It's very bad at trees for some reason. Also mushrooms, but I thought that might be intentional so they don't get blamed for someone eating something poisonous that was misidentified.
Trees are generally difficult to classify well with computer vision. It's hard for the models to establish context because at a scale where you can see the whole tree you tend to include lots of background. If you include a bark photo, it's often ambiguous if there's growth/stuff/weathering on top. Flowers tend to be good inputs.
The training imagery is also really inconsistent in inaturalist and again for plants it's hard to establish context. These are mostly smartphone pictures that non experts have captured in the app. While someone might have verified that a plant is a particular species, because there isn't any foreground/background segmentation, there are often confounding objects in the frame. On top of that you only get a 300px input to classify from. With animals I'd say it's much more common for photographers to isolate the subject. There's also massive class imbalance in inat, a large number of the observations are things like mallards (ie common species in city parks).
I guess the best solution would be to heavily incorporate geographic priors and expected species in the location (which I think is partly done already).
Flowers are crucial for human IDs as well. A lot of tropical tree leaves are very similar, so without context they're virtually impossible to visually distinguish.
Yeah this is a good point. I've done some work with local experts to do tree ID from drone images over rainforest and there were several species where they would need to see the underside of the leaves to get better than the family.
My experience has been great with mushrooms, just to add another datapoint. I mean, it's often about as good as you can get by eye without breaking out the lab equipment.
PlantNet often works better for trees.