Many edible mushrooms have poisonous look-alikes, so your approach would be likely to misidentify those poisonous look-alikes - a potentially deadly mistake.

For example - from https://www.gardeningknowhow.com/edible/vegetables/types-of-edible-mushrooms-their-poisonous-look-alikes

Poisonous Morel Mushroom Look-alikes:

• A common fungus, the false morel is almost the spitting image of its edible cousin except it is not hollow inside and contains cottony material.
• Big red is similar except it has reddish tones and the cap is more brain-like.
• Wrinkled thimble cap truly looks like a morel except its wrinkled cap hangs over the stem.
• Bell morel is smaller and the cap, although similar, is much less textured and it has a cottony interior.

It would be easy to train an ML model to confidently identify any of those as morels if you only trained on morels.

The idea is to train on both so it’s less likely to mistake a poisonous mushroom for an edible one, and to then “hedge” your bet anyway, by always presenting the poisonous look-alikes first.

The most dangerous scenario with this app is also the most useful - a user who has some training in mushroom identification uses the app as a quick way to look up a mushroom they think is a particular edible mushroom, notes that the mushroom they think it is is within the list, then reviews the list of poisonous look-alikes, and then applies their training to rule out those look-alikes. Finally they confirm that they cannot rule out the edible mushroom.

The risks here are that

1. the user’s training is lacking and that they ruled out a poisonous mushroom that the app suggested, or
2. the app didn’t include the particular poisonous mushroom in the first place and the user was thus unable to consider it.