Evaluation of sunflower collection by genetic variability based on germination and plantlet development parameters using Artificial Neural Networks
نویسندگان
چکیده
The aim of the research work was to study the genetic variability of sunflower (Heliantus annuus L.) seeds collection. From 1500 available genotypes a set of 120 recombinant inbred lines coming from different countries were selected to perform the study. The genetic variability consisted in two types of experiments: in vivo and in vitro cultures. The recombinant inbred lines were classified using Artificial Neuronal Networks (ANNs). A first ANN was designed according to genotype origin and variety. Results confirm the ability of the ANN to predict the genotype origin and variety of the sunflower lines of seeds, based on the germination and plantlet development parameters. A second ANN was successfully designed and tested to classify the category of germination plantlet. Classification was performed, for the test set of data, and the results show a very good accuracy.
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