristic. The models showed that the <i>R</i><sup>2</sup> values of the thousand seed weight prediction models for Jinqiang 11 and Bainong 207 were 0.853 and 0.757, respectively; and the <i>R</i><sup>2</sup> values for Luomai 26, Zhoumai 22, and Luomai 42 were less than 0.5. Subsequently, PCA-MLR was used to build a thousand seed weight prediction model, and K-fold cross-validation was used for comparative analysis. Afterwards, three kinds of wheat seeds with 40–50 g thousand seed weight were selected to validate the model. The validation results showed that the more significantly correlated the phenotypic parameters were, the higher the accuracy of the thousand seed weight prediction model. The study provided a set of detection devices and methods for the rapid acquisition of the phenotypic characteristics of wheat seeds and thousand seed weight prediction.https://www.mdpi.com/2077-0472/13/3/706wheat seeddetection devicesphenotypic characteristicsthousand seed weight |
| spellingShingle | Haolei Zhang Jiangtao Ji Hao Ma Hao Guo Nan Liu Hongwei Cui Wheat Seed Phenotype Detection Device and Its Application Agriculture wheat seed detection devices phenotypic characteristics thousand seed weight |
| title | Wheat Seed Phenotype Detection Device and Its Application |
| title_full | Wheat Seed Phenotype Detection Device and Its Application |
| title_fullStr | Wheat Seed Phenotype Detection Device and Its Application |
| title_full_unstemmed | Wheat Seed Phenotype Detection Device and Its Application |
| title_short | Wheat Seed Phenotype Detection Device and Its Application |
| title_sort | wheat seed phenotype detection device and its application |
| topic | wheat seed detection devices phenotypic characteristics thousand seed weight |
| url | https://www.mdpi.com/2077-0472/13/3/706 |
| work_keys_str_mv | AT haoleizhang wheatseedphenotypedetectiondeviceanditsapplication AT jiangtaoji wheatseedphenotypedetectiondeviceanditsapplication AT haoma wheatseedphenotypedetectiondeviceanditsapplication AT haoguo wheatseedphenotypedetectiondeviceanditsapplication AT nanliu wheatseedphenotypedetectiondeviceanditsapplication AT hongweicui wheatseedphenotypedetectiondeviceanditsapplication |