This is the full developer documentation for Pl@ntNet docs # Welcome to Pl@ntNet documentation ! > Learn how to use Pl@ntNet mobile and web versions # Welcome to Pl@ntNet documentation ! > Learn how to use Pl@ntNet mobile and web versions # Batch import observations > Pl@ntNet introduces a batch import feature for researchers and naturalists to upload multiple plant observations simultaneously using CSV or XLSX files. This tool streamlines data contribution by automatically associating metadata like species, date, and GPS with uploaded images. We are introducing a powerful new **feature** in Pl\@ntNet that allows users to **import several plant observations at once**, instead of submitting them one by one. This function is particularly useful for users conducting field work or managing large collections of images—such as researchers, students, and naturalists—who want a faster and more efficient way to contribute to the Pl\@ntNet database. Using a simple **CSV or XLSX file**, users can include all the necessary metadata (such as species name, date, and location) for each observation. The metadata is automatically associated with the corresponding image during the upload process. ## Main Features [Section titled “Main Features”](#main-features) * Upload **dozens or hundreds of observations** in a single operation * **Automatically fill metadata** (e.g., species name, date, GPS) via a structured file. * Supported file formats: **.csv** or **.xlsx** * Each row in the file corresponds to **one image** to be uploaded * Ability to **merge several images** into a single observation (automatically or manually) * Automatic detection of the **best matching species** when the species name is missing * Time saving and improved consistency for structured datasets ## How can I access this new feature? [Section titled “How can I access this new feature?”](#how-can-i-access-this-new-feature) To access this beta feature, we need to authorize your Pl\@ntNet account. Please fill out the following form: . We will notify you as soon as your access is authorized, generally within 3 working days. ## How to use the batch import function [Section titled “How to use the batch import function”](#how-to-use-the-batch-import-function) ### Step 1: Prepare your images [Section titled “Step 1: Prepare your images”](#step-1-prepare-your-images) * Ensure each image clearly shows a **single plant or plant part**. * Give each image a **unique filename**, which will be used to match it with its metadata in the file. ### Step 2: Create your metadata file [Section titled “Step 2: Create your metadata file”](#step-2-create-your-metadata-file) * Use a **CSV** or **XLSX** file (use one of these files as a starting point: [example.csv](https://identify.plantnet.org/batch_import/example.csv) / [example.xlsx](https://identify.plantnet.org/batch_import/example.xlsx)). * Each row corresponds to an image and must include the following columns (headers **cannot** vary): | file\_name | sample\_no | organ | date(dd-mm-yyyy) | time(hh:mm:ss) | taxon\_name | lat(wgs84) | lon(wgs84) | gps\_accuracy(m) | is\_geoloc\_public | location\_name | personal\_notes | are\_additional\_data\_public | | ---------- | ---------- | ----- | ---------------- | -------------- | ------------------------------------ | ---------- | ---------- | ---------------- | ------------------ | -------------- | --------------- | ----------------------------- | | IMG001.jpg | 1 | leaf | 30-02-2024 | 12:52:41 | Eschweilera parviflora (Aubl.) Miers | 5.2977652 | -53.059915 | 1000 | true | Guyane | | true | > 📌 Only the file\_name column is mandatory; other fields are optional but strongly recommended. * The **sample\_no** column can be used as a unique identifier for multiple images of the same observation; these images will be merged into a single observation using the value of this field. * The **organ** column can be used to describe the most visible organ in the image; possible values are: flower, fruit, leaf, bark, habit, other (for any other organ or when several organs are visible). ### Step 3: Access the batch import tool [Section titled “Step 3: Access the batch import tool”](#step-3-access-the-batch-import-tool) * Go to the **“Tools › Batch import”** menu of the Pl\@ntNet web interface. * Select the **flora** in which you want to share your observations. If your observations come from different floras, consider splitting your import into several batches, one for each flora. * Upload your **metadata file** (CSV or XLSX format). * Upload your **image files**.    ### Step 4: Comparison and review [Section titled “Step 4: Comparison and review”](#step-4-comparison-and-review) * The system matches the image filenames to the metadata rows. * Review the preview: ensure each observation is correctly filled. * You can **edit** the information before submitting.  ### Step 5: Submit [Section titled “Step 5: Submit”](#step-5-submit) * Once you have reviewed all entries, submit your batch by clicking the “**Share**” button at the bottom left of the screen. * The observations will be processed and added to your account as usual. * You will be able to explore them individually later if necessary. ## Tips for a successful import [Section titled “Tips for a successful import”](#tips-for-a-successful-import) * Ensure that the filenames in your metadata file **exactly match** the image files. * Use the correct standard **date format** (DD-MM-YYYY). * GPS coordinates must be expressed in **decimal degrees**, WGS84. * Check that there are no **empty rows** or formatting errors before uploading. ## Beta status and feedback [Section titled “Beta status and feedback”](#beta-status-and-feedback) This feature is currently in **beta version** and may evolve based on user feedback. If you encounter any problems or have suggestions for improvement, please let us know. Your input helps us streamline bulk data contributions and better support scientific and community plant observation projects. # Mobile app beta > Join the Pl@ntNet mobile app beta program on Android and iOS to access new features and provide feedback. Get early access to updates via Google Play Store or TestFlight. Share your feedback to help improve Pl@ntNet. If you want to contribute to the improvement of the Pl\@ntNet mobile application and access new features in preview, you can join our “Beta” program on Android and iOS. As a tester, you will have access to the latest Pl\@ntNet features and will be able to provide valuable feedback to the developers. To access the beta version of the Pl\@ntNet mobile application, follow these steps: ## Android [Section titled “Android”](#android) 1. Open [this link](https://play.google.com/store/apps/details?id=org.plantnet) on your device or search for PlantNet on the Google Play Store. 2. Scroll down the page and find the section mentioning “Join the beta”. 3. Tap “Join”. 4. Within 10-30 minutes, you may have an update available if there is a beta version newer than your current version (which is not always the case). ## iOS [Section titled “iOS”](#ios) 1. **Install TestFlight** 1. Open the App Store on your iPhone or iPad. 2. Search for “TestFlight” using the search bar. 3. Tap the download button to install the application. 2. **Join the Pl\@ntNet test program** * Click on this link to access the beta version of Pl\@ntNet: * Tap “Accept” to join the test program. * Install the application by tapping “Install”. 3. **Test the application** * Open the Pl\@ntNet application from TestFlight. * Explore the different features and use the application as you normally would. ## Your feedback on the beta [Section titled “Your feedback on the beta”](#your-feedback-on-the-beta) To share your constructive feedback on the latest versions, or give us positive feedback on the addition of a new feature, feel free to contact us: * Android: by email via * iOS: If you encounter bugs you can send feedback directly from TestFlight by tapping “Send beta feedback”. * If necessary, add your comments and screenshots to illustrate your point. # Variety Identification > Pl@ntNet's beta feature identifies plant varieties, particularly cultivated species like tomatoes and cucumbers, using image analysis. Currently limited to specific crops, it offers preliminary variety suggestions based on visual characteristics. User feedback is crucial for improving accuracy and expanding the feature. We are pleased to introduce a new **beta feature** in the Pl\@ntNet application focused on **plant variety identification**, with a particular emphasis on **cultivated species** such as tomato, citrus, cucumber, zucchini, and others. Pl\@ntNet is known for species-level identification, but this new feature goes further: it aims to recognize specific varieties or cultivars within a species based on visual characteristics. This feature is currently limited to a small number of well-documented crops and varieties. By analyzing characteristics such as the shape, color, and size of fruits, as well as leaf morphology from images, Pl\@ntNet provides preliminary suggestions on the most likely variety. These results are generated using models trained on datasets of labeled varieties. As with all beta features, predictions are experimental and should only be used for informational purposes. Your feedback and contributions are essential to help us improve accuracy and expand the scope of this tool in future versions. ## How can I access this new feature? [Section titled “How can I access this new feature?”](#how-can-i-access-this-new-feature) To access this beta feature, we need to authorize your Pl\@ntNet account. Please fill out the following form: . We will inform you as soon as your access has been granted, usually within 3 business days. ## To go further [Section titled “To go further”](#to-go-further) ### Help us cover more species: share your datasets! [Section titled “Help us cover more species: share your datasets!”](#help-us-cover-more-species-share-your-datasets) If you are an expert Pl\@ntNet user and have access to high-quality images showing plant varieties (with reliable annotations), we would be delighted to receive your help! The contribution of such datasets can significantly improve the accuracy and coverage of our new variety identification function. If you wish to share data or collaborate, please do not hesitate to contact us (mentioning it in the comment field of the form) - we are happy to co-build this feature with the community. ### Which species are already covered? [Section titled “Which species are already covered?”](#which-species-are-already-covered) As of April 2025, we cover: 1. ***Cucurbita pepo*** - Zucchini 2. ***Malus domestica*** - Apple 3. ***Pachylobus edulis*** - Safou 4. ***Solanum lycopersicum*** - Tomato 5. ***Solanum melongena*** - Eggplant We are continually importing new data to cover a greater number of species, so don’t forget to visit this page for an updated overview of all currently supported species: . ### Explore plant variety observations [Section titled “Explore plant variety observations”](#explore-plant-variety-observations) With this feature, users will also be able to **explore observations of different plant varieties** shared by the Pl\@ntNet community. These examples offer a visual reference for distinguishing the characteristics of cultivars, such as differences in fruit color, shape, or growth. Whether you are looking to identify a specific variety or simply learn more about cultivated diversity, these annotated observations are a valuable and growing resource.  ### Identify plant varieties [Section titled “Identify plant varieties”](#identify-plant-varieties) In addition to consulting observations, this feature allows you to **identify specific varieties directly from your own images**. By uploading a photo of a variety of a plant that we cover (see the list above), Pl\@ntNet will analyze the image and suggest possible results. Variety identification is available at the following address: and works almost the same way as the classic Pl\@ntNet identification you are used to. In addition to species identification, Pl\@ntNet will also propose a set of varieties for each species. For now, variety identification cannot be shared on the platform like the usual plant observation to prevent potential determination errors from spreading in our datasets.  Variety identification is also available in the beta version of the identification page (see the screenshot below to switch to the beta version), regardless of the selected flora. You can use the dropdown menu at the top of the identification results to switch between suggestions at the **species**, genus, family and **variety** level.   ### Annotate existing plant observations with variety information [Section titled “Annotate existing plant observations with variety information”](#annotate-existing-plant-observations-with-variety-information) As part of this beta release, you will also be able to **annotate existing plant observations with variety information**. If you recognize a specific variety of a plant, you can add or suggest annotations to enrich the database. These contributions are essential to improve the accuracy of the identification function and build a more complete resource for the entire Pl\@ntNet community. An observation can receive a set of annotations. To annotate an observation, select the “Variety” annotation (**only accessible on your own observations**) in the Annotations section of the right sidebar and search for a specific value. If the variety you identify in your image is absent from the list and you have more than 10 images illustrating it, please contact us [here](https://plantnet.org/#contact).  # Vegetation surveys > Pl@ntNet's new beta feature lets you create and explore vegetation surveys (plots/quadrats), monitoring plant communities. Create surveys with date, location, flora, and species lists, using automatic identification or manual entry. Explore surveys shared by others on a map or list. We are pleased to present a new **beta feature** of Pl\@ntNet: the ability to **create and explore vegetation surveys**, also known as **plots** or **quadrats**. This feature is designed to help users interested in **monitoring plant communities** and performing basic vegetation analyses through the application. A **vegetation survey** in Pl\@ntNet includes the following elements: * The **date** of the survey * The **author** of the survey * The **GPS coordinates** of the study site * The **flora** of the study site * The **EUNIS habitat** of the study site * A **square photo** of the ground or vegetation (the quadrat) * The **list of plant species** present in the image This feature allows naturalists, researchers, students, and citizen scientists to **document and share plant assemblages in a standardized way**. ## How can I access this new feature? [Section titled “How can I access this new feature?”](#how-can-i-access-this-new-feature) To access this beta feature, we need to authorize your Pl\@ntNet account. Please fill out the following form: . We will inform you as soon as your access is authorized, generally within 3 business days. ## Creating a vegetation survey [Section titled “Creating a vegetation survey”](#creating-a-vegetation-survey) To create a vegetation survey: 1. First, you must be logged in 2. At the top of the page, click the “**Create**” button and select “**Plot**”. 3. Fill out the form, only the **title** and **date** are required. 4. Optionally upload a **square image** of an area covered with plants. 5. Click the “**Create**” button at the end of the form.   ## Automatically identify species [Section titled “Automatically identify species”](#automatically-identify-species) 1. If you have already uploaded an image, click the “**Identify species**” button under the image. 2. Check the list of species identified by Pl\@ntNet and click the “**Add**” button if you want to add a particular species to your survey.  ## Manually adding species [Section titled “Manually adding species”](#manually-adding-species) In addition to automatic identification, users can **manually add species to their vegetation survey** using an integrated search field with **auto-completion**, making it quick and easy to find the right species. For each species in the survey, users can also provide **additional metadata**, including **coverage estimates** (to indicate the space occupied by the species in the quadrat) and **phenological information** (such as flowering, fruiting, or vegetation of the plant). These details allow for the creation of richer and more informative surveys for ecological analysis and biodiversity monitoring.  ## Exploring vegetation surveys [Section titled “Exploring vegetation surveys”](#exploring-vegetation-surveys) In addition to creating your own **surveys**, you can also **explore vegetation surveys shared by other users**. These surveys provide valuable information on the **diversity and composition of plant communities** in different habitats and regions. You can * browse surveys on a **map** or as a list * view the **image, location, and date** of each quadrat * See the **species detected** and their distribution in the quadrat * Use the surveys as a learning resource or as a point of reference for your own fieldwork. This collaborative approach allows the development of a shared database of plant community observations powered by the Pl\@ntNet community.  ## Beta status and feedback [Section titled “Beta status and feedback”](#beta-status-and-feedback) This feature is currently in **beta**, meaning it is still under development. The accuracy of identification may vary depending on the quality of the quadrat image and the diversity of the scene. We are constantly improving the detection models and the user interface based on your feedback. If you encounter any problems or have any suggestions, please do not hesitate to let us know. Your contribution helps us make this tool more robust and useful for everyone. # Correcting/reporting an identification error > Spot a Pl@ntNet identification error? Suggest a correction, report the mistake, re-identify with Pl@ntNet, or contact the author/community. Your contributions improve data quality and build a stronger botanical knowledge base. If you spot an error in the identification of an observation, several options are available to you: ### **1. Suggest another determination** [Section titled “1. Suggest another determination”](#1-suggest-another-determination) * Propose a new identification by indicating the species you believe to be correct. * Depending on your experience and therefore your **weight in the application**, your suggestion may or may not replace the author’s initial identification. ### **2. Report a misidentified species** [Section titled “2. Report a misidentified species”](#2-report-a-misidentified-species) * If you are certain that the identification is incorrect but you don’t know the correct species, use the option to report the error. This alerts the community while allowing others to propose a correction. ### **3. Relaunch an identification with Pl\@ntNet** [Section titled “3. Relaunch an identification with Pl@ntNet”](#3-relaunch-an-identification-with-plntnet) * You can resubmit the images to the identification tool to explore other potential suggestions. ### **4. Communicate with the author or the community** [Section titled “4. Communicate with the author or the community”](#4-communicate-with-the-author-or-the-community) * **Send a message to the author of the observation** to share your doubts or discuss the identification. These discussions are often very enriching and allow you to learn together. * **Contact all contributors to the observation** using `@all` in your message. This may include the author, validators, and anyone who has voted or contributed to the identification. ### **Why correcting or reporting is important?** [Section titled “Why correcting or reporting is important?”](#why-correcting-or-reporting-is-important) Each correction helps improve the quality of the data and strengthens the reliability of Pl\@ntNet. Your actions help build a more robust and collaborative botanical knowledge base. # Explore menus (mobile) > Learn how to navigate the Pl@ntNet mobile app menus to select floras, access the identification tool, join groups, and explore plant species via the Feed and Profile sections. **The menu at the top of your screen** allows you to select a ***flora***. Choosing a flora helps refine your search based on a geographical area or a specific theme, thereby increasing your chances of obtaining a correct identification. More info on floras [here.](/en/understand/floras) **The menu at the bottom of your screen** allows you to access various features: ***Feed*** In the **Feed** menu, you can see, in real time, the observations shared within the selected flora. ***Groups*** The **Groups** menu allows you to view the groups you belong to and search for new ones. ***Identification*** The **Identification** menu allows you to access the identification tool by clicking on the camera icon or by importing images from your gallery. ***Species*** The **Species** menu allows you to explore all the plants in the selected flora by species, genus, or family. Numerous filters are available for your searches. You have the option to explore plants located around you by clicking on the dedicated button. ***Profile*** The **Profile** menu allows you to access your observations with, once again, many options to filter your data. 👉 [TUTORIAL explore the menus](https://www.canva.com/design/DAGcAXnvgx4/v6gAm70acowmIcZVaYbQCQ/edit) # Explore my data > Explore and manage your Pl@ntNet observations easily. Search, filter, and sort your data; visualize it on a map; and export it in CSV or XLSX format. Access your contributions across groups. When you create an account and share your observations on Pl\@ntNet, all your data is accessible from your mobile devices or the web. **Search and filter your observations**: Use a variety of filters to explore your contributions, such as date, validation status, IUCN criteria, or uses associated with plants. You can also sort your observations in various ways to better organize your data and analyze it quickly. ### **Other features for your data** [Section titled “Other features for your data”](#other-features-for-your-data) * **Access to groups**: view your contributions within the groups you participate in. * **Map visualization**: get an overview of your observations on an interactive map and visualize their geographical distribution. * **Data export**: download your observations in **.csv** or **.xlsx** format to use with other analysis tools or to share them. # Explore species by region or theme > Explore global plant diversity with Pl@ntNet's data explorer. Browse species by region, theme, or taxonomy, view detailed species sheets with photos and distribution maps, and filter by IUCN status or plant use. Access rich botanical information from the Pl@ntNet community. The data explorer allows you to delve into floras through the collaborative Pl\@ntNet database, offering complete access to botanical information collected by the community. **Navigating floras:** Want to explore the botanical diversity specific to a region or theme? Explore plants by selecting a flora from a hundred options offered in Pl\@ntNet. **Exploring species:** Once you have selected your flora, browse to discover the species. * **Taxonomic classification:** Explore species by genus, family or species. Browse the image galleries produced and validated by users for each taxon. * **Selective display:** Display only the illustrated species or the complete list of species in the selected area, depending on your needs. * **Search filters:** Apply various filters to refine your searches, such as the IUCN global red list ranking or plant uses (medicinal, food, etc.). **Detailed species sheets:** For each illustrated species, Pl\@ntNet allows you to access detailed sheets including descriptions, photos and taxonomic and ecological information. **Distribution maps and geolocated data:** * **Interactive maps:** Visualize the geographical distribution of species using interactive maps, showing where each plant has been observed. * **Geolocated data:** Access the geolocated data of observations, thus facilitating the study of plant distribution at different scales. # Explore the feed > Discover the Pl@ntNet feed to explore real-time community observations and biodiversity data worldwide. Use advanced filters to sort findings and participate in collaborative reviews to improve plant identification and support citizen science. **Real-time observation:** * **Community contributions**: Access in real time all observations shared by the community, offering a dynamic view of biodiversity observed around the world. You can explore images, locations, and information about plants, as well as follow the progress of identifications. * **Sorting observations**: Refine your exploration by sorting observations according to different criteria. You have a multitude of filters to help you in your research: geographical area, flora, observation status, identification, review, comments, edits, IUCN status, and more. * **Collaborative review**: Participate in the review of observations shared by other users. You can contribute to improving identifications by providing corrections or validating data. This collaborative review helps enrich the database, ensure better identification accuracy, and support the community in its efforts to collect biodiversity information. By exploring the feed, you have the opportunity to actively participate in improving identifications and validating data. It is a way to contribute to citizen science in a concrete way. # Identification: choosing the right species > Pl@ntNet helps identify plants from images, offering ranked species suggestions, image galleries, and detailed species sheets. Validate your choice using provided resources and community feedback; even beginners are encouraged to contribute. ### Choosing the right species: A guide to validating your identifications [Section titled “Choosing the right species: A guide to validating your identifications”](#choosing-the-right-species-a-guide-to-validating-your-identifications) Pl\@ntNet is a plant identification tool designed to assist you with your botanical observations. When you submit an image, the application provides you with a list of potential species, ranked by probability. It is up to you to select and validate the species that you think is correct. ### **Support for identification** [Section titled “Support for identification”](#support-for-identification) To help you with your choice, Pl\@ntNet provides you with: * **Image galleries**: compare your observation with similar photos. * **Detailed species sheets**: access additional information on the characteristics, uses and distribution of the suggested plants. ### **Going further** [Section titled “Going further”](#going-further) If you wish to pursue your research, feel free to consult botanical floras or reliable resources on the Internet. This can be particularly useful to confirm an identification if you have doubts about the suggestions offered. ### **Validation by the community** [Section titled “Validation by the community”](#validation-by-the-community) Once your observation is validated and shared, it becomes visible to the entire community. Other users can then confirm or correct your identification, thus contributing to the reliability of Pl\@ntNet’s data. ### **Beginners: feel free to share!** [Section titled “Beginners: feel free to share!”](#beginners-feel-free-to-share) If you are new to botany, don’t worry if you make mistakes. The identifications of new users have reduced weight in the system, minimizing the impact of errors. In addition, the community is there to help you progress and refine your skills. # Intertionalisation and languages > Pl@ntNet has partnered with POEditor to expand its international reach. Find out how to contribute to translating the app interface into 54+ languages and help make plant identification accessible to everyone worldwide. Following the growing interest from many Pl\@ntNet users to benefit from the application in their own language, a collaboration with [POEditor](https://poeditor.com/) has been set up! We hope that this collaboration will: 1. complete and improve the translations of the 54 existing languages in which Pl\@ntNet is available, 2. extend the use of Pl\@ntNet to new languages, thus making it easier for more people to use. ## How does it work? [Section titled “How does it work?”](#how-does-it-work) 1. Join the POEditor project at [this address](https://poeditor.com/join/project?hash=TPsTTctdlo) 2. Select the language(s) you wish to translate (or suggest a new language) 3. Confirm your email with the verification link received at the specified email address (if necessary). 4. Wait for someone from Pl\@ntNet to approve you. You will receive an email as soon as you have been accepted, usually within 2 to 5 business days. 5. On [poeditor.com](https://poeditor.com/), follow the quick tutorial and start translating 🚀 All new languages are welcome, provided they have an ISO code. Once the translation reaches more than 40%, the language should be added to the applications within 1 to 3 months. We take this opportunity to warmly thank all the translators who have made Pl\@ntNet more widely accessible, as well as POEditor for supporting us in this adventure. ### GeoPl\@ntNet [Section titled “GeoPl@ntNet”](#geoplntnet) This new space is dedicated to the translation of the web and mobile application interfaces (iOS and Android) of Pl\@ntNet as well as [geo.plantnet.org](http://geo.plantnet.org/). It does not concern the suggestion or editing of common plant names, which you can do directly via the Pl\@ntNet mobile and web applications. # Making a good observation > Improve your Pl@ntNet observations by taking sharp, well-lit photos of various plant organs (leaves, flowers, fruits, whole plant) from different angles, and ideally include the plant's habitat. High-quality images ensure accurate identification and contribute valuable data. The quality of observations is essential to ensure accurate identifications and useful data for the scientific community. Follow these tips to optimize your observations: ### **1. Take sharp and well-framed photos** [Section titled “1. Take sharp and well-framed photos”](#1-take-sharp-and-well-framed-photos) * Make sure the photos are perfectly **sharp** so that the details of the plant organs are clearly visible. * Frame the image so as to **highlight the organ** photographed, without any extraneous elements in the background. ### **2. Avoid exposure problems** [Section titled “2. Avoid exposure problems”](#2-avoid-exposure-problems) * Avoid **underexposed** (too dark) or **overexposed** (too bright) photos. ### **3. Document several plant organs** [Section titled “3. Document several plant organs”](#3-document-several-plant-organs) * Take photos of different organs to maximize the accuracy of the identification: * **Leaves**: show their shape, edge, veins and attachment to the stem. * **Flowers**: capture the petals, the heart of the flower, and their arrangement. * **Fruits**: highlight their shape, color, and possibly the seeds. * **Whole plant**: photograph the general silhouette and its environment. * Vary the **angles of view** for a better representation (front view, side view, underside view). ### **4. Keep an ecological context** [Section titled “4. Keep an ecological context”](#4-keep-an-ecological-context) * If possible, include a photo of the plant in its **natural habitat**, to provide clues about its ecosystem and environment. By following these recommendations, your observations will not only be more useful for identification, but they will also help improve the Pl\@ntNet database and research projects. # Open data > Discover how Pl@ntNet shares its most reliable botanical observations through GBIF, providing open-source data for global research in ecology and biodiversity. Explore our datasets, community-validated records, and professional identification APIs. ## Pl\@ntNet data on GBIF [Section titled “Pl@ntNet data on GBIF”](#plntnet-data-on-gbif) The most reliable Pl\@ntNet observations are now integrated into the international [GBIF (Global Biodiversity Information Facility)](https://www.gbif.org/en/) database. GBIF is a global network and research infrastructure funded by the world’s governments, providing free and open access to biodiversity data. The publication of Pl\@ntNet data on this platform allows researchers worldwide to use them for studies in ecology, agronomy, or biodiversity conservation. Pl\@ntNet data contribute to research on various themes, such as the impact of climate change on plant species distribution, the spread of invasive alien species, and interactions between plants and pollinators. Only observations considered most reliable in terms of species identification are shared on GBIF. This includes two main categories: 1. Observations shared and validated by the community (accessible [here](https://www.gbif.org/dataset/7a3679ef-5582-4aaa-81f0-8c2545cafc81)). 2. Identification requests for which the automatic recognition algorithm reaches a sufficiently high confidence level (accessible [here](https://www.gbif.org/dataset/14d5676a-2c54-4f94-9023-1e8dcd822aa0)). Observations validated by the community are published with their images under a Creative Commons CC-BY-SA license, mentioning the author’s name. In contrast, for identification requests, only the location and the plant name are shared, without any additional personal information. This exceptional contribution to science is the result of the commitment of all Pl\@ntNet users, whether they are casual users, passionate contributors, or data curators. ## **GBIF API** [Section titled “GBIF API”](#gbif-api) It is also possible to use the [GBIF API](https://www.gbif.org/developer/summary) to explore and download Pl\@ntNet data. ## **Pl\@ntNet identification API** [Section titled “Pl@ntNet identification API”](#plntnet-identification-api) The **Pl\@ntNet identification service** is also available as an **API,** [see here](/en/reference/api-plantnet)**.** ## **Pl\@ntNet-300K image dataset** [Section titled “Pl@ntNet-300K image dataset”](#plntnet-300k-image-dataset) Camille Garcin, Alexis Joly, Pierre Bonnet, Jean-Christophe Lombardo, Antoine Affouard, Mathias Chouet, Maximilien Servajean, Joseph Salmon, Titouan Lorieul. **Pl\@ntNet-300K: a plant image dataset with high label ambiguity and a long-tailed distribution.** NeurIPS 2021 - 35th Conference on Neural Information Processing Systems, December 2021, Virtual Conference, France. [Pl@ntNet-300K image dataset](https://zenodo.org/record/5645731#.YeGDOdvjKWh) # Participate > Become a citizen scientist by contributing to Pl@ntNet. Share plant observations, validate data, and participate in micro-projects to support scientific research and global biodiversity documentation. Contribute to Pl\@ntNet and become a player in citizen science! Your participation is essential to enrich the database and support scientific research. ### Join the community [Section titled “Join the community”](#join-the-community) * **Registration:** Create an account on Pl\@ntNet, share your observations, and participate in data review. ### Share your observations [Section titled “Share your observations”](#share-your-observations) * **Data collection:** Only shared observations are integrated into the Pl\@ntNet databases and contribute to scientific research. By adding your observations, you help document the distribution and diversity of plants and contribute to the improvement of the identification model. * **Documentation and validation:** Ensure you provide well-documented observations with high-quality photos and additional information to facilitate validation by the community and experts. ### Review other users’ data based on your skills [Section titled “Review other users’ data based on your skills”](#review-other-users-data-based-on-your-skills) * **Community validation:** Help validate observations from other users based on your botanical knowledge. Each validation helps improve the accuracy of Pl\@ntNet and enriches the database. * **Feedback:** Provide feedback on shared observations. This can include identification corrections, votes on photo quality, or comments on observations. * **Training and learning:** By reviewing observations, you can also learn from other users and improve your botanical skills. ### Importance of contribution and feedback [Section titled “Importance of contribution and feedback”](#importance-of-contribution-and-feedback) * **Data quality:** Contributions and feedback are essential for maintaining the accuracy and reliability of Pl\@ntNet data. Every user plays a role in ensuring the quality and precision of Pl\@ntNet. * **Scientific impact:** Your contributions help create a robust database that can be used by researchers worldwide for ecological, taxonomic, and conservation studies. ### As a citizen contributor [Section titled “As a citizen contributor”](#as-a-citizen-contributor) [Share your observations](/en/tutorials/create-and-share-an-observation) identified with expertise, participate in [data review](/en/cookbook/review-observations) or help with the [translation of the interface](/en/cookbook/intertionalisation-and-languages). ### As a research or educational institute [Section titled “As a research or educational institute”](#as-a-research-or-educational-institute) You can contribute by improving the recognition system with your own data or by using Pl\@ntNet for your student networks. If you have a clean dataset, contact the team by email to discuss it. ### As an association, local authority, botanical garden, national park, or educational institution [Section titled “As an association, local authority, botanical garden, national park, or educational institution”](#as-an-association-local-authority-botanical-garden-national-park-or-educational-institution) You can create a [**micro-project**](/en/understand/plantnet-microprojects) to contextualize Pl\@ntNet to a specific local or thematic flora. Setting up a micro-project allows you to filter Pl\@ntNet services to your species of interest. (See the dedicated [Micro-projects](/en/understand/plantnet-microprojects) section for technical and financial details). # Pl@ntNet's main features > Explore the key features of Pl@ntNet, a powerful tool for identifying plants from images, exploring regional floras, and joining a global community. Learn how to save your observations, join groups, and use GeoPl@ntNet to discover biodiversity in specific geographic areas. Pl\@ntNet offers many features to help you explore, identify, and share biodiversity: ### **1. Identify plants from images** [Section titled “1. Identify plants from images”](#1-identify-plants-from-images) Take one or more photos of a plant via the app and get a list of possible species ranked by probability. You can validate the identification by comparing your image with those from other users and refine the result. ### **2. Explore floras from different regions** [Section titled “2. Explore floras from different regions”](#2-explore-floras-from-different-regions) Discover the species present in a specific region. Access a wealth of detailed information and illustrations for each species, and explore the unique characteristics of the local flora. ### **3. Join the Pl\@ntNet community** [Section titled “3. Join the Pl@ntNet community”](#3-join-the-plntnet-community) Actively participate in the community by exploring contributions from other users. You can help improve the data by correcting or reporting errors and by voting on the quality of images and proposed identifications. ### **4. Create groups** [Section titled “4. Create groups”](#4-create-groups) Create or join groups to share your observations on specific themes or geographic areas. These groups allow for knowledge exchange and the organization of collaborative projects. ### **5. Extract a list of species for a geographic area** [Section titled “5. Extract a list of species for a geographic area”](#5-extract-a-list-of-species-for-a-geographic-area) Thanks to **GeoPl\@ntNet**, you can obtain a list of species present or predicted for a given geographic area. This helps you better understand local biodiversity and track changes in ecosystems. ### **6. Save and manage your observations** [Section titled “6. Save and manage your observations”](#6-save-and-manage-your-observations) By creating an account, you can save all your observations and organize them using powerful search filters. 👉[TUTO Pl@ntNet features](https://www.canva.com/design/DAGVDWRN5Jc/NxXPFGh6p1XeZqYgp1_CVQ/edit) # Report inappropriate behavior or content > Report inappropriate images or user behavior on Pl@ntNet. Find out how to report images that don't meet Pl@ntNet's criteria or users violating community rules. Help maintain a respectful environment for citizen science. Pl\@ntNet is a collaborative platform based on respect and the quality of contributions. If you encounter an inappropriate image or behavior, here’s how to proceed: ### **Reporting an inappropriate image** [Section titled “Reporting an inappropriate image”](#reporting-an-inappropriate-image) * If an image does not meet Pl\@ntNet’s criteria (e.g., it does not represent a plant or contains inappropriate content), report it by clicking on the dedicated **No plant** icon. ### **Reporting a user with inappropriate behavior** [Section titled “Reporting a user with inappropriate behavior”](#reporting-a-user-with-inappropriate-behavior) * If a user exhibits behavior that violates community rules (e.g., repeated publication of off-topic images, inappropriate language, harassment, etc.), you can also report them: 1. Access the profile of the user in question. 2. Click on the **Report this user** option. 3. Specify the category of the report and detail the reasons. ### All reports are reviewed by the Pl\@ntNet team. [Section titled “All reports are reviewed by the Pl@ntNet team.”](#all-reports-are-reviewed-by-the-plntnet-team) By reporting inappropriate content or users, you help preserve a respectful collaborative space dedicated to citizen science. # Review observations > Contribute to Pl@ntNet's accuracy by reviewing observations: vote on image quality, validate identifications, suggest corrections, or report errors. Your help ensures a reliable database for plant identification. Contributing to the review of observations is essential to ensure the quality and reliability of data on Pl\@ntNet. Here are the different actions you can take: ### **1. Vote for the quality of an image** [Section titled “1. Vote for the quality of an image”](#1-vote-for-the-quality-of-an-image) * Evaluate the clarity and relevance of the submitted photos. A good quality image, well-framed and showing distinct organs is crucial for accurate identification. Votes also help highlight quality images to illustrate the galleries. ### **2. Validate the complete identification** [Section titled “2. Validate the complete identification”](#2-validate-the-complete-identification) * If you have the necessary skills, you can validate the entire identification (identification + organs + image quality) taking into account: * **Image quality**: Is it sharp and usable? * **Visible organs**: Are they correctly identified and relevant to the species in question? * **The proposed determination**: Does it correspond to the observed species? ### **3. Confirm or suggest another determination** [Section titled “3. Confirm or suggest another determination”](#3-confirm-or-suggest-another-determination) * **Confirm**: If you agree with the proposed identification, you can validate it. * **Suggest another determination**: If you think another species is more likely, propose an alternative. ### **4. Report an identification error** [Section titled “4. Report an identification error”](#4-report-an-identification-error) * Use this option if you are sure that the identification is incorrect but you do not know the correct species. ### **5. Retag the organs** [Section titled “5. Retag the organs”](#5-retag-the-organs) * If you notice an error in the labeling of the organs (for example, a leaf labeled as a flower), correct it to improve the accuracy of the data. ### **6. Report a malformed observation** [Section titled “6. Report a malformed observation”](#6-report-a-malformed-observation) * An observation is considered malformed if it includes images of several different species. Report these cases so that they can be corrected. ### **7. Indicate that it is not a plant** [Section titled “7. Indicate that it is not a plant”](#7-indicate-that-it-is-not-a-plant) * If an observation does not contain a plant (for example, objects, people, animals or landscapes), use the option to report that it is not a plant. ### **Why participate in the review of observations?** [Section titled “Why participate in the review of observations?”](#why-participate-in-the-review-of-observations) Each contribution, however small, helps to: * Maintain a reliable and high-quality database. * Strengthen collaboration between community members. * Facilitate species identification for all users, beginners and experts alike. Your commitment to reviewing observations makes Pl\@ntNet a collaborative and evolving tool for biodiversity. # Get started > Learn how to use Pl@ntNet to identify plants: download the app (Android/iOS or web), create an account, activate geolocation, take photos of plants for identification, share your observations, and explore/validate others' observations. # Quick Start Mobile Guide [Section titled “Quick Start Mobile Guide”](#quick-start-mobile-guide) Welcome to Pl\@ntNet! Follow this guide to learn how to use the application and start identifying plants. ### Step 1: Download the Application [Section titled “Step 1: Download the Application”](#step-1-download-the-application) Pl\@ntNet is available for free on the following platforms: * **Android:** [Google Play Store](https://play.google.com/) * **iOS:** [Apple App Store](https://www.apple.com/app-store/) **Pl\@ntNet is also accessible** [**via the web**](https://identify.plantnet.org/fr) ### Step 2: Create an Account [Section titled “Step 2: Create an Account”](#step-2-create-an-account) To contribute and access all features, create an account: 1. Open the application. 2. Click on **“Create an account”**. 3. Fill in the required information. ### Step 3: Activate Geolocation [Section titled “Step 3: Activate Geolocation”](#step-3-activate-geolocation) For more accurate identification and for your data to be used in citizen science projects, accept or activate geolocation on your device. ### Step 4: Identify a Plant [Section titled “Step 4: Identify a Plant”](#step-4-identify-a-plant) 1. Click the **“Identify”** button in the application. 2. Take one or more photos of the plant (leaf, flower, fruit, etc.). 3. Submit the image for identification. 4. Browse the galleries and available links to validate one of the identifications proposed by the algorithm. ### Step 6: Share your observations [Section titled “Step 6: Share your observations”](#step-6-share-your-observations) 1. Add additional information if you wish (location, notes, etc.). 2. Share your observation so that it can be reviewed by the community and integrated into the Pl\@ntNet database. ### Step 7: Explore and validate other users’ Observations [Section titled “Step 7: Explore and validate other users’ Observations”](#step-7-explore-and-validate-other-users-observations) 1. Go to the **“Explore”** tab. 2. Browse the community’s recent observations. 3. Use the filters to search by region, species or theme. 4. Review the identifications if you have the skills or vote for the quality of the images. You will thus contribute to the participatory review of the data. # Introduction to Pl@ntNet > Pl@ntNet is a free plant identification app with over 60,000 species, used by tens of millions globally. It's a citizen science project, using user-submitted photos to improve its identification accuracy and contribute to biodiversity mapping and scientific research. Pl@ntNet also offers a professional API. # What is Pl\@ntNet? [Section titled “What is Pl@ntNet?”](#what-is-plntnet) ### An Open Research Consortium Based in Montpellier [Section titled “An Open Research Consortium Based in Montpellier”](#an-open-research-consortium-based-in-montpellier) **Pl\@ntNet** is supported by a consortium of four French research institutes: [Cirad](https://www.cirad.fr/), [Inria](https://inria.fr/fr), [IRD](https://www.ird.fr/), and [INRAE](https://www.inrae.fr/), as well as the One Science Montpellier Foundation. These institutions support the development of the Pl\@ntNet citizen science platform and its plant identification tool. ### A Plant Identification Tool [Section titled “A Plant Identification Tool”](#a-plant-identification-tool) The Pl\@ntNet project began in 2009 with a prototype capable of recognizing 33 species of Mediterranean trees from leaf scans. Today, the application can identify nearly **60,000 species** worldwide. It has several **tens of millions of users** and is translated into more than 50 languages. ### A Participatory Database [Section titled “A Participatory Database”](#a-participatory-database) Pl\@ntNet relies on a global community of contributors who share their observations and photos of plants. This data enriches species sheets and improves the application’s performance. It is also made available to the scientific community via the [Global Biodiversity Information Facility (GBIF)](https://www.gbif.org/fr/) and the [Inventaire National du Patrimoine Naturel (INPN)](https://inpn.mnhn.fr/espece/jeudonnees/36136). To date, the data produced by the Pl\@ntNet community and shared on GBIF has been used in more than **1000 scientific publications** ([gbif.com](https://www.gbif.org/fr/resource/search?contentType=literature\&publishingOrganizationKey=da86174a-a605-43a4-a5e8-53d484152cd3) and [HAL INRIA](https://haltools.inria.fr/Public/afficheRequetePubli.php?idHal=alexis-joly\&auteurs_EPI=Pierre%20Bonnet\&CB_auteur=oui\&CB_titre=oui\&CB_article=oui\&langue=Anglais\&tri_exp=annee_publi\&tri_exp2=typdoc\&tri_exp3=date_publi\&ordre_aff=TA\&Fen=Aff\&css=..%2Fcss%2FVisuRubriqueEncadre.css)). To contribute to the project, you need to create an account and share your observations. ### A Learning Platform [Section titled “A Learning Platform”](#a-learning-platform) Pl\@ntNet is also an educational tool dedicated to the knowledge and learning of botany. The information it produces is accessible by species or geographical area. It is used in many fields such as conservation, education, agriculture, tourism, commerce, gardening… ### A Biodiversity Mapping Tool [Section titled “A Biodiversity Mapping Tool”](#a-biodiversity-mapping-tool) Thanks to the geolocation observations of users, Pl\@ntNet contributes to biodiversity mapping. This data allows modeling the distribution of species, accessible via the **GeoPl\@ntNet** tool. ### A Scientific Research Platform [Section titled “A Scientific Research Platform”](#a-scientific-research-platform) Pl\@ntNet is involved in research projects in botany, conservation, ecology and agronomy which have led to more than fifty [publications](https://plantnet.org/publication/) in scientific journals as well as several theses. ### A Professional API [Section titled “A Professional API”](#a-professional-api) Pl\@ntNet offers a [**Pro API**](https://my.plantnet.org/) that allows integrating its recognition service into other applications. This API is an asset for many projects requiring automatic plant identification. It has more than 10,000 users. # **Usage Statistics** [Section titled “Usage Statistics”](#usage-statistics) * **More than one billion plant identifications** have been carried out since the launch of Pl\@ntNet, making this application an essential resource for plant identification. * The application covers more than **60,000 illustrated species**, accompanied by detailed descriptions, encouraging user learning and discovery. * To date, about twenty [**micro-projects**](/fr/understand/plantnet-microprojects) are hosted on the platform. * Pl\@ntNet records on average **100,000 to 700,000 active users per day**, with peaks reaching **1.5 million daily identifications** during periods of high activity. * More statistics are available on # Pl@ntNet API > The Pl@ntNet API is a RESTful web service providing advanced AI-powered plant identification. Easily integrate species recognition, taxonomic data, and common names into your applications using community-driven deep learning technology. The Pl\@ntNet API provides computational access to the visual identification engine used in Pl\@ntNet applications in the form of a RESTful web service. This service allows you to simultaneously submit 1 to 5 images of the same plant and receive a list of the most likely species along with a confidence score for each of them. The identification engine is based on the most advanced deep learning technologies and is regularly updated through community contributions and the integration of new expert databases. It is the ideal tool for companies wishing to identify plants from their own systems/services/web or mobile applications. The service also makes all Pl\@ntNet taxonomic data available, with common names in more than 54 languages, [taxonomy](/en/reference/powo-taxonomic-reference), identification of diseases and phytopathogens, etc. This service is available here: # Data and image licensing > Understand the licensing and attribution requirements for using images and observation data from Pl@ntNet. Learn about CC BY-SA and CC BY licenses, how to credit contributors, and guidelines for academic or commercial use. This page describes the licensing and attribution requirements for using images and observation data from Pl\@ntNet. Related page: [Using and sharing data in Pl@ntNet](/en/understand/using-and-sharing-data-in-plantnet) Pl\@ntNet is a citizen science project, and the data provided by our community is generally made available under open licenses to support research, education, and conservation efforts. All observations and images are subject to copyright licensing. Specific attribution and licensing requirements must be followed. ## Image usage license [Section titled “Image usage license”](#image-usage-license) ### License type [Section titled “License type”](#license-type) Most images shared on Pl\@ntNet are licensed under **Creative Commons Attribution-ShareAlike (CC BY-SA)**. This means you are free to: * **Share** — copy and redistribute the material in any medium or format * **Adapt** — remix, transform, and build upon the material for any purpose, even commercially Under the following conditions: * **Attribution** — You must give appropriate credit (see examples below) * **ShareAlike** — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original ### Verifying individual image licenses [Section titled “Verifying individual image licenses”](#verifying-individual-image-licenses) While most images are under CC BY-SA, always check the specific license for each image you plan to use, as some contributors may choose different licensing options. License information is displayed on each observation page. ## Observation data license [Section titled “Observation data license”](#observation-data-license) ### License type [Section titled “License type”](#license-type-1) Observation metadata (species identification, location, date, etc.) are licensed under **Creative Commons Attribution (CC BY)**. This means you are free to: * **Share** — copy and redistribute the material in any medium or format * **Adapt** — remix, transform, and build upon the material for any purpose, even commercially Under the following conditions: * **Attribution** — You must give appropriate credit (see attribution requirements below) ## Attribution requirements [Section titled “Attribution requirements”](#attribution-requirements) When using Pl\@ntNet images or data, you **must** include the following attributions: ### Minimum attribution [Section titled “Minimum attribution”](#minimum-attribution) Any use of Pl\@ntNet content must include: 1. **Pl\@ntNet app credit**: mention that the content comes from the Pl\@ntNet platform 2. **License indication**: specify the applicable license (CC BY-SA for images, CC BY for data) 3. **Contributor credit**: mention the name of the original contributor/author ### Example for images and observation data (CC BY-SA) [Section titled “Example for images and observation data (CC BY-SA)”](#example-for-images-and-observation-data-cc-by-sa) Photo(s): \[Contributor Username], CC BY-SA Data source: Pl\@ntNet ([https://plantnet.org](https://plantnet.org/)), CC BY ## Special use cases [Section titled “Special use cases”](#special-use-cases) ### Academic research [Section titled “Academic research”](#academic-research) For academic publications, we recommend including: * The Pl\@ntNet platform URL ([https://plantnet.org](https://plantnet.org/)) * The date the data was retrieved * The applicable license(s) * A reference to relevant Pl\@ntNet publications, where applicable We also remind you that a large part of our data is available through [GBIF](/en/cookbook/open-data), where you can create datasets and obtain a DOI. ### Commercial use [Section titled “Commercial use”](#commercial-use) Commercial use is permitted under CC BY-SA (images) and CC BY (data) licenses, provided that attribution requirements are met. For images under the CC BY-SA license, any derivative work must also be shared under the same license. ### API users [Section titled “API users”](#api-users) If you access images or data via [the Pl@ntNet API](https://my.plantnet.org/), the same licensing and attribution requirements apply. Ensure that your application or service correctly attributes Pl\@ntNet and complies with the license terms. ## Summary table [Section titled “Summary table”](#summary-table) | Content Type | License | Key Requirements | | ---------------- | -------- | -------------------------------------------- | | Images | CC BY-SA | Attribution + ShareAlike + Mention Pl\@ntNet | | Observation Data | CC BY | Attribution + Mention Pl\@ntNet | ## Questions? [Section titled “Questions?”](#questions) If you have questions regarding licensing or need clarification on specific use cases, please contact us via the [Pl@ntNet website](https://plantnet.org/). ## Related resources [Section titled “Related resources”](#related-resources) * [Creative Commons BY-SA License](https://creativecommons.org/licenses/by-sa/4.0/) * [Creative Commons BY License](https://creativecommons.org/licenses/by/4.0/) * [Pl@ntNet API Documentation](https://my.plantnet.org/doc/getting-started/introduction) * [Contributing to Pl@ntNet](/en/understand/using-and-sharing-data-in-plantnet) # Organs > Explore the list and definitions of organs used in Pl@ntNet to categorize plant images. Learn how different parts like leaves, flowers, and fruits, as well as specialized shots like herbarium sheets and aerial photos, help identify species. For each plant image in Pl\@ntNet, an “organ” is attached to it, in order to identify which part of the plant was photographed. The term has been broadened in Pl\@ntNet to cover a wider variety of shots, such as a canopy taken by a drone, a drawing or a herbarium sheet. Here is the list of organs available in Pl\@ntNet and their definitions: ## Leaf [Section titled “Leaf”](#leaf) A generally flat, green organ that grows on the stem of plants and serves primarily to capture light to make food for the plant through photosynthesis.  ## Flower [Section titled “Flower”](#flower) Part of a plant that contains the reproductive organs and allows for the production of seeds, often colorful or remarkable.  ## Fruit [Section titled “Fruit”](#fruit) The organ derived from the flower that contains the seeds and allows for their dispersal. Example: green bean pod, samara, banana, pear, hazelnut, acorn, achene, pea, beech nut, etc.  ## Bark [Section titled “Bark”](#bark) Outer covering of the trunk, branches and roots of trees.  ## Habit (entire plant) [Section titled “Habit (entire plant)”](#habit-entire-plant) Photo of the entire plant, the general appearance of a plant, resulting from its growth and branching pattern.  ## Branch [Section titled “Branch”](#branch) Woody extension of a tree or shrub, bearing leaves, flowers or fruits.  ## Seed [Section titled “Seed”](#seed) Structure containing an embryo and capable of germinating to give rise to a new plant.  ## Bud [Section titled “Bud”](#bud) Small protrusion giving rise to branches, leaves, flowers and fruits.  ## Aerial photo [Section titled “Aerial photo”](#aerial-photo) Photo of a plant taken from above, usually via a drone flying over a canopy, for example.  ## Herbarium [Section titled “Herbarium”](#herbarium) One or more parts of the plant pressed on a sheet, annotated, constituting a herbarium sheet.  ## Drawing [Section titled “Drawing”](#drawing) Artistic or technical drawing of a plant or its parts, made by hand.  ## Uniform background [Section titled “Uniform background”](#uniform-background) Photo of a plant on a solid background. Example: a flower placed on a sheet (with or without color), the whole thing photographed.  ## Anatomy [Section titled “Anatomy”](#anatomy) Microscopic representation of a part of a plant (observed under a microscope).  # Pl@ntUse > Pl@ntUse, a collaborative portal linked to Pl@ntNet, offers resources and information on useful plants and their applications. It features species lists, common names, an etymological dictionary, image galleries, and old publications, complementing existing encyclopedias. PlantUse is a portal on useful plants and plant uses. Initiated by ethnobotanist [Michel Chauvet](https://uses.plantnet-project.org/en/Chauvet,_Michel) at the end of his career at the [AMAP laboratory in Montpellier](https://amap.cirad.fr/en/index.php) as part of the [Pl@ntNet](https://plantnet.org/) project. This platform is hosted by the [BioWikifarm](http://biowikifarm.net/meta/Biowikifarm) consortium, and supported by [MediaWiki](https://www.mediawiki.org/wiki/MediaWiki/en) technology. Pl\@ntUse is a collaborative space for exchanging information on [**useful plants**](https://uses.plantnet-project.org/en/Plantes_utiles) and plant uses. It does not aim to duplicate existing encyclopedias (including Wikipedia), but to offer complementary functions, such as: * uploading resources that you can reuse * portal to relevant resources existing on the Internet * thematic or bibliographic introduction to any topic of interest * species lists (dried vegetables grown in France, cereals of the world…) * [common names](https://uses.plantnet-project.org/en/Noms_populaires_des_plantes) or vernacular names * [Etymological dictionary](https://uses.plantnet-project.org/en/Dictionnaire_%C3%A9tymologique) of plant names * galleries of portraits of cultivars (varieties) * [old books and articles on useful plants](https://uses.plantnet-project.org/en/Ouvrages_et_articles_anciens) * original publications * [iconography](https://uses.plantnet-project.org/en/Iconographie) * Q\&A space 👉 [access the platform](https://uses.plantnet-project.org/en/Accueil) # POWO Taxonomic Reference > Pl@ntNet uses the Plants Of The World Online (POWO) database from Kew Royal Botanic Gardens for its plant species taxonomy. POWO provides up-to-date plant names and information, using a standardized geographical scheme for accurate location data. This ensures Pl@ntNet offers a comprehensive and current flora for each country. ### Species Database and Taxonomy [Section titled “Species Database and Taxonomy”](#species-database-and-taxonomy) Plant species taxonomy and names are complex subjects. Botanical institutes worldwide publish species lists for their country (taxonomic databases or checklists), which must be regularly updated to integrate new species or revise old ones (change of author, synonymy, genus transfer, etc.). To overcome this challenge, Pl\@ntNet uses a global database provided by the Royal Botanic Gardens, Kew, accessible on [Plants Of The World Online (POWO)](https://powo.science.kew.org/) and called [WCVP](https://sftp.kew.org/pub/data-repositories/WCVP/). This checklist provides up-to-date names and attempts to cover plants from all countries, while providing additional information. The public standard provided by the [**TDWG**](https://www.tdwg.org/)[ (Taxonomic Database Working Group)](https://www.tdwg.org/) uses specific geographical areas for each country and region. The [**World Geographical Scheme for Recording Plant Distributions (WGSRPD)**](https://www.tdwg.org/standards/wgsrpd/), associated with the POWO checklist, allows Pl\@ntNet to provide a flora specific to each country. # Publications > Find Pl@ntNet team publications and publications using the Pl@ntNet GBIF dataset. Explore research on plant identification and biodiversity. 👉 [Pl@ntNet team publications](https://plantnet.org/en/publications/) 👉 [Publications from the GBIF dataset](https://www.gbif.org/fr/resource/search?contentType=literature\&publishingOrganizationKey=da86174a-a605-43a4-a5e8-53d484152cd3) # The Pl@nt Game > Improve your botany skills with The Pl@nt Game, a participatory citizen science project. Identify plant observations, compete in duels, and help process taxonomic data to support biodiversity research alongside Pl@ntNet. The Plant Game is a **participatory game** aimed at producing large volumes of taxonomic data to improve our knowledge of biodiversity. The game offers a double benefit: * learning, practicing, or improving your botany skills while having fun (with a skill progression system) * participating in a large-scale citizen science project on biodiversity Three game modes are available: * **Training**, where you learn to differentiate new plants while allowing the system to evaluate your skills * **The Plant Game**, where you will be assigned various observations to identify—the correct species being unknown at the start—with an estimated complexity that adapts to your abilities * **Duels**, where you can challenge other players, either from your friends list or chosen randomly Thanks to the results collected via these three *game* modes, the system will be able to determine the most probable species for the vast amounts of incorrect or uncertain observations provided as input, thus contributing to a better understanding of the distribution and evolution of plants across the French territory. 👉 [access the platform](https://theplantgame.com/login/?next=%2F) # WGSRPD Geographical Reference > Pl@ntNet now uses the WGSRPD geographical reference standard, providing a localized plant identification experience. This ensures a relevant and up-to-date taxonomic basis for users worldwide, based on the region selected. ### **Regionalized Floras** [Section titled “Regionalized Floras”](#regionalized-floras) To improve plant identification, Pl\@ntNet only displays relevant species based on the selected region. Until now, covering all regional floras has been difficult due to the lack of universal standards and the burden of manual updates. Thanks to the adoption of the [POWO reference](/en/reference/powo-taxonomic-reference), we now use a public standard provided by the [**TDWG**](https://www.tdwg.org/) (Taxonomic Databases Working Group). This standard, named [**World Geographical Scheme for Recording Plant Distributions**](https://www.tdwg.org/standards/wgsrpd/) (WGSRPD), defines geographical areas for each country and region. Combined with the POWO reference list, this standard allows us to offer a specific flora for each geographical region. We thus cover all regions of the world, with adapted representation for each continent. The geographical distribution used takes into account territorial limits and floristic particularities, allowing us to offer users a personalized and localized experience. By adopting this global reference, Pl\@ntNet ensures that it provides a solid, relevant and up-to-date taxonomic basis, while facilitating plant identification for all users, regardless of their observation location.  # Access my observations > Access and manage your identification history on Pl@ntNet across mobile and web platforms. Learn how to filter, map, edit, or export your observations as CSV or XLSX files for personal analysis. ### **On the mobile version** [Section titled “On the mobile version”](#on-the-mobile-version) In the mobile app, you can view all your data in the **Profile** section once you are logged into your account. There you will find your observation history, as well as detailed information about each one. ### **On the Web version** [Section titled “On the Web version”](#on-the-web-version) On the web version, you can download all of your observations via the **My data** section. This download allows you to perform more in-depth analyses using other tools or to share your data with collaborators. Simply click on the **Export** button to obtain a .csv or .xlsx file containing all your observations. ### **Filter and organize your data** [Section titled “Filter and organize your data”](#filter-and-organize-your-data) The application offers many options to filter your data, allowing you to quickly find specific information. You can filter your observations according to different criteria, such as: * **Shared**: Observations you have chosen to share with the community. * **Geolocated**: Those with a precise location. * **Determined**: Observations that have been successfully identified. * **Organs**: Based on the plant organs photographed (leaf, flower, fruit, etc.). * **Validated and revised**: Observations validated by other users or modified after revision. * **Messages**: Observations that contain messages or comments. * **Groups**: Observations added to a specific group. * **Determination and vote**: Based on community identifications or votes. * **Date**: Sorting by observation date. ### **Visualization on a map** [Section titled “Visualization on a map”](#visualization-on-a-map) You can also visualize your observations on an interactive map. This allows you to see the geographic distribution of your observations. ### **Preservation of your data** [Section titled “Preservation of your data”](#preservation-of-your-data) When you create an account on Pl\@ntNet and share your observations, they are saved in the application and accessible at any time. You can go back to your old observations and modify them if necessary. ### Edit a shared observation [Section titled “Edit a shared observation”](#edit-a-shared-observation) You can modify the identification (the determination) of an observation that has already been shared. **On mobile** 1. Open your observation by tapping “Details”. 2. Tap **Enter species**. 3. Type a new plant name (binomial name). **On the web** 1. Open your observation by clicking on it. 2. Type a new plant name (binomial name) in the “Species name” text field. The initially proposed species name will be updated. You can also edit the locality and the comment field. The determination of the observation may eventually depend on the votes of other users. [More information here.](/en/cookbook/correcting-reporting-an-identification-error) ### Delete an observation [Section titled “Delete an observation”](#delete-an-observation) You can delete an observation from its details page. On mobile, use the “…” menu at the top right, and on the web, the “Actions” section at the bottom right. It is currently not possible to delete a single image from a shared observation. ### **Unshared observations** [Section titled “Unshared observations”](#unshared-observations) Observations made from your mobile, but not shared on the platform, are stored locally on your device. You will not be able to view them from other devices and they will be lost if you change phones! ## Export your observations [Section titled “Export your observations”](#export-your-observations) On the web version only, you can download all your observations for your own analyses. In the “My data” section, click on “**Export as**”, then choose the format (CSV or XLSX). # Change the app language > Learn how to change the interface and common name settings on the Pl@ntNet mobile app and website. Discover how to select primary and secondary languages in just a few simple steps. Our applications are available in over 50 languages. Here is how to change it: ## From the mobile application [Section titled “From the mobile application”](#from-the-mobile-application) 1. Open the **Pl\@ntNet** application 2. Tap on “**Profile**” at the bottom right 3. Tap on the three small dots at the top right. 4. Select “**Options**” 5. Tap on “**Settings and languages**” 6. Select your preferred language from the available options. Note: From the mobile application only, you can also choose a secondary language. This second language will be used if the primary language does not contain the plant name, in order to display the common/vernacular name. ## From the identify.plantnet.org website [Section titled “From the identify.plantnet.org website”](#from-the-identifyplantnetorg-website) 1. Open the [website](https://identify.plantnet.org/) 2. Click on the language ISO code at the top right (e.g., “EN” for English, “FR” for French) 3. Select your preferred language from the available options ## Additional information [Section titled “Additional information”](#additional-information) The translations of the applications are largely the result of our community and our users. Common names are directly translatable from the applications. The rest of the interface is translated via [POEditor](/en/cookbook/intertionalisation-and-languages). You can participate in this translation by following the [instructions here](/en/cookbook/intertionalisation-and-languages). # Choose a flora > Learn how to select the best flora on Pl@ntNet to improve plant identification accuracy. Choose from geographic or thematic lists, or use automatic geolocation to find species relevant to your area. **Floras** are lists of plant species specific to a geographic region or a particular theme. Choosing a flora adapted to your observation helps refine identification results and increases your chances of getting a correct identification. By selecting the appropriate flora, you ensure that the identification tool focuses on the species most relevant to your location or subject of interest. Pl\@ntNet offers a wide selection of floras, grouping species by geographic regions or specific themes. You can easily choose or change the active flora at any time via the drop-down menu located at the top of your screen. By default, if your **geolocation** is enabled, Pl\@ntNet will automatically determine the geographic flora corresponding to your current position. This ensures that identification suggestions are tailored to your local environment. For more information on how floras work, visit the [dedicated page](/en/understand/floras). 👉[TUTO Understanding floras](https://canva.link/69srgs6db0t5t1c) # Create an account on Pl@ntNet > Create an account on Pl@ntNet to manage your plant observations, contribute to citizen science, and join community groups. Learn how to sign up on mobile or web and unlock features to save, edit, and share your data for botanical research. Creating an account on Pl\@ntNet is simple and opens up many possibilities for managing your data, participating in citizen science, and interacting with the community. ### **How to create an account?** [Section titled “How to create an account?”](#how-to-create-an-account) * **On the mobile version**: Click on **Login** then **Create an account** and follow the steps. * **On the web version**: Click on the **Sign up** button in the menu bar at the top of the screen, then fill out the dedicated form. More information on managing your account is [available here](/en/tutorials/manage-your-user-account). ### **Why create an account?** [Section titled “Why create an account?”](#why-create-an-account) ### **1. Manage your personal data** [Section titled “1. Manage your personal data”](#1-manage-your-personal-data) * By creating an account, you can save all your observations and share them with the community. * View, edit, and organize your data using various sorting tools (by date, geolocation, validation status, etc.). * Access your observations from any device connected to your account. ### **2. Contribute to an exceptional citizen science project** [Section titled “2. Contribute to an exceptional citizen science project”](#2-contribute-to-an-exceptional-citizen-science-project) * Your shared observations fuel research projects, which have already led to more than **1,000 scientific publications**. * Receive help from the community to review and validate your identifications. * Actively participate by reviewing observations, evaluating identifications, or voting on image quality. ### **3. Create and join groups** [Section titled “3. Create and join groups”](#3-create-and-join-groups) * Participate in groups or create your own to explore specific geographical areas, plant families, or themes. * Configure your groups according to your needs: **public or private**, with or without geographical restrictions. * Export group data in **.csv** or **.xlsx** format for use in your projects. # Create and share an observation > Learn how to create, identify, and share plant observations using Pl@ntNet. Discover how to document species through photos and geolocation to contribute to global botanical research and biodiversity preservation. An “observation” in Pl\@ntNet is documentation of the presence of a plant you have encountered in your environment. It includes: * One or more **photos** of the plant. * An observation **date**. * A **geolocation** (optional). * An **identification**. * Possibly, **additional information** about the location or the plant. **Minimum requirements**: an observation must include at least one photo and a date. ### Create observations [Section titled “Create observations”](#create-observations) 1. **Open the Pl\@ntNet app and log in to your account.** 2. **Go to the “Identify” tab.** 3. **Take one or more photos of the plant to be identified, ensuring you:** * Take sharp and well-framed photos. * Ideally eliminate distracting elements such as manufactured objects around the plant. * Capture different angles, including close-ups of leaves, flowers, and fruits if available. * Check that the light is adequate to avoid excessive shadows and blurring. 4. **Start the identification**: * Start the search in Pl\@ntNet. The algorithm will compare your image with millions of other images validated by the community. * The application returns a list of species ranked by decreasing percentage of certainty. * You can apply filters on families or genera to better target your results if you wish. 5. **Explore the galleries to validate the identification:** * Browse the gallery images to help you choose the right species. These galleries are made up of images validated by the community. * Links to species factsheets are also available to help you in your choice. 6. **Validate the correct identification:** * If you are not certain of the species, you can simply enter the genus or share your observation without identification. But don’t hesitate to share; other community members will be able to correct your errors. 7. **Add additional details:** * Location **:** Geolocation is automatically added if your GPS is enabled. You can also enter it manually if necessary. * Notes **:** Include details about the plant’s habitat, its environment, or any other relevant information if available. 8. **Share your data with the community:** Once your observation is created, you can: * Share your observation with the community. * Keep your observations private. You will find [more information here](/en/tutorials/access-my-observations) on managing your observations. ### **Why share your observations?** [Section titled “Why share your observations?”](#why-share-your-observations) By sharing your observations you: 1. **Contribute to science** You participate in the collection of botanical data on a global scale. Your data is used by researchers to better understand plant diversity, its evolution, and the threats facing it. 2. **Benefit from community experience** The Pl\@ntNet community can validate or correct your identifications, enrich your knowledge, and foster exchanges of experience. 3. **Improve the identification tool** Each shared observation enriches the database and improves the performance of the identification tool, making it more accurate and efficient. 4. **Support biodiversity preservation** By documenting plant diversity, you contribute to raising awareness and supporting conservation efforts for habitats and species. # Create and share in a group > Pl@ntNet groups let you organize observations, create species maps, share data, and collaborate. Create public or private groups, with or without geographical limits, to focus on specific species, areas, or themes. Easily find relevant data and share knowledge with other users. Groups on Pl\@ntNet allow users to group and organize their observations around species, geographical areas or specific themes, offering several advantages: * **Simplified access to relevant data**: Easily find observations related to your interests. * **Creation of observation maps**: Visualize the distribution of species in an area or within a taxonomic group. * **Sharing and exporting data**: Use the collected observations for scientific, educational or management projects. * **Collaboration between members**: Share knowledge with other users on topics of common interest. Groups can be configured to meet a variety of needs: * **Public or private**: Control access to the group. * **With or without geographical restriction**: Adapt the group to a specific location or leave it open to a wider audience. ### **How to create a group on Pl\@ntNet** [Section titled “How to create a group on Pl@ntNet”](#how-to-create-a-group-on-plntnet) 1. **Access the “Groups” tab** in the application. 2. Click on **“Create a group”**. 3. **Name and describe the group**: Choose a relevant name and add a clear description to explain its purpose or themes. 4. **Define a geographical area** (optional): If necessary, limit the group’s observations to a specific area. 5. **Check existing groups**: Before finalizing, perform a keyword search to ensure that a similar group does not already exist. 6. **Finalize the creation** and start inviting members to join your group. Groups are an excellent tool for strengthening exchanges and fostering collaboration within the Pl\@ntNet community. # Identify a disease or pest > Discover how to identify plant diseases and pests using Pl@ntNet. Starting March 2026, use the website or mobile app's specific flora tool to recognize common plant health issues through photos. Starting from March 2026, you will be able to identify diseases and pests affecting your plants. For more information on this feature, please [read this page](/en/understand/diseases-and-pests-identifcation). Identification of diseases and pests is available to everyone. ## On the website [Section titled “On the website”](#on-the-website) Available at ➡️ Simply drop images or select them after clicking on “Select up to 4 images”. ## On mobile [Section titled “On mobile”](#on-mobile) Prerequisite: version 3.25 To identify a disease on the mobile application, you must change the flora to select the specific flora “Diseases and pests”: 1. On the identification screen, click on the flora name at the top 2. Select “Specific floras” 3. Scroll down to tap on “Diseases and pests” Video tutorial ➡️  A few notes: * Anyone can identify diseases, but the sharing of annotated observations associated with a disease is very limited. * We will not provide methods for treating plants in order to protect ourselves from various legal issues related to country-specific regulations. # Identify a plant > Identify plants easily with Pl@ntNet! Take photos of plant organs (leaf, flower, fruit etc.), and Pl@ntNet offers possible identifications ranked by certainty. Explore images, consult species sheets, and share your observations to contribute to citizen science. Get accurate results with sharp, well-lit photos showing clear plant characteristics. ### **On the mobile version** [Section titled “On the mobile version”](#on-the-mobile-version) 1. From the **Identification** tab, take one or more photos of the plant or import them from your gallery. 2. Select the type of organ photographed (leaf, flower, fruit, etc.). 3. Pl\@ntNet will offer a list of possible identifications, ranked by order of certainty. ### **On the web version** [Section titled “On the web version”](#on-the-web-version) 1. Click on **Identify** and send one or more photos of the plant. 2. Specify the organs visible in your images. 3. Pl\@ntNet will generate a list of possible identifications, also ranked by order of certainty. ### **Explore and validate suggestions** [Section titled “Explore and validate suggestions”](#explore-and-validate-suggestions) From the suggestions provided by Pl\@ntNet, you can: * Explore the image galleries shared by other users. * Consult the species sheets to compare characteristics and refine your choice. **Once the species is identified, validate your choice and share your observation with the community.** **And if you hesitate to share your observations, keep in mind that:** * **Your data will be reviewed and validated by other users.** * **Only shared observations can contribute to citizen science projects and enrich the Pl\@ntNet database.** ### **Tips for optimizing your identifications** [Section titled “Tips for optimizing your identifications”](#tips-for-optimizing-your-identifications) * Take **sharp** and **well-lit** photos. * Photograph **several organs** of the plant (leaf, flower, fruit, etc.) to maximize accuracy. * Frame your photos so that the characteristics of the plant are **clearly visible**. # Install Pl@ntNet > Learn how to install Pl@ntNet on Android, iOS, or use the web version on your computer. Enhance your experience by creating an account and contributing to biodiversity research through participatory science. **To install the Pl\@ntNet app, follow these steps depending on your device:** ### **On a smartphone** [Section titled “On a smartphone”](#on-a-smartphone) 1. Open the **Google Play Store** (Android) or the **App Store** (iOS) on your phone. 2. Search for **“Pl\@ntNet”** in the search bar. 3. Tap the **Install** or **Download** button, then wait for the installation to complete. 4. Once installed, open the app and start exploring the biodiversity around you. 👉 [Tutorial: installing Pl@ntNet on your mobile](https://youtu.be/nBdOBM82GTg) ### **On a computer** [Section titled “On a computer”](#on-a-computer) Pl\@ntNet also exists as a web version: 👉 [identify.plantnet.org](http://identify.plantnet.org/) ### **Enhance your Pl\@ntNet experience** [Section titled “Enhance your Pl@ntNet experience”](#enhance-your-plntnet-experience) * **Create a user account**:\ This allows you to save your observations, access advanced features, and join a community of passionate users. * **Enable geolocation sharing**: By sharing your location, your observations become more accurate and contribute to geographical analyses useful for scientific research. * **Contribute to participatory science**: By sharing your data and observations, you actively contribute to biodiversity research projects. Your contributions enrich the global database and support initiatives to better understand and protect plants. 👉 [Link to the video “Why share your data?”](https://youtu.be/XMzKnmlnLYs) # Install the offline/embedded mode > Learn how to install and use Pl@ntNet in offline mode to identify plants without an internet connection. Follow our step-by-step guide to download embedded models and supplementary flora data for your mobile device. **Offline mode: Identify plants without an Internet connection** Offline mode is an optimized and compressed version of the Pl\@ntNet identification model, designed to allow you to identify plants even without an Internet connection. This model is exclusively available on the mobile application. ### **Preparing for offline mode** [Section titled “Preparing for offline mode”](#preparing-for-offline-mode) To use offline mode, you must first download the offline model. This base model provides lists of species without illustrations. You can enhance this experience by downloading compressed thumbnails of the floras corresponding to the regions or plant groups that interest you. These thumbnails will allow the model to provide identifications accompanied by images. ### **Steps to download and activate offline mode** [Section titled “Steps to download and activate offline mode”](#steps-to-download-and-activate-offline-mode) 1. **Activate embedded identification** Go to the home page of the **Identification** menu and activate the **Embedded identification** option. A user account will be required to download and use the offline mode. 1. **Download the offline model** Follow the instructions to download the embedded data. 1. **Add supplementary data** * Go to the **Supplementary data** section. * Click on **Add data to download**. * Select the floras you wish to integrate into the offline model. * Download the associated thumbnails. Once the download is complete, you will be able to activate and deactivate offline mode directly from the home screen, according to your needs. ### **Returning to online mode** [Section titled “Returning to online mode”](#returning-to-online-mode) When you switch back to online mode: * Your offline observations are accessible. * You can share them with the community. * It is recommended to re-run your identifications online, as the online model is updated more frequently and generally offers better performance. 👉[TUTO Install Plantnet and download offline mode](https://youtu.be/eFFNu48zai0) # Manage your user account > Manage your Pl@ntNet user account with ease. Learn how to log in, edit your profile, recover your password, and manage your personal information or account deletion on both mobile and web. > Learn how to log in, edit your profile, recover your password, and manage your personal information. ## Account creation [Section titled “Account creation”](#account-creation) Find information on [account creation and the benefits of a user account here](/en/tutorials/create-an-account-on-plantnet). ## Logging into your account [Section titled “Logging into your account”](#logging-into-your-account) **On the mobile app** Go to the **Profile** section at the bottom right of your app, then click on “Log in”. **On the web version** Simply click the green “Log in” button at the top right. ## Editing your profile information [Section titled “Editing your profile information”](#editing-your-profile-information) You can edit your information at any time. **On the mobile app** Go to the Profile section (👤), then click on your username at the top. **On the web version** Click on your name at the top right, then on “My account”. Once the changes are made, press “Update”. ## Changing or hiding your first and last name on observations [Section titled “Changing or hiding your first and last name on observations”](#changing-or-hiding-your-first-and-last-name-on-observations) You can change your first and last name displayed on future observations only. You cannot change the author’s name on your old observations; you will need to delete and re-upload them. To change your first and last name, please refer to the previous section. If you wish to remain anonymous, delete your first and/or last name from your profile. Your username will then appear instead of your real identity on your next shared observations. ## Recovering your password [Section titled “Recovering your password”](#recovering-your-password) **On the mobile app and on the web version** Once on the login page/screen, press “Forgot password?”. Enter your email address, then the green button below. You should receive an email within 5 minutes; please check your spam folder if necessary. ## Deleting your account [Section titled “Deleting your account”](#deleting-your-account) To delete your account, open your profile following the previous instructions, then click on “Delete my account”. Confirm your password, then click “Delete my account”. ⚠️ Once your account is deleted, your data will be anonymized and all your old observations will still be visible, but without your first/last name or username. If you wish for all your data to be completely deleted, please contact us. # Report a bug > Learn how to report a bug to Pl@ntNet by providing a detailed description, error messages, and platform details. Help improve Pl@ntNet by sending your report via email. > ℹ️ Before reporting a bug to us, please check that all our services are available from [this page](https://plantnet.github.io/status/). To help us fix a bug, please include the following information in your report: * A precise description of the problem encountered and how to reproduce it * The error message(s) displayed * A screenshot illustrating the bug, if possible. * The platform you are on: Chrome, Edge, Android, iOS, etc. * For mobile only: the version of the Pl\@ntNet application (found at the very bottom of the **Options** section). You can send this information by e-mail to contact\[at]plantnet-project.org Ideally in English or French. # Diseases and pests identifcation > Pl@ntNet introduces a new feature to identify plant diseases and pests from images, helping users detect symptoms like leaf spots, discoloration, and infestations. Available on the website and mobile app, this tool provides informative advice on plant health across dozens of species using advanced computer vision technology. We are delighted to introduce a new feature in the Pl\@ntNet application: the ability to **identify plant diseases and pests** from images. This **first version** aims to help users detect visible symptoms caused by common diseases or pests on a limited set of plant species. By uploading photos showing signs of disease or infestation (spots on leaves, discoloration, deformations, or the presence of insects), users can receive preliminary suggestions about potential issues affecting the plant. Up to four photos can be sent; do not hesitate to take several photos of the signs of disease to improve the results. While it is intended to grow and improve over time, it already constitutes a solid basis for supporting users in observing and understanding plant health. New diseases and pathogens, as well as new plant species, will be gradually added. Disease identification results should be considered as **informative advice**, and not as a professional diagnosis, and are not intended to provide information on treatments (legally, this often depends on the governments of each country). ## How can I access this new feature? [Section titled “How can I access this new feature?”](#how-can-i-access-this-new-feature) Identification of diseases and pests is available to everyone: * on the website: * on the mobile application (v3.26 minimum): by choosing the “Diseases and pests” flora in the “Specific floras” sub-menu Disease observation contributions are currently very limited. If you are a professional in certain diseases and want to improve identification performance, please fill out the following form: . Please feel free to detail your skills and profession so that we can respond to your request as quickly as possible, usually within 3 working days. ## Going further [Section titled “Going further”](#going-further) ### Help us cover more species: share your datasets! [Section titled “Help us cover more species: share your datasets!”](#help-us-cover-more-species-share-your-datasets) If you are an expert Pl\@ntNet user and have access to high-quality images showing plant diseases or pests (with reliable annotations), we would love to receive your help! The contribution of these datasets can significantly improve the accuracy and coverage of our new disease identification feature. If you wish to share data or collaborate, please do not hesitate to contact us (by mentioning it in the comment field of the form) - we are delighted to co-build this feature with the community. ### Which species are already covered? [Section titled “Which species are already covered?”](#which-species-are-already-covered) We are continuously importing new data to cover a larger number of species, so remember to visit this page for an updated overview of all currently supported species: As an example, by March 2026, we already covered the following species: * *Allium ampeloprasum* - Wild Leek * *Allium cepa* - Onion * *Allium sativum* - Garlic * *Ananas comosus* - Pineapple * *Asparagus officinalis* - Asparagus * *Avena sativa* - Common oat * *Beta vulgaris* - Beet * *Brassica napus* - Rapeseed * *Brassica oleracea* - Romanesco broccoli * *Capsicum annuum* - Bell pepper * *Carica papaya* - Papaya * *Castanea sativa* - Sweet chestnut * *Cicer arietinum* - Chickpea * *Citrullus lanatus* - Watermelon * *Citrus × aurantiifolia* - Key lime * *Citrus × aurantium* - Bitter orange * *Citrus × latifolia* - Persian lime * *Citrus × limon* - Lemon * *Citrus hystrix* - Kaffir lime * *Citrus maxima* - Pomelo * *Citrus medica* - Buddha’s hand * *Coffea arabica* - Arabian coffee * *Corylus avellana* - Hazel * *Cucumis melo* - Melon * *Cucumis sativus* - Cucumber * *Cucurbita pepo* - Field pumpkin * *Daucus carota* - Wild carrot * *Foeniculum vulgare* - Fennel * *Glycine max* - Soybean * *Helianthus annuus* - Sunflower * *Hordeum vulgare* - Barley * *Humulus lupulus* - Common hop * *Juglans regia* - English walnut * *Lactuca sativa* - Lettuce * *Lathyrus oleraceus* - Pea * *Lens culinaris* - Lentil * *Mangifera indica* - Mango * *Medicago sativa* - Alfalfa * *Musa × paradisiaca* - Plantain * *Musa acuminata* - Wild banana * *Musa balbisiana* - Wild banana * *Nicotiana tabacum* - Tobacco * *Persea americana* - Avocado * *Phaseolus vulgaris* - Common bean * *Pisum sativum* - Pea * *Prunus armeniaca* - Apricot * *Prunus domestica* - Plum * *Prunus dulcis* - Almond * *Prunus persica* - Peach * *Pyrus communis* - Common pear * *Raphanus raphanistrum* - Wild radish * *Ribes nigrum* - Blackcurrant * *Rubus idaeus* - Raspberry * *Solanum lycopersicum* - Tomato * *Solanum melongena* - Eggplant * *Solanum tuberosum* - Potato * *Sorghum bicolor* - Sorghum * *Spinacia oleracea* - Spinach * *Triticum aestivum* - Bread wheat * *Vanilla planifolia* - Vanilla * *Vicia faba* - Broad bean * *Vitis vinifera* - Grapevine * *Zea mays* - Maize ### Explore observations of diseased plants [Section titled “Explore observations of diseased plants”](#explore-observations-of-diseased-plants) With this new feature, Pl\@ntNet users will also be able to **explore community observations of plants affected by various diseases and pests**. These real-world examples, provided by other users and institutes, provide valuable insights into how different symptoms appear on specific species. Whether you are looking to identify a problem or learn more about plant health, this growing collection of annotated images will help you recognize and compare visible signs of stress in plants.  ### Identify plants affected by diseases or pests [Section titled “Identify plants affected by diseases or pests”](#identify-plants-affected-by-diseases-or-pests) In addition to searching for observations, this feature allows you to **identify specific diseases and pests directly from your own images**. By uploading a photo that clearly shows symptoms - such as spots, wilting, discoloration, or the presence of insects - Pl\@ntNet will analyze the image and suggest possible causes. These suggestions are based on visual similarities to verified cases in our database and aim to help you quickly understand what may be affecting the plant. Disease identification is available at the following address: [identify.plantnet.org/diseases-and-pests](http://identify.plantnet.org/diseases-and-pests) and works almost exactly like the classic Pl\@ntNet identification you are used to. Pl\@ntNet also suggests the different host species for the diseases in question.  For now, disease identification cannot be shared on the platform like regular plant observations to prevent potential identification errors from spreading in our datasets (unless your profession has led you to contact us, see above). ### Annotate existing plant observations with diseases [Section titled “Annotate existing plant observations with diseases”](#annotate-existing-plant-observations-with-diseases) Once the disease or pest is selected, it **annotates the plant observation with the disease or pest**. If you recognize symptoms on an image or if you know the specific problem affecting the plant, you can add or suggest annotations to enrich the database. As mentioned previously and in order to improve identification performance, only a small number of users can contribute to these annotated observations. These contributions are essential for improving the accuracy of the identification feature and building a more comprehensive resource for the entire Pl\@ntNet community. Each image of an observation can receive a set of annotations. To annotate an image, select “Phytopathogens” annotations in the Annotations section next to the image and search for a specific value. If the disease you identify on your image is not in the list and you have more than 10 images illustrating it, please contact us [here](https://plantnet.org/#contact).  ## Research effort behind this feature [Section titled “Research effort behind this feature”](#research-effort-behind-this-feature) This new disease and pest identification feature is the result of ongoing **research on phytosanitary diagnosis using computer vision and deep learning techniques**. It builds on recent advances in image-based plant pathology and benefits from expert-created datasets to train and evaluate our models. This collaboration between science and citizen observation is at the heart of Pl\@ntNet’s mission: transforming collective intelligence into actionable tools for monitoring biodiversity and plant health. We especially thank the participation of EPPO, INRAE, Cirad, and IRD. # Floras > Learn how Pl@ntNet uses geographical and thematic floras to improve plant identification accuracy. Discover how selecting the right flora filters results by region and theme while contributing to local biodiversity knowledge. Pl\@ntNet performs its searches within floras or themes that are either automatically suggested if your GPS is enabled, or that you can define yourself. These floras correspond to lists of species specific to each region or theme. ### Why select a flora or theme in Pl\@ntNet? [Section titled “Why select a flora or theme in Pl@ntNet?”](#why-select-a-flora-or-theme-in-plntnet) 1. **Adaptation to geographical regions:** * Plant distribution varies considerably depending on the geographical region. * By choosing a specific flora, you tell the application which plant species are likely to be present in your area. * This allows Pl\@ntNet to filter results based on local flora, thereby improving the accuracy of identifications. 2. **Reduction of errors:** * Without selecting a specific flora, the application may generate more possible results, including plants irrelevant to your region. * By selecting the appropriate flora, you reduce the risk of obtaining incorrect identifications. 3. **Enriching knowledge of local floras:** * By choosing a flora, you contribute to enriching knowledge of local biodiversity. * Shared data can be consulted in the feed and easily reviewed by specialists of the flora concerned. ### Types of Entries for Floras in Pl\@ntNet [Section titled “Types of Entries for Floras in Pl@ntNet”](#types-of-entries-for-floras-in-plntnet) 1. **Geographical entry:** * Search within the list of plants native to an area. * Relevant for identifying wild plants, less useful for ornamental plants whose origin may differ from where they are cultivated. 2. **Thematic entry:** * Based on various subjects such as useful plants or invasive plants. 3. **Microproject entry:** * Contextualization of the application for a defined theme or geographical area. * Searches are carried out only on a list of plants defined within the framework of the project, covering national parks, botanical gardens, or sensitive themes such as the desert locust biotope. **The most widely used taxonomic reference for defining floras in Pl\@ntNet** is the one provided by the Royal Botanic Gardens, Kew, called [**Plants Of The World Online (POWO**](https://powo.science.kew.org/)**)**. Some floras and microprojects are still on old reference systems, which causes issues with species names during identification. Their reference systems are expected to be updated in 2026, but as of October 8, 2025, here is the list of the floras in question: * Useful plants * Weeds * Invasive plants * Useful plants of tropical Africa * Useful plants of Asia * Les Ecologistes de l’Euzière * Provence * LEWA in KENYA * Ordesa * Cévennes * Mediterranean ornamental trees * Crops * Trees of Europe * Desert Locust Biotopes in West Africa * Remarkable flora of the Alpes-Maritimes * ESALQ and Piracicaba trees * Brazilian Amazon * Trees of the Brazilian Amazon * Domaine Saint Jacques du Couloubrier * Sugar cane weeds in Australia * Albert-Kahn Departmental Museum Garden * Guanacaste Conservation Area, Costa Rica * Gardens by the Bay - Cloud Forest * Gardens by the Bay - Flower Dome # Governance > Pl@ntNet is managed by a consortium of leading research organizations, including CIRAD, Inria, INRAE, CNRS, and IRD. This governance structure ensures the platform's long-term sustainability and scientific development through specialized committees and strategic partnerships. Pl\@ntNet’s governance is provided by a consortium made up of five research organizations ([CIRAD](https://www.cirad.fr/), [Inria](https://www.inria.fr/), [INRAE](https://www.inrae.fr/), [CNRS](https://www.cnrs.fr/fr) and [IRD](https://www.ird.fr/)), the [Université de Montréal](https://www.umontreal.ca/) and the [Inria Foundation](https://www.fondation-inria.fr/). The management of the consortium is part of the [InriaSOFT](https://www.inria.fr/fr/inriasoft-pour-la-diffusion-des-logiciels-open-source) framework, a program for the long-term sustainability of digital achievements by Inria and its partners. The main governance bodies are the executive committee (in charge of strategic and administrative decisions), the scientific and technical committee (in charge of the technical roadmap) and the office (in charge of the day-to-day coordination of the platform). Since 2021, Pl\@ntNet governance has been open to other members such as research organizations, local authorities, public governance bodies or natural area managers. Membership in the consortium is available under 3 statuses, more information at . # How the identification model works > Pl@ntNet uses AI, specifically Vision Transformers, to identify plants from images. Community validation and taxonomic databases improve accuracy, with user contributions refining the model over time. The system provides a confidence score for each identification. The Pl\@ntNet identification model is designed to help users identify plants from images. It relies on a combination of **artificial intelligence (AI)** and **human contributions** to achieve the best possible results. Here’s how it works: ### 1. **Taking photos** [Section titled “1. Taking photos”](#1-taking-photos) The user takes one or more images of the plant they wish to identify. These images ideally illustrate different parts of the plant such as leaves, flowers, fruits and bark. The clearer and more varied the images, the more likely the model will be able to make an accurate identification. ### 2. **Initial analysis by AI** [Section titled “2. Initial analysis by AI”](#2-initial-analysis-by-ai) Pl\@ntNet’s artificial intelligence is based on **image recognition models** capable of comparing photos sent by users to a vast database of plant species. Initially, Pl\@ntNet used **convolutional neural networks (CNNs)**, a classic deep learning method particularly effective for processing images. CNNs analyze images by extracting important visual features, such as leaf shape, flower color, or stem texture. These features are then used to suggest similar species. However, CNNs have some limitations and Pl\@ntNet has made a major technological leap by adopting **Vision Transformers (ViT)**. Unlike CNNs, which process images by locating and analyzing parts of the image from small windows, Vision Transformers process the entire image using an attention-based approach, a method inspired by models used in natural language processing. **Vision Transformers** are particularly well-suited to analyzing complex images and fine details on plants, such as the precise shape of leaves or the configuration of flowers. This approach allows the model to be more accurate and robust, especially when it comes to identifying plant species that share similar characteristics but differ in subtle details. Vision Transformers are therefore a major advance in the accuracy and reliability of identifications made by Pl\@ntNet’s AI. Thanks to this evolution, the AI model has become more powerful and capable of processing more complex images with better accuracy. This allows Pl\@ntNet to offer even more reliable plant identifications, even for species that are difficult to distinguish visually. ### 3. **Community validation** [Section titled “3. Community validation”](#3-community-validation) However, AI alone is not enough and the participation of the **Pl\@ntNet community** in validating automatic identifications is essential. Users can review the suggestions made by the AI and validate or correct the identifications. If several users confirm an identification, it becomes more reliable. ### 4. **Continuous model improvement** [Section titled “4. Continuous model improvement”](#4-continuous-model-improvement) Each time a user validates an identification or corrects an error, this information is used to improve the AI model. In other words, the AI learns from its mistakes thanks to human contributions, which increases the accuracy of future identifications. ### 5. **Use of taxonomic databases** [Section titled “5. Use of taxonomic databases”](#5-use-of-taxonomic-databases) The identification model uses **taxonomic databases** to verify that a plant name is valid. These databases contain reliable information about species and help avoid errors in identifications. ### 6. **Identification reliability** [Section titled “6. Identification reliability”](#6-identification-reliability) The model also calculates a **confidence score** for each identification. This score reflects how confident the AI is in its identification. The higher the score, the more reliable the identification. If the identification does not reach a certain confidence threshold, it will probably be rejected or require further review. ### 7. **Contribution to the database** [Section titled “7. Contribution to the database”](#7-contribution-to-the-database) When the identification is validated, it is added to the Pl\@ntNet database. This data is then used to enrich the platform and contribute to scientific research on biodiversity. ### 8. **Interaction with other users** [Section titled “8. Interaction with other users”](#8-interaction-with-other-users) In addition to automatic identification, Pl\@ntNet allows users to **vote** to validate or reject identification suggestions made by others. This creates a collaborative process that constantly improves the quality of identifications. ### In summary [Section titled “In summary”](#in-summary) The Pl\@ntNet identification model works thanks to a combination of artificial intelligence, community reviews, and taxonomic databases. Each observation is first analyzed by the AI, then validated or corrected by the community. The AI learns from these interactions to become more accurate over time, thus creating a collaborative and evolving system for plant identification. # Machine learning and model training > Pl@ntNet uses deep learning and millions of user-submitted images to identify plants. Advanced models like Vision Transformers improve accuracy, especially for rare species. Regular challenges and rigorous testing ensure reliability and contribute to global biodiversity preservation. Pl\@ntNet is a collaborative application dedicated to plant identification, used by millions of people worldwide. Artificial intelligence (AI), and more specifically deep learning, is at the heart of Pl\@ntNet’s operation. ### The principle of deep learning and images [Section titled “The principle of deep learning and images”](#the-principle-of-deep-learning-and-images) Deep learning is a branch of artificial intelligence where “neural networks” learn to recognize patterns in data, here images. By analyzing millions of photos of plants, a computer model can learn to differentiate species based on their visual characteristics: leaf shape, flower color, etc. Pl\@ntNet relies on millions of images collected by users and experts. These photos are sorted, validated, and organized to train deep learning models. The more images and diversity there are, the more accurate the model becomes! ### Recent progress thanks to advanced models [Section titled “Recent progress thanks to advanced models”](#recent-progress-thanks-to-advanced-models) Today, Pl\@ntNet uses a cutting-edge technology called Vision Transformer (ViT). These models, such as the one from DINOv2, make it possible to take advantage of the large quantities of available images while improving the recognition of rare or poorly photographed plants. In addition to being trained on photos provided by the community, the model is evaluated on very specific test images. These images, often from experts and not accessible online, serve to ensure that the system is reliable, even for plants that are difficult to identify. ### The importance of challenges and tests [Section titled “The importance of challenges and tests”](#the-importance-of-challenges-and-tests) Pl\@ntNet organizes the PlantCLEF challenge every year, an event where researchers and engineers test the latest advances in plant identification. These competitions allow different approaches to be compared and the performance of the models to be improved. For example, at PlantCLEF2022, a challenge based on 4 million images showed that new architectures, such as Vision Transformers, surpass older systems based on convolutional neural networks. Internally, Pl\@ntNet also carries out rigorous tests. “Sanctuary” sets of images, selected for their quality and rarity, make it possible to verify that the model correctly recognizes complex or under-represented species. ### Why all this is important [Section titled “Why all this is important”](#why-all-this-is-important) Thanks to these advances, Pl\@ntNet is not just a practical tool for botany enthusiasts. It contributes to the preservation of biodiversity by helping to document plants on a global scale. Each photo shared enriches the database and strengthens the model’s capabilities, thus creating a virtuous cycle for science and nature. With this technology, everyone can participate in better understanding and protecting the plants around us! # Observations validation > Discover how Pl@ntNet ensures data quality through a rigorous observation validation process. Learn about user voting weights, identification criteria, and how high-quality contributions support biodiversity research and improve AI models. Observation validation is a fundamental pillar of Pl\@ntNet, ensuring the quality of shared data and its relevance for scientific research. This process combines human contributions and automated calculations to assess the reliability of each observation. ### **Votes** [Section titled “Votes”](#votes) 1. **User votes** Pl\@ntNet users actively participate by voting on observations. Their contribution is weighted by their [“weight”](/en/understand/users-ranking), which reflects their experience and reliability within the application. 2. **Partner votes** Partners, such as institutions or associated experts, have votes with a fixed weight. 3. **Calculation of Scores and Probabilities** * **Value score**: This is the sum of the weights of users who voted for a species. * **Value probability**: This is calculated by dividing the score of that value by the sum of the scores of all values proposed for the observation. ### **Observation Validation Criteria** [Section titled “Observation Validation Criteria”](#observation-validation-criteria) To be validated, an observation must satisfy several conditions: 1. **Valid image** * The image must not be marked as “not a plant.” * It must obtain a quality score of at least 2. 2. **Non-malformed observation** * It must not contain images of several distinct species (“malformed”). * The “malformed” vote score must be less than 2. 3. **Correct determination** * The determination score must be at least 2. * The probability of the determination must be greater than or equal to 0.7. 4. **Absence of censorship** * The observation must not be censored by the identification engine. 5. **Other criteria** * The user who created the observation must not be blocked. * All mandatory fields must be completed (for example, for partner observations). * Images must include identifiable organs (such as leaves, flowers, or fruits). ### **Special Cases** [Section titled “Special Cases”](#special-cases) An observation can be validated even if the identified species is not yet indexed in Pl\@ntNet’s taxonomic databases. These observations will appear in contribution feeds and user profiles but will not appear in the image galleries. If the species is added later, the observation will be automatically linked to the corresponding gallery. ### **Importance of Image Quality** [Section titled “Importance of Image Quality”](#importance-of-image-quality) To maximize the chances of validation, it is essential to provide high-quality images: * Take several photos of distinctive organs (leaves, flowers, fruits, etc.). * Ensure images are sharp and well-framed. * Enable geolocation to enrich the observation with precise contextual data. ### **Impact of Validated Observations** [Section titled “Impact of Validated Observations”](#impact-of-validated-observations) Validated observations feed into open databases like GBIF, contributing to research on biodiversity, ecology, and computer science. They also participate in the continuous improvement of Pl\@ntNet’s artificial intelligence models, thereby increasing the accuracy of future identifications. This rigorous process ensures that the collected data is of high quality, reliable, and usable for research, while encouraging collaboration among users. # Pl@ntNet, a citizen science project > Pl@ntNet is a global citizen science project using AI to identify plants. Millions contribute observations, improving plant recognition and building a massive biodiversity database used by researchers worldwide. Join the community and help map plant species! Pl\@ntNet is a citizen science platform that uses artificial intelligence (AI) to facilitate the identification and inventory of plant species. It is one of the largest biodiversity observatories in the world, with several million contributors in more than 200 countries. Pl\@ntNet is based on a principle of cooperative learning. Users who have created an account can share their observations, which can then be reviewed by the community. This data is also used by the AI to improve plant recognition. For example, it is possible to confirm the name of a species or suggest another identification if one has botanical knowledge. Only observations that have reached a sufficient degree of confidence are then added to the public database and used to train the AI. The tool is thus co-constructed by and for citizens. The most qualified users contribute their expert knowledge by contributing and reviewing observations, while less experienced users benefit from this knowledge through the use of the Pl\@ntNet application. Thanks to the participation of everyone, Pl\@ntNet has collected more than one billion plant images (see the [statistics](https://identify.plantnet.org/fr/stats))! However, only a small portion of this data is ultimately shared with researchers worldwide via open data portals on biodiversity, such as the [GBIF](https://www.gbif.org/) or [OpenObs](https://openobs.mnhn.fr/) of the INPN. A key element for this use is the presence of geolocation. Indeed, this information is crucial for mapping species. So don’t forget to share your geolocation if you want to fully contribute to the participatory science project. # Pl@ntNet Microprojects > Pl@ntNet microprojects offer specialized adaptations for specific floras, locations, or themes to improve plant identification accuracy. Learn how to implement a project, manage observation data, and coordinate with partners to support biodiversity exploration. A microproject is a specific adaptation of the Pl\@ntNet tool to a particular flora, defined by a precise geographical location or a dedicated theme. By joining a microproject, you focus your searches on a list of associated species, which guarantees more accurate results better suited to your needs. Furthermore, microproject leads can efficiently collect, analyze, and manage all observations shared within this framework. When a microproject is geolocated, the application can automatically offer access to users located within the defined perimeter, provided that geolocation is enabled. This feature promotes a targeted exploration of plant biodiversity. ### **How to implement a microproject?** [Section titled “How to implement a microproject?”](#how-to-implement-a-microproject) 1. **Prepare a list of species**: Implementing a microproject requires access to the list of species of interest for the project. This list, including the Latin names of families, genera, and species, allows for an assessment of the number of species already illustrated in Pl\@ntNet. It constitutes an essential step in defining the microproject’s objectives. 2. **Necessary investment**: The creation of a microproject requires a financial investment to cover the costs of implementation and maintenance. We invite our partners to co-finance these costs according to the following scale: * **Implementation cost**: €2,000 + (€5 x number of species). * Example: A microproject covering 200 species will cost €3,000 (€2,000 + (€5 x 200)). This amount covers the engineering time required for data formatting and integration. * **Annual maintenance cost**: * €1,000 per year for projects up to 1,000 species. * An additional €1 per species beyond 1,000 species. * Example: A microproject of 2,000 species will have an annual maintenance cost of €2,000. # Pl@ntNet's strengths and weaknesses > Pl@ntNet is a free, AI-powered plant identification app and website with a global community. While offering accurate identification and contributing to scientific research, it faces challenges like data validation, unequal contribution quality, and limited taxonomic coverage. ### **Strengths** [Section titled “Strengths”](#strengths) 1. **An accessible and collaborative tool**: Pl\@ntNet is a free platform, open to all, and available as a mobile app and web version. It allows millions of people worldwide to contribute to plant identification and inventory. 2. **Power of artificial intelligence**: The integration of advanced AI models, such as vision transformers, offers fast and accurate plant identification, with continuous improvement thanks to the collected data. 3. **Scientific and environmental impact**: Validated observations feed open databases like GBIF, contributing to research on biodiversity, ecosystem conservation, and the fight against climate change. 4. **International dimension**: With contributors in over 200 countries, Pl\@ntNet covers a wide diversity of floras, making it one of the largest biodiversity observatories in the world. 5. **Education and awareness**: Pl\@ntNet encourages citizens to learn more about the flora around them and to participate in data collection. ### **Weaknesses** [Section titled “Weaknesses”](#weaknesses) 1. **Lack of human resources to validate observations**: Despite an active community, the volume of observations often exceeds validation capacities. This can slow down the integration of new reliable data into the database. 2. **Limited participation**: Many users do not share their observations or choose not to activate geolocation, which limits the usefulness of the data for biodiversity monitoring. 3. **Unequal quality of contributions**: Some observations are blurry, poorly framed, or lack essential information (plant organs, geolocation), which complicates their validation. 4. **Presence of errors in the data**: Although Pl\@ntNet relies on a collaborative system and quality control, identification or location errors may remain. Community contribution to detect and correct these errors is essential. 5. **Partial taxonomic coverage**: Pl\@ntNet currently covers approximately 70,000 species, a small fraction of the 350,000 known plant species. Although the database is growing every day, there is still a long way to go to cover all plant biodiversity. 6. **Lack of technical and financial resources**: As with many scientific and participatory projects, the resources available to improve tools, expand taxonomic coverage, and strengthen teams are insufficient compared to the project’s ambitions. ### Collective motivation at the service of biodiversity [Section titled “Collective motivation at the service of biodiversity”](#collective-motivation-at-the-service-of-biodiversity) Despite the challenges, our team and our community remain firmly committed to making Pl\@ntNet an accessible tool for all, built by all. Each contribution, whether a shared photo, a vote, or a validated observation, is a stone added to the edifice of a common project serving science, education, and the preservation of biodiversity. Together, we are moving forward every day to fill the gaps, push the boundaries, and strengthen Pl\@ntNet as an essential resource for better understanding and protecting the nature around us. # Species list > Explore the species list available in Pl@ntNet, including coverage details and limitations. Learn about WCVP taxonomic references, model update schedules, and what to do if a species is not recognized. All species illustrated in Pl\@ntNet and listed in the [WCVP repository](/en/reference/powo-taxonomic-reference) are available for identification and entry. The complete list is available [here](https://identify.plantnet.org/en/k-world-flora/species). Here are some details: 1. Plant species not covered: **no algae, mosses, liverworts, or lichens are available in Pl\@ntNet.** 2. Fungi are not available in Pl\@ntNet; there are no plans to add them in the short or medium term. 3. If a species is newly illustrated, the identification engine must be updated (re-trained) in order to recognize it. Depending on the team’s projects, this is done every 3 to 12 months. The version of the identification model is indicated at the very bottom of the [website](https://identify.plantnet.org/), or at the bottom of the mobile app options (CNN: YYYY-MM-DD). ## Species not recognized [Section titled “Species not recognized”](#species-not-recognized) When manually entering a species, it may happen that the species is not visible. This may be due to: 1. Certain filters may be active (illustrated species only, species not present in the current [flora](/en/understand/floras)). 2. The version of [WCVP](/en/reference/powo-taxonomic-reference) used in Pl\@ntNet is not up to date. 3. The name used is a synonym not present in Pl\@ntNet. 4. [WCVP](/en/reference/powo-taxonomic-reference) does not include this species (in this case, you can contact [Kew](https://powo.science.kew.org/)). # Terms of use > Pl@ntNet's terms of use govern access and use of the platform. See the full terms for details. All information [here](https://identify.plantnet.org/fr/terms_of_use) # Understanding identification errors in Pl@ntNet > Discover how Pl@ntNet identifies and manages identification drifts, where confusion between similar species can become self-sustaining. Learn about the vital role of community 'watchdogs' and statistical analysis in monitoring approximately 1,500 affected species to ensure data accuracy. In 2026, Pl\@ntNet recognizes approximately 85,000 species. In some cases, an identification error can reinforce itself over time: a common species is regularly confused with a similar-looking rare species, and these new observations then contribute to maintaining the confusion. For several years, experienced members of the community, known as *veilleurs* (watchdogs), have been spotting and documenting these situations. Crossing their work with a statistical analysis allows us to better understand the phenomenon and provide an initial order of magnitude: approximately **1,500 species could be affected**, out of the roughly 85,000 species recognized by Pl\@ntNet. This estimate remains uncertain and depends on several hypotheses. ## How a drift can appear [Section titled “How a drift can appear”](#how-a-drift-can-appear) The Pl\@ntNet identification system relies notably on observations validated by the community. The more data available and correctly identified, the more it can contribute to improving the model. But when confusion sets in between two closely related species, this mechanism can also amplify an error. Take a common species, represented by thousands of observations, and a rare species that resembles it. If the model starts suggesting the rare species too often instead of the common one: 1. some observations of the common species are recorded under the name of the rare species; 2. these observations increase the amount of data associated with the rare species; 3. the model progressively learns this confusion and may suggest the rare species even more frequently. Since rare species generally have fewer observations, a relatively small amount of erroneous data can have a significant effect on their representation. The watchdogs call this **identification drift**: an error that does not remain isolated but can become self-sustaining. ## The role of the watchdogs [Section titled “The role of the watchdogs”](#the-role-of-the-watchdogs) Watchdogs are experienced users (confirmed botanists or very knowledgeable amateurs) who spot drifts and participate in their correction through the collaborative validation system. Over the years, their work has made it possible to build a catalog of documented cases: confused species, the evolution of the problem, and corrections made. Approximately **190 species** have been studied in detail. This expertise is particularly important because an unusual statistical trend alone is not enough to prove that a drift exists. Interpreting the data requires knowledge of the species and their context. ## Sources, sinks and underdogs [Section titled “Sources, sinks and underdogs”](#sources-sinks-and-underdogs) Watchdogs use several terms to describe the different cases encountered. A **source** is generally a common species that becomes less frequently correctly recognized. Its observations are progressively attributed to another species. A **sink** is the species that incorrectly receives these observations. It is often a rare species, whose observation database can be quickly affected by these errors. An **underdog** is also a rare and under-recognized species, but without a drift necessarily being involved. It may simply lack observations or be difficult to identify. Watchdogs often favor an approach called **asymmetric curation**: they prioritize cleaning the sink’s data. Correcting a small, heavily contaminated database can be more effective than directly correcting a large database of observations. ## What the statistical analysis shows [Section titled “What the statistical analysis shows”](#what-the-statistical-analysis-shows) An independent analysis studied **795 species with more than 500 observations**, representing about 2.5 million observations between 2017 and 2024. For each species, the analysis estimates whether its probability of being detected by users increases or decreases over time. This evolution is represented by a slope: * a **negative slope** indicates that the species is being detected less and less; * a **positive slope** indicates that it is being detected more and more. The hypothesis is that a source should generally show a negative slope, while a sink might show a positive slope. The cross-referencing with cases already known by the watchdogs yields a mixed result. For **sources**, negative slopes seem to be a good detection tool: about 80% of known sources appear in this list, with an estimated purity of about 74%. For **sinks**, the result is much less useful. Only about 13% of known sinks are found. The main reason is that sinks are often rare and have fewer than 500 observations: they are therefore excluded from the analysis from the start. Slopes can therefore help **prioritize species to be examined**, but they do not replace the expertise of the watchdogs. ## Why corrections sometimes complicate the analysis [Section titled “Why corrections sometimes complicate the analysis”](#why-corrections-sometimes-complicate-the-analysis) Curation itself can modify the statistics used to detect drifts. When a sink is cleaned, a portion of the removed observations is often re-validated as belonging to the source. The source then gains observations, while the sink loses them. Over time, this can change their respective slopes: * a source that has already been corrected may no longer appear as a species in decline; * a cleaned sink may, conversely, appear among species with a negative slope. It is therefore important to keep a history of corrections. A species already identified as a source may remain vulnerable to new drifts, even after being corrected. ## What could be the scale of the phenomenon? [Section titled “What could be the scale of the phenomenon?”](#what-could-be-the-scale-of-the-phenomenon) By combining the cases documented by the watchdogs and the results of the statistical analysis, the following estimates are obtained: * approximately **450 sources**; * approximately **1,000 sinks**, with a more uncertain estimate between 600 and 1,300 depending on the method; * approximately **1,500 species affected in total** by identification drift. For comparison, Pl\@ntNet recognizes about 85,000 species. These figures should be interpreted with caution. The watchdogs’ catalog is not a random sample and focuses primarily on European flora. The statistical analysis only covers species with at least 500 observations and is based on a period from 2017 to 2024. These results therefore provide an order of magnitude rather than a definitive measurement of the phenomenon. ## Several questions remain open. [Section titled “Several questions remain open.”](#several-questions-remain-open) Can a species be a source in one region and a sink in another? Can we identify the precise moment a drift begins? Is it possible to find sinks starting from already known sources? In the longer term, the goal could be to develop tools capable of automatically flagging species that are beginning to drift. These tools would not replace human expertise but could help watchdogs spot problems earlier. Identification drifts are a real phenomenon affecting a limited portion of the species recognized by Pl\@ntNet. The work of the watchdogs is essential for spotting, understanding, and correcting them. Statistical analysis, for its part, provides an additional means of identifying species that deserve special attention. By combining botanical expertise and data analysis, Pl\@ntNet has a better foundation for monitoring these drifts and intervening before they become more significant. ### See also [Section titled “See also”](#see-also) * [The Pl@ntNet “watchdogs”: towards a new role for coordinating the community in error correction](https://plantnet.org/2026/06/22/les-veilleurs-de-plntnet-vers-un-nouveau-role-pour-coordonner-la-communaute-dans-la-correction-derreurs/) * [Contact Pl@ntNet](https://plantnet.org/#contact) # Users ranking > Discover how user ranking and weights work on Pl@ntNet. Learn how your contributions, including species observations and votes, increase your influence and reliability status within the community. In Pl\@ntNet, every user has a **weight** that reflects their level of contribution and expertise. This weight is used to rank users and determine their influence on the platform. ### 1. Initial weight [Section titled “1. Initial weight”](#1-initial-weight) When you sign up for Pl\@ntNet, you start with a **base weight**. This initial weight is relatively low, but it increases as you contribute. ### 2. How weight is calculated [Section titled “2. How weight is calculated”](#2-how-weight-is-calculated) A user’s weight depends on two main elements: * **Species observations**: If you make observations and they are validated (meaning the species identification is correct and the species name is valid), this increases your weight. * **Votes on other users’ observations**: If you vote on others’ observations, this also contributes to your weight, but to a lesser extent than your own observations. Votes on observations already validated by others are taken into account, provided the species name is correct. ### 3. Adjusted weight [Section titled “3. Adjusted weight”](#3-adjusted-weight) Your weight can be adjusted based on your activity: * **Weight for observations**: Your observations count more than your votes. This means that if you add quality observations, your weight will increase faster than if you only vote. * **Weight for votes**: Your votes are taken into account, but they have a smaller impact than your observations. Indeed, Pl\@ntNet considers that users who add new observations are more active and engaged than those who only vote. ### 4. Special cases [Section titled “4. Special cases”](#4-special-cases) * **Fixed weight**: Some users may have a weight set manually by administrators, generally for those who do not follow the platform’s rules or who make incorrect contributions. * **Minimal weight**: New users, particularly those who already have botanical knowledge, may be assigned a minimal weight to prevent them from starting with a weight that is too low. However, their actual weight can never be lower than this minimal weight. ### 5. Validity of observations [Section titled “5. Validity of observations”](#5-validity-of-observations) For an observation to be considered valid, it must meet several criteria: * It must contain at least one correct image (meaning an image that is not blurry and corresponds to the identified species). * It must not be marked as invalid or “noplant” by the community. * It must have a correct identification and a sufficient identification probability. * Other technical criteria, such as the user not being blocked, can also influence validity. In summary, the more you contribute by adding valid observations and voting relevantly, the more your weight will increase, and the more you will be recognized as an active and reliable user on the platform. # Using and sharing data in Pl@ntNet > Learn how to access, export, and share your observations in Pl@ntNet. Understand how your data contributes to biodiversity research, global identification services, and GBIF while ensuring your personal information and sensitive locations remain protected. **Every Pl\@ntNet user with an account can access their data:** * **On the mobile version**, in the **Profile** section of the mobile app, once the user is authenticated. * **On the Web version**, from the **My data** menu and the **Export** button, it is possible to download all of one’s observations to perform analyses with other tools or to share them with collaborators. **Data shared in Pl\@ntNet are used to:** * **Improve the platform’s services**: Shared observations help to refine identification algorithms, enrich the application’s database, and species information sheets. They also contribute to powering GeoPl\@ntNet services. * **Contribute to research projects within Pl\@ntNet**: Data are used in studies on ecology, biodiversity, and citizen science. * **Contribute to research projects worldwide** through [data sharing via GBIF](/en/cookbook/open-data). **Pl\@ntNet does not share its users’ personal data with third parties.** Information shared publicly includes images, identifications, and plant locations if they have been made public. However, for rare or endangered species, you have the option to keep geolocation fields and personal notes confidential. **To access Pl\@ntNet data for research purposes, you must submit a formal request** via the [contact form](https://plantnet.org/en/#contact) on the Pl\@ntNet website. In this request, please specify the details of your research project, as well as the planned use of the data. The Pl\@ntNet team will evaluate your request and provide the necessary information under the best conditions for collaboration. For more information on how to use Pl\@ntNet data (images and observations), please consult [the general terms of use](https://identify.plantnet.org/en/terms_of_use).