I'm not describing binary classification, I'm describing multiclass. "Group classification" isn't really a thing. Yes, your ml system probably guesses what kind of plant it is and then looks up the ediblity of components.

The problem with this is how they will handle rare plants that aren't in the dataset, or that are in the dataset but with insufficient data to be recognised.

Because multiclass assumes that it's seen representative data on all possible outputs (e.g. plant types) it will tend to be dangerously confident on plant types it hasn't seen before.

This is because it can rule out other classes. E.g. if you're trying to classify as rose, tulip, or daisy and you get a bramble, your classifier is likely to be very certain it's a rose because tulips and daisies don't have thorns. So your softmax score is likely to show heavy confidence in rose even though it's actually none of them.

This is exactly what can go wrong when you try to use the softmax/standard multiclass approach and come across an interesting rare mushroom or wild carrot. You don't want it to guess which type of plant in the database it's most like, even if this guess comes with scores, you want it to say that it genuinely doesn't know and you shouldn't eat it.