Why do we use different image processing at train and test time for an image classification tasl
The key difference is that for training the preprocessing serves the purpose of data augmentation: it increases variance in the training data and thereby helps to better generalize. That is why you will find random cropping, flipping, rotating and zooming here. 

But during inference that is not required and these random preprocessing steps would not be helpful. Therefore, you only do the preprocessing steps required  and eliminate randomness (e.g. crop images to the correct input size with centercrop instead of randomcrop).