geraldmc/plantdoc-full

PlantDoc — full variant

A curated mirror of the PlantDoc plant disease classification dataset (Singh et al. 2020), with normalized class metadata and a stable schema, hosted as a Hugging Face Dataset for reproducible distribution. The companion plantdoc-tiny variant is a 164-image stratified subsample of this dataset for fast test-suite use.

PlantDoc was built to address a specific failure mode in earlier plant disease datasets like PlantVillage: lab-condition images don't predict field-condition performance. PlantDoc images are web-scraped photos of diseased and healthy leaves in real-world settings — heterogeneous in resolution, lighting, background, and capture device. That heterogeneity is the point.

Quick start

from datasets import load_dataset

ds = load_dataset("geraldmc/plantdoc-full", revision="v0.1.0", split="train")
print(len(ds))                # 2578
print(ds.features)            # image + 7 metadata columns
print(ds[0]["class_label"])   # 'Apple Scab Leaf'

Inside the iResearch Institute 2026 Virtual Lab, the typical entry point is:

import irilab2026 as iri

metadata_df, hf_dataset = iri.load_plantdoc()

What's in this dataset

• 2,578 images across 28 classes covering 13 host plant species
• Train/test split shipped intact from the upstream repository — 2,342 train + 236 test
• Heterogeneous resolution (189–4,000 px width, 194–4,272 px height), heterogeneous lighting, heterogeneous backgrounds — by design
• Mostly RGB, some CMYK color modes — downstream loaders should .convert("RGB") defensively

Schema

Column	Type	Description	Example
image	image	PIL Image at original upstream resolution	(W, H) JPEG
class_label	string	Upstream folder name, verbatim	"Apple Scab Leaf"
class_idx	int64	0–27, case-sensitive alphabetical sort over class_label	0
host	string	Normalized host name	"Apple"
disease	string	Lowercased disease name, or "healthy" for healthy leaves	"scab"
is_healthy	bool	True iff the class is a healthy leaf	False
split	string	"train" or "test", from the upstream partition	"train"
filename	string	Original filename, verbatim (URL-encoded chars and double extensions preserved)	"052609%20Hartman%20Crabapple%20scab%20single%20leaf.JPG.jpg"

About host

Hosts are normalized from upstream folder names with one transformation: underscores become spaces (Bell_pepper → "Bell pepper"). Capitalization and spelling are otherwise preserved as upstream had them. Notable inheritances from upstream:

• Soyabean retains the upstream misspelling (canonical is "Soybean") so the normalized name links unambiguously to the original folder.
• grape retains the upstream lowercase. Other hosts are Title Case.
• Corn retains the upstream naming (the more standard botanical name is "Maize") to match Singh et al. 2020 and downstream benchmark papers.

If your downstream code needs cross-dataset matching (e.g., aligning PlantDoc classes to PlantVillage classes), do that normalization explicitly in your code rather than relying on these column values to be canonical — that step is meaningful research methodology and should be visible in your work.

About disease

Disease names are lowercased even when the upstream had Title Case (Powdery mildew → "powdery mildew", Septoria leaf spot → "septoria leaf spot"). Two non-obvious mappings worth flagging:

• Tomato mold leaf → "mold" (the canonical disease is leaf mold, caused by Passalora fulva, but the upstream label format doesn't say "leaf mold" — the column stays close to upstream literal naming).
• Tomato two spotted spider mites leaf → "two spotted spider mites" (a pest, not strictly a plant disease, but it's a PlantDoc class).

About class_idx

Class indices 0–27 are assigned by case-sensitive alphabetical sort over class_label values. With case-sensitive sort, lowercase grape leaf and grape leaf black rot land at indices 26 and 27, after the uppercase Title Case classes.

Known caveats

One class has 2 training images and 0 test images

Tomato two spotted spider mites leaf appears in train/ with 2 images and is entirely absent from test/ in the upstream repository. Singh et al. 2020 and downstream benchmark papers (e.g., Ahmad et al. 2023) report 27 classes — they implicitly drop this orphan. This dataset preserves all 28 classes for upstream fidelity. If you're benchmarking against the literature, drop the orphan explicitly:

df = df[df["class_label"] != "Tomato two spotted spider mites leaf"]

Class names are inconsistent in capitalization, word order, and the position of "leaf"

The upstream folder names are not internally consistent. Examples that all appear in this dataset: Apple Scab Leaf (Title Case, disease before "Leaf"), Tomato Septoria leaf spot (mixed case, "leaf spot" as a compound), grape leaf (all lowercase), Tomato mold leaf (disease before "leaf"), Tomato leaf bacterial spot (host "leaf" disease). The host and disease columns are the hand-curated normalization; the class_label column preserves the upstream string verbatim.

Filenames have quirks worth knowing about

About 6% of filenames contain URL-encoded characters (%20 for spaces, %2C for commas) — artifacts of however the upstream curators saved web-scraped images. Roughly 12% have double extensions like .JPG.jpg or .jpeg.jpg. The filename column preserves these exactly because they're identifiers — the link back to the upstream filename is exact and unambiguous.

Per-class test set sizes are small

Test split sizes range from 4 (Corn Gray leaf spot) to 12 (Corn leaf blight, grape leaf), with a median around 9. Per-class accuracy estimates on this dataset will have wide confidence intervals — frame your conclusions accordingly. Aggregate metrics (averaged across classes or grouped by host family) are more reliable than per-class numbers.

About class_count = 28 but the README of upstream says 17

The upstream README at pratikkayal/PlantDoc-Dataset claims "13 plant species and up to 17 classes of diseases." That's 17 disease classes; the dataset includes healthy-leaf classes for many hosts, bringing the total to 28 (or 27 if the orphan is dropped — see above).

Build provenance

This dataset was built by:

1. Cloning pratikkayal/PlantDoc-Dataset at commit 5467f6012d78 (on a case-sensitive Linux filesystem — see note below).
2. Walking train/ and test/ directory trees to enumerate all image files.
3. Applying a hand-curated 28-row class-normalization lookup to produce the host, disease, and is_healthy columns.
4. Assigning class_idx by case-sensitive alphabetical sort over distinct class_label values.
5. Constructing an HF Dataset with the Image() feature and pushing to geraldmc/plantdoc-full with revision tag v0.1.0.

Build script: scripts/build_pd_full_hf.py in the irilab2026 repository.

A note about case-sensitive filesystems

The upstream repository contains 6 pairs of files whose names differ only in case (e.g., CAR1.jpg and car1.jpg in Apple rust leaf/). On case-insensitive filesystems (default macOS APFS/HFS+, default Windows NTFS) git clone silently drops one file per pair and produces a 2,572-image working tree instead of 2,578. This dataset was built on Colab's case-sensitive Linux filesystem to preserve all 2,578 upstream images. If you rebuild from scripts/build_pd_full_hf.py, do so on a case-sensitive filesystem.

License

This curated mirror is released under CC BY 4.0, matching the license of the upstream pratikkayal/PlantDoc-Dataset.

Attribution must include both the upstream dataset (Singh et al. 2020) and this curated mirror. See Citation below.

Citation

@inproceedings{singh2020plantdoc,
  title     = {{PlantDoc}: A Dataset for Visual Plant Disease Detection},
  author    = {Singh, Davinder and Jain, Naman and Jain, Pranjali and
               Kayal, Pratik and Kumawat, Sudhakar and Batra, Nipun},
  booktitle = {Proceedings of the 7th ACM IKDD CoDS and 25th COMAD},
  pages     = {249--253},
  year      = {2020}
}

When citing the curated mirror itself, additionally reference this Hugging Face Dataset by its repo ID and revision tag (geraldmc/plantdoc-full @ v0.1.0).

Related resources

• geraldmc/plantdoc-tiny — 164-image stratified subsample for test-suite use
• geraldmc/plantvillage-full — the lab-condition counterpart dataset; PlantDoc is most commonly used as a transfer test for classifiers trained on this
• pratikkayal/PlantDoc-Dataset — the upstream GitHub repository