Pl@ntNet
Released in 2013Pl@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.
Use Cases
- To identify unknown plants encountered during nature walks or hikes
- To contribute to citizen science projects by uploading plant observations
- To learn about local flora and ecosystems in an interactive way
- To assist in gardening and landscaping by identifying plants and weeds
- To support biodiversity research by collecting data on plant distributions
- To monitor invasive plant species in a given area
- To create a personal digital herbarium of observed plants
- To verify the identity of medicinal plants before use
- To assist in environmental impact assessments for construction projects
- To enhance botanical education in schools through interactive plant identification
Job Uses
Landscape Designer
HireIdentifies plants in existing gardens and discovers new species for client projects.
Park Ranger
HireCatalogs plant species in protected areas and educates visitors about local flora.
Urban Forester
HireMonitors and manages tree species diversity in city environments using the app.
Botany Teacher
HireUses the app as an interactive learning tool for students during field trips.
Environmental Consultant
HireConducts plant surveys for environmental impact assessments using Pl@ntNet for quick identification.
Organic Farmer
HireIdentifies beneficial plants and potential weeds in and around crop fields.
Herbal Medicine Practitioner
HireLocates and verifies medicinal plants in the wild for sustainable harvesting practices.
Florist
HireExplores new plant varieties to expand product offerings and provide information to customers.
Note: This tool is designed to augment human capabilities, not fully replace jobs. The extent of its impact may vary based on specific job requirements and industry standards.
Pros & Cons
API
An API is available!
- 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
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.
Sources:
FAQs
How does Pl@ntNet handle images of non-plant entities, such as faces or objects, and what measures are in place to ensure only plant-related data is used for identification?
Pl@ntNet incorporates automated rejection of inappropriate content. This means that any pictures of non-plant entities, such as faces or pornographic content, are filtered out and no results are returned. This mechanism ensures that only plant-related data is used for identification.
Can users contribute to the accuracy of Pl@ntNet by sharing their knowledge and reviewing observations made by others?
Yes, users can contribute to the accuracy of Pl@ntNet by sharing their knowledge and reviewing observations made by others. The platform is based on a cooperative learning principle, where users who have created an account can share their observations, which can then be reviewed by the community and used by the AI to teach it to recognize plants.
What types of plant images are most informative for accurate identification, and how many images should be submitted for better results?
The most informative types of plant images for accurate identification are flowers, fruits, and leaves. Submitting several images of these parts can improve results. For example, submitting several flowers and/or fruits is often more efficient than trying to be exhaustive in coverage of different types. However, images of bark are less discriminative and should be used only when other parts are not visible.
How frequently is the Pl@ntNet AI model updated, and what criteria are used to measure its performance?
Pl@ntNet's model is updated approximately every two months with a principle of non-regression. Non-regression is measured in terms of several indicators, including accuracy, number of species recognized, response time, energy consumption, and memory usage. The model is tested on private datasets of different nature to ensure it meets these criteria.
Can users use the GBIF species API to get more data about the species identified by Pl@ntNet, and how does this enhance the tool's functionality?
Yes, users can use the GBIF species API to get more data about the species identified by Pl@ntNet. If a Pl@ntNet species has a gbif.id field, users can use it to load more data, such as vernacular names, which enhances the tool's functionality by providing additional information about the identified species.
How does Pl@ntNet handle the scalability of its database as the number of observations doubles every year, and what solutions have been implemented to address these challenges?
Pl@ntNet uses a document-oriented database, specifically ArangoDB, which provides flexible storage and dynamic querying capabilities. This solution allows Pl@ntNet to handle tens of millions of documents and dynamically query them, reducing disk usage by 95% and improving query performance.
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*Last modified: October 24, 2024
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