how to evaluate feature quality for decision tree model

Question

Most of the tutorials assume that the features are known before generating the model and give no way to select 'good' feature and to discard 'bad' ones.

The naive method is to test the model with new features and see how the new results change compared to the previous model but it can be complex to interpret when the tree is complex.

Is there an academic way to select good features and to discard bad ones?

(ressources appreciated)