Pl@ntNet
Pl@ntNet is an AI-powered platform for identifying plants through photos taken by users. Available as a web and smartphone app, it allows people to snap pictures of plants and receive likely species identifications. The system operates on a citizen science model, where users contribute observations that are reviewed by the community and used to train the AI, improving accuracy over time. Pl@ntNet serves as a massive biodiversity observatory, with millions of contributors across the globe. It's useful for conservation efforts, education, and community engagement among nature enthusiasts. The platform also offers an API for developers to integrate plant identification features into their own applications. Overall, Pl@ntNet combines AI technology with crowd-sourced data to create a powerful tool for understanding and preserving plant biodiversity.
Made by Cirad, INRAE, Inria, IRD, and Tela Botanica · Listing checked Oct 24, 2024
What it does
Use it to
- Identify unknown plants encountered during nature walks or hikes
- Contribute to citizen science projects by uploading plant observations
- Learn about local flora and ecosystems in an interactive way
- Assist in gardening and landscaping by identifying plants and weeds
- Support biodiversity research by collecting data on plant distributions
- Monitor invasive plant species in a given area
- Create a personal digital herbarium of observed plants
- Verify the identity of medicinal plants before use
- Assist in environmental impact assessments for construction projects
- Enhance botanical education in schools through interactive plant identification
Pricing
Checked Oct 24, 2024- Free for non-profit use
- Pl@ntNet offers a free plan with 500 identifications per day for individual use. For team use, volume-based pricing is available, ranging from €5 to €2 per 1,000 identifications based on yearly volume. Enterprise solutions are customizable. API access requires an account and API key. The platform operates under a non-profit research and educational initiative, with specific terms for data sharing and cooperative learning. Validated observations are integrated into GBIF. Users should check the official website for the most up-to-date licensing details.
Prices come from Pl@ntNet's public pricing page and change, so confirm on their site before you subscribe.
Good and watch out
What's good
- High accuracy in plant identification, reaching up to 88.2% in some reports
- Transparency through confidence scores for each possible species
- Comprehensive data sets sourced by experts to reduce bias
- User-friendly interface for easy photo upload and species matching
- Contributes to citizen science and biodiversity research
- Available as both web and mobile applications
- Supports multiple languages
- Offers API access for developers
- Regular updates to improve recognition performance
- Free for non-commercial use
Watch out for
- Accuracy may vary when identifying leaves or plants not in bloom
- Performance depends heavily on high-quality, well-lit, and detailed plant photos
- Risk of misidentification if users are unaware of the app's limitations
- May struggle to identify rare or unusual plants due to limited training data
- Requires internet connection for full functionality
- Commercial use beyond 500 queries per day requires payment
- Limited to plant identification, not suitable for other types of organisms
- Relies on user contributions, which may lead to some data quality issues
- May not cover all plant species, especially in less documented regions
- Learning curve for users to provide optimal images for identification
Tool facts
Free Version
Yes
Pricing
Free for non-profit use
API Available
Yes
Release date
December 31, 2012
Platforms
AI models
Languages
Tags
For developers
Using the Pl@ntNet API
- The Pl@ntNet API provides a RESTful Web service for computational access to the visual identification engine.
- Developers can submit up to 5 images of the same plant and receive a list of the most likely species along with confidence scores.
- Both POST and GET requests are supported for image submission.
Web Page Integration
- Developers can integrate Pl@ntNet identification into their web pages using the 'ai-taxonomist-webcomponent' available on GitHub.
- Example of HTML/PHP integration is provided in the documentation.
Benchmark/Batch Processing
- A Node.js benchmark script is available for running Pl@ntNet identification on a set of images with or without ground-truth to assess results quality.
Third-Party Libraries
- The Pl@ntNet API is integrated into the R package 'BiologicalRecordsCentre/plantnet' for easier integration into R-based projects.
API
- Create an account and generate a private API key from account settings
- API key authentication required for all requests
- POST and GET endpoints available for image submission
- Up to 5 images can be submitted per request
- Free for up to 500 identification queries per day
- Paid plans available for commercial usage beyond 500 queries per day
- Examples provided in Node.js, Java, PHP, Python, and R
- API documentation available on the Pl@ntNet website and exposed through OpenAPI
- API availability: Yes
Sources
Community resources
Pl@ntNet Official Website
The official website of Pl@ntNet provides detailed information about the platform, its features, and community resources. It includes guides on how to use the tool, community contributions, and scientific publications that utilize Pl@ntNet data.
GBIF Pl@ntNet Dataset
The GBIF dataset for Pl@ntNet contains occurrences of plants automatically identified from user observations. It includes information on the validation criteria and method steps used to ensure the accuracy of the data.
Pl@ntNet App Store Page
The Pl@ntNet app page on the Apple App Store provides information about the mobile application, its features, and user reviews. It is a useful resource for those who want to learn more about the app and its functionalities.
Pl@ntNet Case Study
The case study on ArangoDB provides an overview of Pl@ntNet's architecture and its role as a citizen science observatory. It highlights the platform's capabilities and its significance in botanical observations.