1 MB by images is too much. It means that you have a lot of pixels to compute in the inputs, and your images have more features that are not very useful for the classification (we human don't need high resolution images for recognize objects, it's the same for model)
It means that maybe you need also a more deeper network, and then, more computation.
In your particular case, you have a dataset of trees, so images are somewhat similar and then, maybe the classification is harder. So, images need enough information in pixels to have a good prediction.
You should resize your whole datsets to more common size because 1mb are too much (I don't know the resolution, but I think it could be around thousand for both dimension).
Imagenet have images of size 224x224, and there are 1000 classes, which some of them are close. For examples, there are many dogs classes (boxer, american terrier, ...), cats classes (tiger, egyptian, ....), thus I think size of a few hundred would be enough.
In your comment you said you will try with 336x252, I think it can be a good start.
Of course you will need some experimentation in this regard. Maybe you can try to train some models in a few epoches with different image sizes, to see the best one, and keep it to train the model further !