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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.

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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.
 
Creator Conejo Rodriguez, F.
Gonzalez Guzman, J.
Ramirez, Gil J.
Urban, Milan
Wenzl, P.
 
Subject evaluation
gene banks
machine learning
agronomic characters
phenotyping
imagery
classification
functional diversity
 
Date 2023-08-01
2023-12-26T13:59:57Z
2023-12-26T13:59:57Z
 
Type Presentation
 
Identifier 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.
https://hdl.handle.net/10568/135933
 
Language en
 
Rights CC-BY-4.0
Open Access
 
Format 17 sl.
application/pdf