Image analysis of seeds and machine learning as a tool for distinguishing populations: Applied to an invasive tree species
Invasive species threaten crops and ecosystems worldwide. Therefore, we sought to understand the relationship between the geographic distribution of species populations and the characteristics of seeds using new techniques such as seed image analysis, multivariate analysis, and machine learning. Th...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Eduem (Editora da Universidade Estadual de Maringá)
2023-10-01
|
| Series: | Acta Scientiarum: Agronomy |
| Subjects: | |
| Online Access: | https://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/62658 |
| _version_ | 1797658122302521344 |
|---|---|
| author | Francival Cardoso Felix Kyvia Pontes Teixeira das Chagas Fernando dos Santos Araújo Josenilda Aprigio Dantas de Medeiros Fábio de Almeida Vieira Salvador Barros Torres Mauro Vasconcelos Pacheco |
| author_facet | Francival Cardoso Felix Kyvia Pontes Teixeira das Chagas Fernando dos Santos Araújo Josenilda Aprigio Dantas de Medeiros Fábio de Almeida Vieira Salvador Barros Torres Mauro Vasconcelos Pacheco |
| author_sort | Francival Cardoso Felix |
| collection | DOAJ |
| description |
Invasive species threaten crops and ecosystems worldwide. Therefore, we sought to understand the relationship between the geographic distribution of species populations and the characteristics of seeds using new techniques such as seed image analysis, multivariate analysis, and machine learning. This study aimed to characterize Leucaena leucocephala (Lam.) de Wit. seeds from spatially dispersed populations using digital images and analyzed their implications for genetic studies. Seed size and shape descriptors were obtained using image analysis of the five populations. Several analyses were performed including descriptive statistics, principal components, Euclidean distance, Mantel correlation test, and supervised machine learning. This image analysis technique proved to be efficient in detecting biometric differences in L. leucocephala seeds from spatially dispersed populations. This method revealed that spatially dispersed L. leucocephala populations had different biometric seed patterns that can be used in studies of population genetic divergence. We observed that it is possible to identify the origin of the seeds from the biometric characters with 80.4% accuracy (Kappa statistic 0.755) when we applied the decision tree algorithm. Digital imaging analysis associated with machine learning is promising for discriminating forest tree populations, supporting management activities, and studying population genetic divergence. This technique contributes to the understanding of genotype-environment interactions and consequently identifies the ability of an invasive species to spread in a new area, making it possible to track and monitor the flow of seeds between populations and other sites.
|
| first_indexed | 2024-03-11T17:55:29Z |
| format | Article |
| id | doaj.art-d05d34a26cfc434ca6a058d97f5c15ac |
| institution | Directory Open Access Journal |
| issn | 1679-9275 1807-8621 |
| language | English |
| last_indexed | 2024-03-11T17:55:29Z |
| publishDate | 2023-10-01 |
| publisher | Eduem (Editora da Universidade Estadual de Maringá) |
| record_format | Article |
| series | Acta Scientiarum: Agronomy |
| spelling | doaj.art-d05d34a26cfc434ca6a058d97f5c15ac2023-10-17T17:52:20ZengEduem (Editora da Universidade Estadual de Maringá)Acta Scientiarum: Agronomy1679-92751807-86212023-10-0146110.4025/actasciagron.v46i1.62658Image analysis of seeds and machine learning as a tool for distinguishing populations: Applied to an invasive tree speciesFrancival Cardoso Felix0Kyvia Pontes Teixeira das Chagas1Fernando dos Santos Araújo2Josenilda Aprigio Dantas de Medeiros3Fábio de Almeida Vieira4Salvador Barros Torres5Mauro Vasconcelos Pacheco6Universidade Federal do Paraná Universidade Federal do ParanáUniversidade Federal do Agreste de Pernambuco Universidade Federal do Rio Grande do Norte Universidade Federal do Rio Grande do Norte Universidade Federal Rural do Semiárido Universidade Federal do Rio Grande do Norte Invasive species threaten crops and ecosystems worldwide. Therefore, we sought to understand the relationship between the geographic distribution of species populations and the characteristics of seeds using new techniques such as seed image analysis, multivariate analysis, and machine learning. This study aimed to characterize Leucaena leucocephala (Lam.) de Wit. seeds from spatially dispersed populations using digital images and analyzed their implications for genetic studies. Seed size and shape descriptors were obtained using image analysis of the five populations. Several analyses were performed including descriptive statistics, principal components, Euclidean distance, Mantel correlation test, and supervised machine learning. This image analysis technique proved to be efficient in detecting biometric differences in L. leucocephala seeds from spatially dispersed populations. This method revealed that spatially dispersed L. leucocephala populations had different biometric seed patterns that can be used in studies of population genetic divergence. We observed that it is possible to identify the origin of the seeds from the biometric characters with 80.4% accuracy (Kappa statistic 0.755) when we applied the decision tree algorithm. Digital imaging analysis associated with machine learning is promising for discriminating forest tree populations, supporting management activities, and studying population genetic divergence. This technique contributes to the understanding of genotype-environment interactions and consequently identifies the ability of an invasive species to spread in a new area, making it possible to track and monitor the flow of seeds between populations and other sites. https://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/62658artificial intelligence; descriptors; Euclidean distance; Mantel correlation; seed analysis; Leucaena leucocephala |
| spellingShingle | Francival Cardoso Felix Kyvia Pontes Teixeira das Chagas Fernando dos Santos Araújo Josenilda Aprigio Dantas de Medeiros Fábio de Almeida Vieira Salvador Barros Torres Mauro Vasconcelos Pacheco Image analysis of seeds and machine learning as a tool for distinguishing populations: Applied to an invasive tree species Acta Scientiarum: Agronomy artificial intelligence; descriptors; Euclidean distance; Mantel correlation; seed analysis; Leucaena leucocephala |
| title | Image analysis of seeds and machine learning as a tool for distinguishing populations: Applied to an invasive tree species |
| title_full | Image analysis of seeds and machine learning as a tool for distinguishing populations: Applied to an invasive tree species |
| title_fullStr | Image analysis of seeds and machine learning as a tool for distinguishing populations: Applied to an invasive tree species |
| title_full_unstemmed | Image analysis of seeds and machine learning as a tool for distinguishing populations: Applied to an invasive tree species |
| title_short | Image analysis of seeds and machine learning as a tool for distinguishing populations: Applied to an invasive tree species |
| title_sort | image analysis of seeds and machine learning as a tool for distinguishing populations applied to an invasive tree species |
| topic | artificial intelligence; descriptors; Euclidean distance; Mantel correlation; seed analysis; Leucaena leucocephala |
| url | https://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/62658 |
| work_keys_str_mv | AT francivalcardosofelix imageanalysisofseedsandmachinelearningasatoolfordistinguishingpopulationsappliedtoaninvasivetreespecies AT kyviapontesteixeiradaschagas imageanalysisofseedsandmachinelearningasatoolfordistinguishingpopulationsappliedtoaninvasivetreespecies AT fernandodossantosaraujo imageanalysisofseedsandmachinelearningasatoolfordistinguishingpopulationsappliedtoaninvasivetreespecies AT josenildaaprigiodantasdemedeiros imageanalysisofseedsandmachinelearningasatoolfordistinguishingpopulationsappliedtoaninvasivetreespecies AT fabiodealmeidavieira imageanalysisofseedsandmachinelearningasatoolfordistinguishingpopulationsappliedtoaninvasivetreespecies AT salvadorbarrostorres imageanalysisofseedsandmachinelearningasatoolfordistinguishingpopulationsappliedtoaninvasivetreespecies AT maurovasconcelospacheco imageanalysisofseedsandmachinelearningasatoolfordistinguishingpopulationsappliedtoaninvasivetreespecies |