Digital functional phenomic descriptors featured from machine learning-driven image-based phenotyping improve the accuracy of classic descriptors: A case study on Arachis spp. and Phaseolus spp.
| Autores principales: | , , , , |
|---|---|
| Formato: | Ponencia |
| Lenguaje: | Inglés |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/135933 |
| _version_ | 1855513190348095488 |
|---|---|
| author | Conejo Rodriguez, F. Gonzalez Guzman, J. Ramirez, Gil J. Urban, Milan Oldřich Wenzl, Peter |
| author_browse | Conejo Rodriguez, F. Gonzalez Guzman, J. Ramirez, Gil J. Urban, Milan Oldřich Wenzl, Peter |
| author_facet | Conejo Rodriguez, F. Gonzalez Guzman, J. Ramirez, Gil J. Urban, Milan Oldřich Wenzl, Peter |
| author_sort | Conejo Rodriguez, F. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| format | Ponencia |
| id | CGSpace135933 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| record_format | dspace |
| spelling | CGSpace1359332025-11-05T12:51:40Z Digital functional phenomic descriptors featured from machine learning-driven image-based phenotyping improve the accuracy of classic descriptors: A case study on Arachis spp. and Phaseolus spp. Conejo Rodriguez, F. Gonzalez Guzman, J. Ramirez, Gil J. Urban, Milan Oldřich Wenzl, Peter evaluation gene banks machine learning agronomic characters phenotyping imagery classification functional diversity 2023-08-01 2023-12-26T13:59:57Z 2023-12-26T13:59:57Z Presentation https://hdl.handle.net/10568/135933 en Open Access application/pdf Conejo Rodriguez, F.; Gonzalez Guzman, J.; Ramirez, G.J.; Urban, M.; Wenzl, P. (2023) Digital functional phenomic descriptors featured from machine learning-driven image-based phenotyping improve the accuracy of classic descriptors: A case study on Arachis spp. and Phaseolus spp. 17 sl. |
| spellingShingle | evaluation gene banks machine learning agronomic characters phenotyping imagery classification functional diversity Conejo Rodriguez, F. Gonzalez Guzman, J. Ramirez, Gil J. Urban, Milan Oldřich Wenzl, Peter Digital functional phenomic descriptors featured from machine learning-driven image-based phenotyping improve the accuracy of classic descriptors: A case study on Arachis spp. and Phaseolus spp. |
| title | Digital functional phenomic descriptors featured from machine learning-driven image-based phenotyping improve the accuracy of classic descriptors: A case study on Arachis spp. and Phaseolus spp. |
| title_full | Digital functional phenomic descriptors featured from machine learning-driven image-based phenotyping improve the accuracy of classic descriptors: A case study on Arachis spp. and Phaseolus spp. |
| title_fullStr | Digital functional phenomic descriptors featured from machine learning-driven image-based phenotyping improve the accuracy of classic descriptors: A case study on Arachis spp. and Phaseolus spp. |
| title_full_unstemmed | Digital functional phenomic descriptors featured from machine learning-driven image-based phenotyping improve the accuracy of classic descriptors: A case study on Arachis spp. and Phaseolus spp. |
| title_short | Digital functional phenomic descriptors featured from machine learning-driven image-based phenotyping improve the accuracy of classic descriptors: A case study on Arachis spp. and Phaseolus spp. |
| title_sort | digital functional phenomic descriptors featured from machine learning driven image based phenotyping improve the accuracy of classic descriptors a case study on arachis spp and phaseolus spp |
| topic | evaluation gene banks machine learning agronomic characters phenotyping imagery classification functional diversity |
| url | https://hdl.handle.net/10568/135933 |
| work_keys_str_mv | AT conejorodriguezf digitalfunctionalphenomicdescriptorsfeaturedfrommachinelearningdrivenimagebasedphenotypingimprovetheaccuracyofclassicdescriptorsacasestudyonarachissppandphaseolusspp AT gonzalezguzmanj digitalfunctionalphenomicdescriptorsfeaturedfrommachinelearningdrivenimagebasedphenotypingimprovetheaccuracyofclassicdescriptorsacasestudyonarachissppandphaseolusspp AT ramirezgilj digitalfunctionalphenomicdescriptorsfeaturedfrommachinelearningdrivenimagebasedphenotypingimprovetheaccuracyofclassicdescriptorsacasestudyonarachissppandphaseolusspp AT urbanmilanoldrich digitalfunctionalphenomicdescriptorsfeaturedfrommachinelearningdrivenimagebasedphenotypingimprovetheaccuracyofclassicdescriptorsacasestudyonarachissppandphaseolusspp AT wenzlpeter digitalfunctionalphenomicdescriptorsfeaturedfrommachinelearningdrivenimagebasedphenotypingimprovetheaccuracyofclassicdescriptorsacasestudyonarachissppandphaseolusspp |