In particular, know how to identify the common and deadly species (eg: much of the genus Amanita) yourself, and get multiple trustworthy field guides for your part of the world.

  • hedgehog@ttrpg.networkEnglish
    0·
    8 months ago

    Identifying mushrooms with an ML-based algorithm is a fine idea if you properly design the application to leverage that. As a hedgehog, this is what I would do:

    1. Train my model on a variety of mushrooms, particularly poisonous ones.
    2. When testing the model, test as many mushrooms as possible and take note of what’s frequently mis-identified.
    3. When testing the model, make sure to get a variety of different kinds of lighting.
    4. In addition to the mis-identifications noted while testing the app, maintain a list of commonly misidentified mushrooms - like the hedgehog mushroom and its counterparts - particularly the ones a forager should be most concerned with (meaning the most poisonous ones).
    5. When identifying a mushroom to the user, err on the side of calling it a poisonous mushroom. Consider providing a list of possible matches, with the worst case scenario ones up top.
    6. Include pictures and other information about the mushrooms, as well as regional mushroom lookups for mushrooms that weren’t included.
    7. Don’t include text like “99% confident that this is a hedgehog mushroom” when the 99% figure is an output from your ML model. I know we said earlier to make sure to do a ton of testing and I’m sure you think you did, but you didn’t do enough to be able to say that. At best, reduce your certainty by 25%, then divide that number between the identified mushroom and the lookalikes, making sure to give extra weight to the most poisonous ones. So that 99% certainty becomes at most a more realistic 38% chance that it’s the poisonous lookalike and 37% chance that it’s whatever was identified in the first place.

    You might say that this app would be useless for determining if a mushroom is safe to eat, and I agree, but it’s also a better approach than any of the existing apps out there. If you need to use an app to determine if a wild mushroom is safe to eat then the answer is simple: it isn’t. C’mon, I’m a hedgehog and even I know that.

      • hedgehog@ttrpg.networkEnglish
        0·
        8 months ago

        This comment reads like it was written by someone who has never designed a mushroom identification app.

    • KairuByte@lemmy.dbzer0.comEnglish
      0·
      8 months ago

      I feel like training on poisonous mushrooms is the wrong direction. You want to err on the side of poisonous, not edible. Anything it can’t identify should be considered poisonous.

      • hedgehog@ttrpg.networkEnglish
        0·
        8 months ago

        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.
    • Ultraviolet@lemmy.worldEnglish
      0·
      8 months ago

      It’s probably just a ChatGPT wrapper with a preset prompt. That’s all these “AI entrepreneurs” are capable of. Absolute fucking hacks.

      • Akisamb@programming.devEnglish
        0·
        8 months ago

        Convolutional neural networks and plant identifying apps came before chat gpt. Beyond both relying on neural networks they don’t have much in common.