This is exactly the kind of thing that keeps me coming back to HN year after year. 99% may be a quick, mildly entertaining read, but that 1% tends to be empowering or life changing for me. I've had a continuously growing interest in plant and bird identification as a hobby (animals are a bit easier). I've gone so far as to research apps, put Audobon society books on my wishlist, and try to look up some specimens I see in my area. Unfortunately it's, frankly, a steep learning curve and not a habit yet for me to take pictures, remember to look at them later, search their characteristics, etc. This will be the perfect tool to help me jumpstart my newfound interest and get more familiar with the flora and fauna around my home.
I know it's weird to mention when you are talking about all these fancy tools, but I was surprised how often I get decent results when simply taking a photo and using Yandex image-search. First time I did it I didn't even expect anything but random pictures of grass, but it's actually good and it is even sort of helpful that I can see similar, but different plants in the same search (which is how I found out that apparently I was mistaking for its close relative a plant that I'm used to putting into my tea from the very childhood: I just called it what my grand-dad thought it was). For well-known decorative plants it often straight up shows the exact plant variety I'm looking for.
iNaturalist is another good option for plant identification (and other forms of life as well). It has decent machine learning for suggesting IDs and is also backed by its community providing IDs. I've been using it a lot this year and have found it pretty helpful for IDs (though some regions and life forms are more likely to have good identifications than others), as well as for showing me what people are seeing in the area, and feeling like I'm contributing useful data.
You can find some interesting starting points at kaggle.com if you want e.g. a large set of photos of agricultural plants labeled as healthy or diseased (allowing you to immediately start in on building a classification model without all the upfront grunt work).
