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Datasets:
imageomics
/
invasive_plants_hawaii

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session
stringclasses
7 values
filename
stringlengths
12
26
sampling_type
stringclasses
1 value
image
imagewidth (px)
3.02k
6k
site
int32
1
10
day
int32
16
28
plant
stringclasses
1 value
level
stringclasses
1 value
view
stringclasses
1 value
healthy
stringclasses
2 values
rust
stringclasses
3 values
leaf_miner
stringclasses
3 values
other_insect
stringclasses
3 values
mechanical_damage
stringclasses
3 values
other_remarks
stringclasses
5 values
expert_healthy
stringclasses
3 values
expert_rust
stringclasses
3 values
expert_leaf_miner
stringclasses
3 values
expert_other_insect
stringclasses
3 values
expert_mechanical_damage
stringclasses
3 values
expert_confidence
stringclasses
7 values
expert_other_remarks
stringclasses
2 values
expert_notes
stringclasses
32 values
session_1_1_16_2025
DSC00752.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
spots
session_1_1_16_2025
DSC00675.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_1_1_16_2025
DSC00687.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Maybe
nan
nan
session_1_1_16_2025
DSC00734.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_1_1_16_2025
DSC00732.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Maybe
nan
Harder to tell
session_1_1_16_2025
DSC00717.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_1_1_16_2025
DSC00728.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_1_1_16_2025
DSC00679.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_1_1_16_2025
DSC00671.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_1_1_16_2025
DSC00750.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_1_1_16_2025
DSC00657.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Maybe
nan
could just be damage, some minor spotting
session_1_1_16_2025
DSC00673.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Small
nan
Tiny spot of rust
session_1_1_16_2025
DSC00691.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_1_1_16_2025
DSC00760.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
Tip is clearly rust
session_1_1_16_2025
DSC00655.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_1_1_16_2025
DSC00748.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_1_1_16_2025
DSC00667.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
Spotting
session_1_1_16_2025
DSC00693.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_1_1_16_2025
DSC00670.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_1_1_16_2025
DSC00663.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_1_1_16_2025
DSC00689.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
Spotting
session_1_1_16_2025
DSC00648.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_1_1_16_2025
DSC00744.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_1_1_16_2025
DSC00709.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Small
nan
small amount at tip
session_1_1_16_2025
DSC00705.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Clear
nan
Insect damage
session_1_1_16_2025
DSC00695.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Clear
nan
nan
session_1_1_16_2025
DSC00719.png
opportunistic
1
16
exp
nan
D
No
No
No
No
No
nan
No
No
No
No
No
Unclear
nan
Young leaf, maybe rust present
session_1_1_16_2025
DSC00701.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Most likely
nan
Most likely bug damage, but could be rust
session_1_1_16_2025
DSC00653.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Clear
nan
nan
session_1_1_16_2025
DSC00665.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Most likely
nan
Some spotting, but probably just old
session_1_1_16_2025
DSC00726.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Clear
nan
Preliminary spotting or just old
session_1_1_16_2025
DSC00754.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Clear
nan
nan
session_1_1_16_2025
DSC00651.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Clear
nan
Maybe small spores
session_1_1_16_2025
DSC00713.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Clear
nan
nan
session_1_1_16_2025
DSC00707.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Clear
nan
Maybe small spores
session_1_1_16_2025
DSC00715.png
opportunistic
1
16
exp
nan
D
No
No
No
No
No
nan
No
No
No
No
No
Unclear
nan
young leaves, maybe spores starting
session_1_1_16_2025
DSC00742.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Most likely
nan
one spot, some discoloration
session_1_1_16_2025
DSC00740.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Likely
nan
Beginnings of rust, small spottings
session_1_1_16_2025
DSC00711.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Likely
nan
nan
session_1_1_16_2025
DSC00758.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Likely
nan
nan
session_1_1_16_2025
DSC00730.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Likely
nan
Realy early signs of rust
session_1_1_16_2025
DSC00746.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Likely
nan
Insect or older leaf
session_1_1_16_2025
DSC00721.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Likely
nan
Really early signs of rust
session_1_1_16_2025
DSC00724.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_1_1_16_2025
DSC00738.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Likely
nan
Early rust present
session_1_1_16_2025
DSC00736.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Likely
nan
Beginnings of rust, small spottings
session_1_1_16_2025
DSC00764.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_1_1_16_2025
DSC00683.png
opportunistic
2
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Clear
nan
Insect damage
session_1_1_16_2025
DSC00681.png
opportunistic
2
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Clear
nan
Insect damage, small chance of rust
session_1_1_16_2025
DSC00677.png
opportunistic
2
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Clear
nan
Insect damage, small chance of rust
session_1_1_16_2025
DSC00762.png
opportunistic
2
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Clear
nan
Insect damage
session_1_1_16_2025
DSC00697.png
opportunistic
2
16
exp
nan
D
No
No
No
No
No
nan
No
No
No
No
No
Unclear
nan
Could be rust, could be old
session_1_1_16_2025
DSC00756.png
opportunistic
2
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
No spotting, but rust
session_1_1_16_2025
DSC00661.png
opportunistic
2
16
exp
nan
D
No
No
Yes
No
No
nan
No
No
Yes
No
No
Clear
nan
nan
session_1_1_16_2025
DSC00699.png
opportunistic
2
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
Old leaf like characteristics
session_1_1_16_2025
DSC00703.png
opportunistic
2
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
Old leaf like characteristics.
session_1_1_16_2025
DSC00685.png
opportunistic
2
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Likely
nan
old leaf characteristics
session_1_1_16_2025
DSC00647.png
opportunistic
2
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Maybe
nan
rust, more old like characteristics
session_2_1_18_2025
IMG_0341.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
spots
session_2_1_18_2025
IMG_0355.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_2_1_18_2025
IMG_0351.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Maybe
nan
nan
session_2_1_18_2025
IMG_0347.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_2_1_18_2025
IMG_0396.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Maybe
nan
Harder to tell
session_2_1_18_2025
IMG_0394.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_2_1_18_2025
IMG_0408.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_2_1_18_2025
IMG_0373.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_2_1_18_2025
IMG_0414.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_2_1_18_2025
IMG_0337.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_2_1_18_2025
IMG_0339.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Maybe
nan
could just be damage, some minor spotting
session_2_1_18_2025
IMG_0361.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Small
nan
Tiny spot of rust
session_2_1_18_2025
IMG_0363.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_2_1_18_2025
IMG_0435.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
Tip is clearly rust
session_2_1_18_2025
IMG_0416.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_2_1_18_2025
IMG_0384.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_2_1_18_2025
IMG_0345.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
Spotting
session_2_1_18_2025
IMG_0402.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_2_1_18_2025
IMG_0376.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_2_1_18_2025
IMG_0390.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_2_1_18_2025
IMG_0386.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
Spotting
session_2_1_18_2025
IMG_0410.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_2_1_18_2025
IMG_0439.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Clear
nan
nan
session_2_1_18_2025
IMG_0392.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Small
nan
small amount at tip
session_2_1_18_2025
IMG_0388.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Clear
nan
Insect damage
session_2_1_18_2025
IMG_0400.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Clear
nan
nan
session_2_1_18_2025
IMG_0359.png
opportunistic
1
16
exp
nan
D
No
No
No
No
No
nan
No
No
No
No
No
Unclear
nan
Young leaf, maybe rust present
session_2_1_18_2025
IMG_0437.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Most likely
nan
Most likely bug damage, but could be rust
session_2_1_18_2025
IMG_0443.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Clear
nan
nan
session_2_1_18_2025
IMG_0371.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Most likely
nan
Some spotting, but probably just old
session_2_1_18_2025
IMG_0365.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Clear
nan
Preliminary spotting or just old
session_2_1_18_2025
IMG_0424.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Clear
nan
nan
session_2_1_18_2025
IMG_0428.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Clear
nan
Maybe small spores
session_2_1_18_2025
IMG_0335.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Clear
nan
nan
session_2_1_18_2025
IMG_0382.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Clear
nan
Maybe small spores
session_2_1_18_2025
IMG_0445.png
opportunistic
1
16
exp
nan
D
No
No
No
No
No
nan
No
No
No
No
No
Unclear
nan
young leaves, maybe spores starting
session_2_1_18_2025
IMG_0433.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Most likely
nan
one spot, some discoloration
session_2_1_18_2025
IMG_0380.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Likely
nan
Beginnings of rust, small spottings
session_2_1_18_2025
IMG_0357.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Likely
nan
nan
session_2_1_18_2025
IMG_0331.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Likely
nan
nan
session_2_1_18_2025
IMG_0418.png
opportunistic
1
16
exp
nan
D
No
Yes
No
No
No
nan
No
Yes
No
No
No
Likely
nan
Realy early signs of rust
session_2_1_18_2025
IMG_0406.png
opportunistic
1
16
exp
nan
D
Yes
No
No
No
No
nan
Yes
No
No
No
No
Likely
nan
Insect or older leaf
End of preview. Expand in Data Studio

Dataset Card for Invasive Plants Project

This dataset is aimed at the image multi-classification and segmentation of various leaf damage types caused by biocontrol agents. The dataset contains images of both the dorsal and ventral side of Clidemia Hirta leaves, that were all collected in January 2025 near Hilo (Hawaii), in dirt trails along Steinback Highway. Clidemia Hirta is a highly invasive plant on the island of Hawaii (Big Island).

Dataset Configurations and Splits

Three configurations of the dataset can be loaded from the HuggingFace datasets library. Each configuration can be further subdivided into three analytical splits.

The Systematic Configuration

The systematic configuration will download to your local machine the subset of the dataset corresponding to the leaves that were collected by following our systematic sampling protocol (see the paper for more details). The dorsal split will only download the images corresponding to the dorsal side of these leaves, the ventral split will only download the images corresponding to the ventral side, and the both split will include both sides of the images. Here's how to load each of the three available splits : 

from datasets import load_dataset

# Dorsal Split
dorsal_dataset = load_dataset("imageomics/invasive_plants_hawaii", name="systematic", split="dorsal")

# Ventral Split
ventral_dataset = load_dataset("imageomics/invasive_plants_hawaii", name="systematic", split="ventral")

# Combined Ventral and Dorsal Split
combined_dataset = load_dataset("imageomics/invasive_plants_hawaii", name="systematic", split="both")

The Opportunistic Configuration

The opportunistic configuration will download to your local machine the subset of the dataset corresponding to the leaves that were collected by following our opportunistic sampling protocol (see the paper for more details). The dorsal split will only download the images corresponding to the dorsal side of these leaves, the ventral split will only download the images corresponding to the ventral side, and the both split will include both sides of the images. Here's how to load each of the three available splits : 

from datasets import load_dataset

# Dorsal Split
dorsal_dataset = load_dataset("imageomics/invasive_plants_hawaii", name="opportunistic", split="dorsal")

# Ventral Split
ventral_dataset = load_dataset("imageomics/invasive_plants_hawaii", name="opportunistic", split="ventral")

# Combined Ventral and Dorsal Split
combined_dataset = load_dataset("imageomics/invasive_plants_hawaii", name="opportunistic", split="both")

The Full Configuration

The full configuration will download the full dataset (i.e., both opportunistically and systematically sampled leaves) to your local machine . The dorsal split will only download the images corresponding to the dorsal side of the leaves, the ventral split will only download the images corresponding to the ventral side, and the both split will include both sides of the images. Here's how to load each of the three available splits : 

from datasets import load_dataset

# Dorsal Split
dorsal_dataset = load_dataset("imageomics/invasive_plants_hawaii", name="full", split="dorsal")

# Ventral Split
ventral_dataset = load_dataset("imageomics/invasive_plants_hawaii", name="full", split="ventral")

# Combined Ventral and Dorsal Split
combined_dataset = load_dataset("imageomics/invasive_plants_hawaii", name="full", split="both")

Dataset Details

Baseline Damage Classification Benchmark

F1-Score

Model / Damage Healthy Leaf Miner Fungus Other Insect Mechanical
ResNet-50 69.3% 66.8% 49.7% 68.9% 60.7%
ResNext 73.5% 66.4% 62.0% 66.2% 59.9%
ConvNext 87.2% 78.0% 76.2% 73.2% 67.8%
ViT-base 48.5% 69.5% 47.4% 71.2% 66.0%
MaxViT 81.5% 70.3% 72.9% 74.9% 52.1%
Conv ViT 70.7% 71.9% 66.5% 60.7% 65.1%

ROC-AUC

Model / Damage Healthy Leaf Miner Fungus Other Insect Mechanical
ResNet-50 84.0% 82.4% 68.9% 73.6% 70.4%
ResNext 88.1% 82.3% 71.1% 77.0% 70.6%
ConvNext 97.1% 88.2% 86.1% 87.1% 73.8%
ViT-base 81.2% 84.6% 65.9% 81.4% 70.8%
MaxViT 93.6% 83.8% 82.1% 86.4% 71.3%
Conv ViT 85.7% 81.1% 69.8% 71.9% 73.2%

Imaging Sessions

The leaves were imaged during 12 distinct imaging sessions (marked as session in the dataset). The session session_1_16_2025 was our initial test imaging setup and we don't recommend using the images associated with this session to train any leaf damage classification or segmentation model. We used only the images from session 2 to session 12 to train our model. These images were carefully taken in a lab-controlled setting, with an intense bright light, on a 1/4 inch white grid paper. The four red dots can be used to remove distortion in the images, and the color card can be used to calibrate the colors. Two different phones were used to image the leaves, namely :

  • Sessions 1, 2, 3, 4, 5, 6, 9, 11 : iPhone 13
  • Sessions 7, 8, 10, 12 : Pixel 3

Leaf ID

Each leaf can be uniquely identified by the following informations :

  • Imaging session : The name of the imaging session during which the dorsal and ventral pictures of the leaf were taken, marked in the session column of the dataset.
  • Site ID : The ID of the site (marked in the site column of the dataset) where the leaf was collected.
  • Plant ID : The ID of the plant (marked in the plant column of the dataset) from which the leaf was collected. For opportunistically sampled leaves, the plant ID will be noted as Exp.
  • Level : The level of the plant (marked in the level column of the dataset) from which the leaf was collected. For leaves sampled opportunistically, the value of the level column is equal to None. For the leaves sampled systematically, the value of level column can be either
    • L : Leaf was sampled from the bottom level of the plant
    • M : Leaf was sampled from the middle of the plant
    • H : Leaf was sampled from the top of the plant
  • Index : An integer number to help identify each leaf individually, marked in the index column of the dataset

Extracting Images and Distinguishing Between Dorsal and Ventral Sides

The images corresponding to the dorsal and ventral side of each can be easily accessed by using the following informations:

  • Filename : The filename of the image, marked in the filename column of the dataset
  • View : The side of the leaf (D for dorsal, V for ventral) associated to the filename image. This information is marked in the view column of the dataset

Damage Types (Classification Labels)

A given leaf can be completely healthy (which will be indicated in the healthy column of the dataset), or show a combination of the following types of damage:

  • Fungus Damage : Damage caused by Colletotrichum gloeosporioides, an antrachnose fungus that was released in Hawaii as a biocontrol agent for the first time in 1997. Infected leaves will have dark brown lesions that resemble rust spots. The presence or absence of fungus damage for a given leaf will be indicated in the rust column of the dataset.
  • Mechanical Damage : Mechanical damage caused notably by human/animal manipulation, pesticides, or cars. Affected leaves will have dark irregular lesions on them. These lesions have a different shape and are darker than those caused by C. gloeosporioides. The presence or absence of mechanical damage for a given leaf will be indicated in the mechanical_damage column of the dataset.
  • Leaf Miner Damage : Damage caused by leaf miner insects. The larvae of Lius Poseidon, released in Hawaii in 1988 as a biocontrol agent for C. Hirta, is a notable example. The larvae creates a distinctive brown serpentine shape, that widens as the larvae grows. The presence or absence of leaf miner damage for a given leaf will be indicated in the leaf_miner column of the dataset.
  • Other Insects Damage : Damage caused by other insects, notably leaf defoliation. The presence or absence of other insect damage for a given leaf will be indicated in the other_insect column of the dataset.

For a given leaf, each damage type column (healthy, rust, other_insect, leaf_miner, mechanical_damage) will be marked by either Yes, Maybe, or No. The column other_remarks can also include additional notes about the leaf damage(s) that were observed by the human who labelled it.

Expert Validation

Expert validation on the classification labels of the leaves is available for a subset of the dataset. For a given leaf, the damage types observed by the expert are marked in the columns expert_healthy, expert_rust, expert_leaf_miner, expert_mechanical_damage and expert_other_insect. The expert's confidence level in their annotations is stored in the expert_confidence column. Additional remarks from the expert on the observable damage of the leaf can be found in the expert_other_remarks and expert_notes columns.

Supported Tasks and Leaderboards

Our dataset supports image multi-class classification, as well as image segmentation. See our paper for current benchmarks.

Dataset Structure 

/invasive_plants_hawaii/
    docs/
        field_imaging_and_extraction_protocol.pdf
        lab_imaging_protocol.pdf
    metadata/
        full_dataset.csv
        full_opportunistic_dataset.csv
        full_systematic_dataset.csv
        full_dataset_dorsal.csv
        systematic_dataset_dorsal.csv
        opportunistic_dataset_dorsal.csv
        full_dataset_ventral.csv
        systematic_dataset_ventral.csv
        opportunistic_dataset_ventral.csv
        protocol/
          imaging_sessions_metadata.csv
          sites_metadata_gps.csv
          trips_metadata.csv
    full/
        both-00000-of-00046.parquet
        dorsal-00000-of-00024.parquet
        ventral-00000-of-00023.parquet
        ...
        both-00045-of-00046.parquet
        dorsal-00023-of-00024.parquet
        ventral-00022-of-00023.parquet
    systematic/
        both-00000-of-00023.parquet
        dorsal-00000-of-00012.parquet
        ventral-00000-of-00012.parquet
        ...
        both-00023-of-00024.parquet
        dorsal-00011-of-00012.parquet
        ventral-00011-of-00012.parquet
    opportunistic/
        both-00000-of-00024.parquet
        dorsal-00000-of-00012.parquet
        ventral-00000-of-00012.parquet
        ...
        both-00023-of-00024.parquet
        dorsal-00011-of-00012.parquet
        ventral-00011-of-00012.parquet
    images/
        session_1_1_16_2025/
          {img_name}.png
        session_2_1_18_2025/
          {img_name}.png
        ...
        session_12_1_28_2025/
          {img_name}.png

The folder docs contains the .pdf files detailing our leaf imaging protocols.

The folder metadata contains the .csv files liking each image to their labels, for each configuration (full, opportunistic, systematic) and for each split (dorsal, ventral, both). The subfolder protocol, located inside the metadata folder, contains the .csv files summarizing the metadata associated with each imaging session, each sampling site and each sampling trip.

The folders full, opportunistic and systematic contains the parquet files that can be loaded when using the load_dataset function from the HuggingFace datasets library. Each parquet file is approximately between 400 and 800 MB.

The image folder contains the images of the leaves, splitted between 12 imaging sessions.

Data Instances

This dataset is a collection of Clidemia Hirta leaves images paired with associated damage classes, identified either by 1) a computer scientist or 2) a computer scientist and an expert. There are 821 leaves, each imaged on both their dorsal and ventral side (1642 images in total, 821 dorsal images, 821 ventral images). Of these 821 leaves, 423 of them were opportunistically sampled, and the remaining 398 were collected according to our systematic sampling protocol.

Data Fields

The files full_dataset.csv, full_opportunistic_dataset.csv.csv, full_systematic_dataset.csv, full_dataset_dorsal.csv, systematic_dataset_dorsal.csv, opportunistic_dataset_dorsal.csv, full_dataset_ventral.csv, systematic_dataset_ventral.csv and opportunistic_dataset_ventral.csv all share the same following metadata with each image in the dataset :

  • session : The session during which the leaf was imaged
  • filename : The filename of the .PNG image associated with the leaf in the session folder
  • site : The site ID where the leaf was collected
  • day : The day of when the leaf was collected (a day of January 2025)
  • plant : The ID of the plant from which the leaf was collected
  • level : The level of the plant from which the leaf was collected
  • index : The ID number associated with the leaf
  • view : The side from which the leaf was taken (dorsal or ventral)
  • healthy : Yes if the leaf is surely healthy, Maybe if the leaf is potentially healthy, No if the leaf is surely not healthy
  • rust : Yes if the leaf has fungus damage, Maybe if the leaf has potentially fungus damage, No if the leaf doesn't show any sign of fungus damage
  • leaf_miner : Yes if the leaf has been damaged by a leaf miner, Maybe if the leaf was potentially damaged by a leaf miner, No if the leaf wasn't damaged by a leaf miner
  • other_insect : Yes if the leaf was damaged by an insect that isn't a leaf miner, Maybe if the leaf was potentially damaged by an insect that is not a leaf miner, No if the leaf was not damaged by an insect that is not a leaf miner
  • mechanical_damage : Yes if the leaf has mechanical damaged, Maybe if there is potentially some signs of mechanical damage, No if there is no sign of mechanical damage on the leaf
  • other_remarks : Other remarks about the leaf written by the human who labelled it
  • expert_healthy : If the leaf was classified or not as healthy by the expert. Can be Yes, Maybe, No or None (if the leaf was not labelled by the expert)
  • expert_rust : If the expert observed any sign of fungus damage on the leaf. Can be Yes, Maybe, No or None (if the leaf was not labelled by the expert)
  • expert_other_insect : If the expert observed any sign of other insect damage on the leaf. Can be Yes, Maybe, No or None (if the leaf was not labelled by the expert)
  • expert_leaf_miner : If the expert observed any sign of leaf miner damage on the leaf. Can be Yes, Maybe, No or None (if the leaf was not labelled by the expert)
  • expert_confidence : How confident the expert was in their labels
  • expert_other_remarks : Additional remarks from the expert on the damage they observed on the leaf
  • expert_notes : Additional remarks from the expert on the damage we can observe on the leaf
  • sampling_type : Describes the sampling strategy that was used to collect the leaf. Can be opportunistic or systematic

The metadata/protocol/trips_metadata.csv file contains information about each field data collection trip during which the leaves were collected :

  • Trip # : The ID of the trip
  • Site ID : The sampling site ID we visited during that trip
  • Date : The date we did the field collection trip
  • Start Time : The time when we started the field collection trip
  • End Time : The time when we ended the field collection trip
  • Notes : Relevant notes about the sampling trip

The metadata/protocol/imaging_sessions_metadata.csv file contains information about each leaf imaging session :

  • ID : The ID of the imaging session (between 1 and 12)
  • Location : The location where we did the imaging session
  • Camera : The camera used to take pictures of the leaves
  • Photographer : The individual who took the pictures of the leaves during that imaging session
  • Session Start Time : Start time of the imaging session
  • Session End Time : End time of the imaging session
  • Notes : Relevant notes about the imaging session

Dataset Creation

This dataset was compiled as part of the field component of the Experiential Introduction to AI and Ecology Course run by the Imageomics Institute and the AI and Biodiversity Change (ABC) Global Center. This field work was done on the island of Hawai'i January 15-30, 2025.

Curation Rationale

This dataset was created with the goal of developping a deep learning-based tool that would allow conservationists in Hawaii to automate their monitoring of the impact of biocontrols on invasive plants species distribution in Hawaii. We selected the specie Clidemia Hirta as our field collection target because its abundance, but our protocols can be deployed and our algorithms can be trained on any similar kind of datasets.

Source Data

The raw images were downloaded from an iPhone 13 and a Pixel 3, then transformed into a .PNG format. Please feel free to reach out to us if you are interested in getting the raw images.

Data Collection and Processing

Please see docs/Field Imaging & Extraction Protocol.pdf and docs/Lab Imaging Protocol.pdf for more information on our data collection, processing and imaging protocols.

Who are the source data producers?

Imaged in Hawaii by the data annotators (David Carlyn and Catherine Villeneuve)

Annotations

Annotation process

Classification annotations were done after imaging, and were done after carefully looking at the leaf. We recorded our labels into a shared Google Drive Sheet, that we later cleaned and transformed into a standardized .csv format.

Who are the annotators?

  • Expert Annotator : Ellyn Bitume
  • Student Annotators : David Carlyn, Catherine Villeneuve and Leonardo Viotti

Personal and Sensitive Information

Clidemia Hirta is an highly invasive plant of the island of Hawaii. If you are interesting in using the GPS locations of our field sites to reproduce our protocol/collect more leaves, please follow the appropriate car/clothes/shoe cleaning guidelines in order to prevent the further spread of Clidemia Hirta on the island.

Considerations for Using the Data

Please use the citation bellow if you end up using our dataset.

Bias, Risks, and Limitations

Our systematic sampling protocol is probably not 100% bias free. Please be careful if you use this data to infer any relationship with e.g. damage and environmental covariates, and please reach out to us if you would like more detailed informations about the limitations of our systematic sampling protocol.

Recommendations

We don't recommend using our systematic sampling protocol as is to infer relationship between Clidemia Hirta biocontrols and environmental covariates. Careful statistical processing is required.

Licensing Information

[More Information Needed]

Citation

Data:

@dataset{invasive_plants_hawaii_dataset,
  author = {David Edward Carlyn and Catherine Villeneuve and Leonardo Viotti and Kazi Sajeed Mehrab
            and Ellyn Bitume and Chuck Stewart and Leanna House},
  title = {Hawaii Leaf Damage Dataset},
  year = {2025},
  url = {https://huggingface.co/datasets/imageomics/invasive_plants_hawaii},
  publisher = {Hugging Face}
}

Paper:

@article{invasive_plants_hawaii_paper,
  author = {David Edward Carlyn and Catherine Villeneuve and Leonardo Viotti and Kazi Sajeed Mehrab
            and Ellyn Bitume and Chuck Stewart and Leanna House},
  title = {Hawaii Leaf Damage Paper},
  year = {2025},
  journal = {},
  url={},
  doi={}
}

Acknowledgements

This work was supported by both the Imageomics Institute and the AI and Biodiversity Change (ABC) Global Center. The Imageomics Institute is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under Award #2118240 (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). The ABC Global Center is funded by the US National Science Foundation under Award No. 2330423 and Natural Sciences and Engineering Research Council of Canada under Award No. 585136. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or Natural Sciences and Engineering Research Council of Canada.

We acknowledge the support of the U.S. Forest Service, more specifically the researchers associated with the Pacific Southwest Research Station (Institute of Pacific Islands Forestry), for their support and collaboration in this research. Data collection and protocols were notably done in close collaboration with Dr. Ellyn Bitume, a research entomologist for the U.S. Forest Service. We also acknowledge the support of the National Ecological Observatory Network (NEON), a program sponsored by the U.S. National Science Foundation (NSF) and operated under cooperative agreement by Battelle. We especially thank the team associated with the Pu'u Maka'ala Natural Area Reserve, for helping us with logistics and equipments.

Dataset Card Authors

Catherine Villeneuve and David Carlyn

Dataset Card Contact

Catherine Villeneuve can be reached out through [email protected]

David Carlyn can be reached out through [email protected]

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Curated by:
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Language:
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Repository:
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Paper:
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Size of the auto-converted Parquet files (First 5GB per split):
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Estimated number of rows:
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