Great question! My solution is still to photograph without the app, then post-process the geotagging and taxon tagging and observation fields on my computer, then batch upload. That’s the fastest iNat workflow I’ve figured out. It is still kinda’ slow though, waiting for the uploader to process the metadata then manually combining photos together when I’ve taken several of one organism.
I used to spend a lot more time using the uploader, but 2020 has been an odd and distracting year (!) and I’ve got into a habit of just uploading the most important observations (new species to an area) and leaving the rest of it off iNat. Instead I’ve been spending most of my iNat time identifying and tagging others’ observations.
None of this stops me from observing though. I’m an outlier in that I do ecological surveys all the time. We live in an age of unprecedented ecological change and so I’ve turned my life into one long transect. I averaged just over 3,700 observations a week this year—assuming 12 hour days, that’s an average annual OPH of 45. To do this, I don’t use iNat. I focus on the taxa I can reliably identify quickly, and I only photograph/record them when I’m unsure of an ID or they’re notable (breeding or flowering out of season, or in an unexpected place, or a new interaction or behaviour). I record my observations into a database on my iPhone, mostly as audio notes that I later bulk transcribe into text with AWS Transcribe.
iNat is not designed to be the place for this volume of recording, but I’d still like a faster way to upload my photos and audio.
In my ideal world, I’d take my folder of photos and audio recordings from a week, and the data from all the observations I made that week, and a computer script would combine them to automatically label my photos with the taxon and my observations notes, by matching the datetime stamps of my observations with the photos, and then automatically upload them all up to iNat. That would involve the iNat API.
I’d be super interested to hear if anyone’s already got a workflow like this built.