@article{
author = "Oveisi, Mostafa and Šikuljak, Danijela and Anđelković, Ana and Božić, Dragana and Trkulja, Nenad and Piri, Ramin and Poczai, Peter and Vrbničanin, Sava",
year = "2024",
abstract = "Background Avena fatua and A. sterilis are challenging to distinguish due to their strong similarities. However,
Artificial Neural Networks (ANN) can effectively extract patterns and identify these species. We measured seed traits
of Avena species from 122 locations across the Balkans and from some populations from southern, western, and
central Europe (total over 22 000 seeds). The inputs for the ANN model included seed mass, size, color, hairiness, and
placement of the awn attachment on the lemma.
Results The ANN model achieved high classification accuracy for A. fatua and A. sterilis (R2>0.99, RASE<0.0003) with
no misclassification. Incorporating geographic coordinates as inputs also resulted in successful classification (R2>0.99,
RASE<0.000001) with no misclassification. This highlights the significant influence of geographic coordinates on
the occurrence of Avena species. The models revealed hidden relationships between morphological traits that are
not easily detectable through traditional statistical methods. For example, seed color can be partially predicted by
other seed traits combined with geographic coordinates. When comparing the two species, A. fatua predominantly
had the lemma attachment point in the upper half, while A. sterilis had it in the lower half. A. sterilis exhibited slightly
longer seeds and hairs than A. fatua, while seed hairiness and mass were similar in both species. A. fatua populations
primarily had brown, light brown, and black colors, while A. sterilis populations had black, brown, and yellow colors.
Conclusions Distinguishing A. fatua from A. sterilis based solely on individual characteristics is challenging due to
their shared traits and considerable variability of traits within each species. However, it is possible to classify these
species by combining multiple seed traits. This approach also has significant potential for exploring relationships
among different traits that are typically difficult to assess using conventional methods.",
publisher = "BMC , United Kingdom",
journal = "BMC Plant Biolog",
title = "Application of artificial neural networks to classify Avena fatua and Avena sterilis based on seed traits: insights from European Avena populations primarily from the Balkan Region",
pages = "537",
volume = "24",
doi = "10.1186/s12870-024-05266-3"
}
