The problem
Bring one of the world's largest image recognition ML models to the hands of as many people as possible. Enable them to identify plants in real time. Build a global map of every plant on the planet.
The solution
Design and build custom native applications. A Swift native application for iOS. A Kotlin native application for Android. Both of them leverage a mobile app backend.
The result
PlantSnap has more than 40 million installs and an average rating of 4.4 globally. Nearly 15 million user plant entries and more than 100 million plant identifications.
Tech stack
Swift
Kotlin
CoreML
MLKit
Figma
How to make high-tech AI usable by everyone?
We started by building a custom camera solution for taking photos and preparing them for image recognition. We added some guides for the users and this worked well initially. But as the image recognition model was getting more complex, it was becoming more difficult to match the user images to the ML model.
It was incredibly challenging and we tried multiple approaches, but the one that actually made a big difference was integrating a custom plant detection model, which runs on device.
Other features
USER PROFILE
USER COLLECTION
EXPLORE MAP
PLANT SEARCH
USER AUTHENTICATION
PUSH NOTIFICATIONS
