Our technology

In order to enable our plant identification API to provide the best results, our tech stack uses Google’s Tensorflow and some python-based heavy lifting. Minor part of the service is based on Google Cloud and Microsoft Azure.

The neural network’s architecture is based on Inception model, but we have implemented a bunch of customization, mostly related to the certainty determination, adaptive represenative photo selection and improving the accuracy with some meta attributes like GPS coordinates.

Model specifications

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Identifiable plants
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Success rate

Our newest model can identify 10.788 plants species. The set of species selection is based on the most identified plants within United States, European Union, India and Australia.

We are continualy testing the accuracy. It’s havily related on the quality of the photo, but the general success rate fluctuates between 80%-90%. In the future versions we’l stabilize the rate over 90% while widening the spectrum of classified species.

Roadmap

  • Object detection to give you the bouding boxes for different plant species within a single image
  • Photo quality estimation - to enable the client to decide if the photo is goog enough to sent it for identification; does the photo even contain a plant?
  • Feature extraction