As far as I can tell, there isn’t an easy way to find which images would be most useful for the computer vision. Essentially you need to get a species or genus over the threshold of a certain number of observations:
This means that we do include images from observations of captive and cultivated organisms. Lastly, in recent models, a taxon must have at least 100 verifiable observations and at least 50 with a community ID to be included in training (actually, that’s really verifiable + would-be-verifiable-if-not-captive, because we want to train on images of captive/cultivated records too).
https://www.inaturalist.org/blog/31806-a-new-vision-model
A manual approach might be to look for taxa that you’re familiar with and able to ID, then to create a list (or set of lists) that includes those taxa within your domain of expertise that have fewer than 100 observations.
You can use your list of taxa to create a custom search URL that will only show possible members of that list list so you will see any matching taxa that require an ID. It can work quite well: I keep lists of plants in British Columbia that I can readily identify to use in my own custom search URL.
e.g. https://www.inaturalist.org/observations/identify?list_id=1139967
See the wiki for further examples and instructions on custom search URLs.
The disadvantage of this approach is that many users rely heavily on iNat’s initial, computer vision ID suggestions. So if you filter by exact matches for a taxon with zero or few observations, then there is a good chance you won’t find many which match the list. One way around that is to go up one or two levels and spend time filtering through a genus.