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All the operations you are doing in preprocessing are in fact convolutions. So in fact you are building a CNN with N+5 layers where you fix the weights the first 5 layers.
So you can do the preprocessing if you consider that you found the best operations to recognize the type of wood.
Otherwise leave the network to train all the layers using the original RGB image as input, and it will try to learn the best descriptors (weights) from the provided samples for providing the best results.
On the other hand, doing preprocessing or fixing the weights on the first layers are both used in the training of CNNs. Having less layers to train will speed up the training process and helps to avoid overfitting.
