HIGHLIGHTS
- What: Working in this direction, (Blazakis et_al, 2017) an integrated image-based tool on automated methodology was developed to describe olive fruit, leaf and endocarp morphologies. This approach showed a very high classification accuracy among 14 olive cultivars in comparison to previous studies. This study shows that morphological markers, specifically the geometrical features of fruits, leaves, and endocarps, could effectively identify and discriminate olive germplasm.
- Who: Konstantinos N. Blazakis from the Inland University of Applied Sciences, Spain have published the article: Discrimination of 14 olive cultivars using morphological analysis and machine learning algorithms, in the . . .
If you want to have access to all the content you need to log in!
Thanks :)
If you don't have an account, you can create one here.