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PlantCLEF 2021



For several centuries, botanists have collected, catalogued and systematically stored plant specimens in herbaria. These physical specimens are used to study the variability of species, their phylogenetic relationship, their evolution, or phenological trends. One of the key step in the workflow of botanists and taxonomists is to find the herbarium sheets that correspond to a new specimen observed in the field. This task requires a high level of expertise and can be very tedious. Developing automated tools to facilitate this work is thus of crucial importance. More generally, this will help to convert these invaluable centuries-old materials into FAIR data.

Data collection

The task will rely on a large collection of more than 60,000 herbarium sheets that were collected in French Guyana (i.e. from the Herbier IRD de Guyane ) and digitized in the context of the e-ReColNat project. iDigBio (the US National Resource for Advancing Digitization of Biodiversity Collections) hosts millions of images of herbarium specimens. Several tens of thousands of these images, illustrating the French Guyana flora, will be used for the PlantCLEF task this year. A valuable asset of this collection is that several herbarium sheets are accompanied by a few pictures of the same specimen in the field. For the test set, we will use in-the-field pictures coming different sources including Pl@ntNet and Encyclopedia of Life.

The training dataset of the LifeCLEF2021 Plant Identification challenge will be based on the same visual data used during the previous LifeCLEF 2020 Plant Identification challenge but will also introduce new data related to 5 "traits" covering exhaustively all the 1000 species of the challenge. Traits are a very valuable information that can potentially help improve prediction models. Indeed, it can be assumed that species which share the same traits also share to some extent common visual appearances. This information can then potentially be used to guide the learning of a model through auxiliary loss functions for instance.
These data were collected through the Encyclopedia of Life API. The 5 most exhaustive traits ("plant growth form", "habitat", "plant lifeform", "trophic guild" and "woodiness") were verified and completed by experts of the Guyanese flora, so that each of the 1000 species have a value for each trait.

Task description

The challenge will be evaluated as a cross-domain classification task. The training set will consist of herbarium sheets whereas the test set will be composed of field pictures. To enable learning a mapping between the herbarium sheets domain and the field pictures domain, we will provide both herbarium sheets and field pictures for a subset of species. The metrics used for the evaluation of the task will be the classification accuracy and the Mean Reciprocal Rank.

How to participate ?

A direct link to the related challenge on AIcrowd platform will be provided soon. In the mean time:

  1. Each participant has to register on AIcrowd ( with username, email and password. A representative team name should be used
    as username.
  2. In order to be compliant with the CLEF requirements, participants also have to fill in the following additional fields on their profile:
    • First name
    • Last name
    • Affiliation
    • Address
    • City
    • Country
  3. This information will not be publicly visible and will be exclusively used to contact you and to send the registration data to CLEF, which is the main organizer of all CLEF labs. Once set up, participants will have access to the dataset tab on the challenge's page. A LifeCLEF participant will be considered as registered for a task as soon as he/she has downloaded a file of the task's dataset via the dataset tab of the challenge.


The winner of each of the four LifeCLEF 2020 challenges will be offered a cloud credit grants of 5k USD as part of Microsoft's AI for earth program.


A total of 4 participating groups submitted 29 runs. Thanks to all of you for your efforts!

Team run name Aicrowd name Filename MRR (whole test set) MRR (sub-set of the test set related to species with few training photos in the field)
Neuon AI Run 7 holmes_chang submissionformat_11_run8_run1_run11_run13_run14.csv 0.181 0.158
Neuon AI Run 10 holmes_chang submissionformat_14_run14t_run1_run11_run13_run14.csv 0.176 0.153
Neuon AI Run 8 holmes_chang submissionformat_12_run8_run1_run11.csv 0.169 0.15
Neuon AI Run 2 holmes_chang 2_mixed_run13_run14_precrop_mean_emb_cosine_similarity_inverse_weight_flip_crop.csv 0.152 0.117
Neuon AI Run 9 holmes_chang submissionformat_13_run8_run1_run11_run13_run14_e.csv 0.147 0.129
Neuon AI Run 6 holmes_chang submissionformat_10_run8_run13_run14.csv 0.143 0.126
Neuon AI Run 5 holmes_chang submissionformat_9_run1_run13_run14.csv 0.137 0.116
Neuon AI Run 4 holmes_chang submissionformat_5_run6_run13_run14.csv 0.088 0.073
Neuon AI Run 1 holmes_chang run1_cont_precrop_mean_emb_cosine_similarity_inverse_weight_flip_crop.csv 0.071 0.066
LU Run 9 heaven Submission_2021_3.56_3.35_n_times.txt 0.065 0.037
Domain Run 8 Domain_run Submission_2021_3.7.txt 0.065 0.037
LU Run 8 heaven Submission_2021_3.56_raw.txt 0.065 0.037
LU Run 7 heaven Submission_2021_3.56_3.35.txt 0.063 0.04
Domain Run 4 Domain_run Submission_2021_3.txt 0.063 0.046
Neuon AI Run 3 holmes_chang submissionformat_3_run6.csv 0.06 0.056
Domain Run 6 Domain_run Submission_2021_3.35.txt 0.06 0.039
LU Run 6 heaven predictions_2021_3.5921463632307007_weight.txt 0.057 0.042
To Be Run 2 To_be predictions_2021_3.525211958946899.txt 0.056 0.038
LU Run 4 heaven predictions_2021_52.343750.095351815_weight.txt 0.051 0.031
LU Run 3 heaven predictions_2021_46.8750.09727478_weight.txt 0.05 0.026
To Be Run 1 To_be predictions_20021_49.218750.09158182.txt 0.045 0.022
Domain Run 5 Domain_run Submission_2021_408.txt 0.038 0.011
LU Run 5 heaven predictions_20021_50.781250.08264196.txt 0.037 0.022
LU Run 1 heaven Submission_2021_4.71_ass.txt 0.036 0.009
LU Run 2 heaven Submission_2021_46.8750_all.txt 0.034 0.013
Domain Run 3 Domain_run Submission_2.txt 0.031 0.015
Domain Run 1 Domain_run Submission_2021_1.txt 0.019 0.009
Domain Run 2 Domain_run Submission_2021_2.txt 0.018 0.009
Domain Run 7 Domain_run Submission_2021_5.56.txt 0.006 0.003
Organizer's submission 7 herve.goeau fsda_classifier_extra_stage_5_final_genus_family_climber_herb_plant_lifeform_shrub_tree_woodiness_best_acc_on_photo_val_7_aug.csv 0.198 0.093
Organizer's submission 6 herve.goeau fsda_classifier_extra_stage_5_final_genus_family_climber_herb_shrub_tree_best_acc_on_photo_val_7_aug.csv 0.192 0.091
Organizer's submission 4 herve.goeau fsda_classifier_extra_stage_5_final_genus_family_plant_lifeform_best_acc_on_photo_val_7_aug.csv 0.188 0.073
Organizer's submission 5 herve.goeau fsda_classifier_extra_stage_5_final_genus_family_woodiness_best_acc_on_photo_val_7_aug.csv 0.184 0.077
Organizer's submission 3 herve.goeau fsda_classifier_extra_stage_5_final_genus_family_best_acc_on_photo_val_7_aug.csv 0.183 0.073
Organizer's submission 2 herve.goeau fsda_extra_final_single_loop_ep30_aug_7.csv 0.153 0.056
Organizer's submission 1 herve.goeau fsda_final_aug_7.csv 0.052 0.042


This second graph focuses on the very difficult sub-part of the test set and reorders the submissions according to the second metric.

This last graph reports the results with some baselines provided by the organizers in collaboration with Juan Villacis who was ranked first in last year's edition (ITCR PlantNet - aabab).



Pl@ntNet      iDigBio