Extraction and discrimination of tobacco leaf shape based on landmark method
The shape information of leaves from 39 tobacco varieties was extracted by using landmark method. The differences in leaf shapes were compared and analyzed among different varieties and different leaf positions at different growth stages. Principal component analysis was used to reduce the dimension...
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| Format: | Article |
| Language: | English |
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Zhejiang University Press
2022-08-01
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| Series: | 浙江大学学报. 农业与生命科学版 |
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| Online Access: | https://www.academax.com/doi/10.3785/j.issn.1008-9209.2021.07.091 |
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| author | ZHONG Peige ZHOU Yeying ZHANG Yan SHI Yi GUO Yan LI Baoguo MA Yuntao |
| author_facet | ZHONG Peige ZHOU Yeying ZHANG Yan SHI Yi GUO Yan LI Baoguo MA Yuntao |
| author_sort | ZHONG Peige |
| collection | DOAJ |
| description | The shape information of leaves from 39 tobacco varieties was extracted by using landmark method. The differences in leaf shapes were compared and analyzed among different varieties and different leaf positions at different growth stages. Principal component analysis was used to reduce the dimensionality of the data. The sources of differences were visualized among different leaf shapes. Decision tree, random forest and support vector machine were used to perform discriminant analysis on tobacco leaf shapes. The results of the principal component analysis showed that the first three principal components accounted for 42.7%, 21.3% and 10.7% of the total differences in tobacco leaves at the flowering stage, which were characterized by leaf width and the maximum width position, leaf torsion, and petiole size, respectively. The discriminant results of tobacco leaf shape based on machine learning showed that the discriminant accuracy based on landmark data was 52%-62%, while the value was 51%-54% for common leaf shape indicators. The discriminant accuracy on superior or medial leaves was about 10% higher than that of inferior leaves, representing more obvious characteristics of variety. Due to the growth of the leaves, the discriminant accuracy of the leaves at rosette stage was nearly 10% lower than flowering stage. The discriminant accuracy of landmark method increased to 77% after removing 12 atypical varieties. The effect of the landmark method on leaf shape information extraction is better than the common leaf shape indicators, which provides a new idea for the automated extraction of leaf shape information. |
| format | Article |
| id | doaj-art-18198bcca92b4ef7a29c047d9f46d29d |
| institution | Kabale University |
| issn | 1008-9209 2097-5155 |
| language | English |
| publishDate | 2022-08-01 |
| publisher | Zhejiang University Press |
| record_format | Article |
| series | 浙江大学学报. 农业与生命科学版 |
| spelling | doaj-art-18198bcca92b4ef7a29c047d9f46d29d2025-08-20T03:32:05ZengZhejiang University Press浙江大学学报. 农业与生命科学版1008-92092097-51552022-08-014853354210.3785/j.issn.1008-9209.2021.07.09110089209Extraction and discrimination of tobacco leaf shape based on landmark methodZHONG PeigeZHOU YeyingZHANG YanSHI YiGUO YanLI BaoguoMA YuntaoThe shape information of leaves from 39 tobacco varieties was extracted by using landmark method. The differences in leaf shapes were compared and analyzed among different varieties and different leaf positions at different growth stages. Principal component analysis was used to reduce the dimensionality of the data. The sources of differences were visualized among different leaf shapes. Decision tree, random forest and support vector machine were used to perform discriminant analysis on tobacco leaf shapes. The results of the principal component analysis showed that the first three principal components accounted for 42.7%, 21.3% and 10.7% of the total differences in tobacco leaves at the flowering stage, which were characterized by leaf width and the maximum width position, leaf torsion, and petiole size, respectively. The discriminant results of tobacco leaf shape based on machine learning showed that the discriminant accuracy based on landmark data was 52%-62%, while the value was 51%-54% for common leaf shape indicators. The discriminant accuracy on superior or medial leaves was about 10% higher than that of inferior leaves, representing more obvious characteristics of variety. Due to the growth of the leaves, the discriminant accuracy of the leaves at rosette stage was nearly 10% lower than flowering stage. The discriminant accuracy of landmark method increased to 77% after removing 12 atypical varieties. The effect of the landmark method on leaf shape information extraction is better than the common leaf shape indicators, which provides a new idea for the automated extraction of leaf shape information.https://www.academax.com/doi/10.3785/j.issn.1008-9209.2021.07.091geometric morphometricslandmark methodtobaccoleaf shapemachine learning |
| spellingShingle | ZHONG Peige ZHOU Yeying ZHANG Yan SHI Yi GUO Yan LI Baoguo MA Yuntao Extraction and discrimination of tobacco leaf shape based on landmark method 浙江大学学报. 农业与生命科学版 geometric morphometrics landmark method tobacco leaf shape machine learning |
| title | Extraction and discrimination of tobacco leaf shape based on landmark method |
| title_full | Extraction and discrimination of tobacco leaf shape based on landmark method |
| title_fullStr | Extraction and discrimination of tobacco leaf shape based on landmark method |
| title_full_unstemmed | Extraction and discrimination of tobacco leaf shape based on landmark method |
| title_short | Extraction and discrimination of tobacco leaf shape based on landmark method |
| title_sort | extraction and discrimination of tobacco leaf shape based on landmark method |
| topic | geometric morphometrics landmark method tobacco leaf shape machine learning |
| url | https://www.academax.com/doi/10.3785/j.issn.1008-9209.2021.07.091 |
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