The primary purpose of Plant.id is to provide a plant species and diseases identification API. To make it accessible to non-programmers, we created a web demo, which you can try at https://plant.id/ website. But did you know Plant.id demo website was created as a progressive web app? This means you can easily install and use it on your mobile device. Here is a tutorial on installing Plant.id to your phone so you can use it as a regular mobile application.
Tap the share button and scroll down to view the whole menu.
Select the option “Add to home screen.”
Confirm the installation with the “Add” button.
Now you should see the Plant.id app on your home screen.
Plant.id Demo provides everyone with free plant identification and disease identification. Since it is a demo, you can perform five identifications per week. It is not possible to upgrade this limit. If you are looking for expert plant identification, try our mobile application FlowerChecker.
Discover our journey with rock and mineral identification API. Read about the challenges faced and decisions made throughout the process. It has been a process of discovery and learning. The project is currently on hold but remains open for potential future development. Interested parties can contact the team at email@example.com for access to the internal demo or to share ideas for improvement.
The new API can identify 3,100 species of fungi (including lichens and related organisms such as slime molds) and includes enhanced information that has been carefully selected and prepared based on the needs of people interested in mushroom identification.
Plant.id is an API that identifies plant species and diseases from photos with machine learning. Send us images of your plant and get the possible suggestions with plenty of other information including representative images of the species.
Let us introduce you to the first production version of Plant.id Health Assessment. During the last year, we have been collecting feedback on the beta version of our plant disease identification API. Moreover, we have expanded our datasets and deepened our understanding of machine learning-powered plant disease identification.