TamAko783/TPDD_Honglin_CLS

Tobacco Leaf Abnormality (TLA) — 16-class YOLO classification split

A cleaned, relabeled, leakage-safe train/val/test split of the TLA (Tobacco Leaf Abnormality) dataset, packaged in the Ultralytics image-classification folder layout (train/val/test/<class>/) and targeting yolo26n-cls.

⚠️ Dataset credit — original authors

The underlying images were collected and annotated by Hong Lin, Rita Tse, Su-Kit Tang and colleagues at the Macao Polytechnic University. TLA is the 16-class refinement of their TPDD (Tobacco Plant Disease Dataset). All credit for the data belongs to them. This repository only performs data processing (cleaning, relabeling, a leakage-safe split, and optional train-only augmentation) and redistributes it for reproducible benchmarking. If you use this data, please cite the original papers below.

Processing & repackaging by TamAko783. The uploader is not the creator of the images and claims no ownership of the original data.

Source papers (please cite)

• TPDD — Tobacco Plant Disease Dataset. Hong Lin, Rita Tse, Su-Kit Tang, et al. SPIE — International Conference on Digital Image Processing (ICDIP) 2022. DOI: 10.1117/12.2644288
• A few-shot learning method for tobacco abnormality identification (TLA / FREN). Hong Lin, Rita Tse, Su-Kit Tang, et al. Frontiers in Plant Science, 2024. DOI: 10.3389/fpls.2024.1333236 (PMC11055634) — defines the 16-class taxonomy and reports the few-shot benchmark (60.7% 16-way 1-shot → 81.8% 16-way 10-shot).

What's inside

train/  <16 class folders>/ *.jpg   # real + *_paste* (Tier-3 lesion) + *_rot* (16x rotation)
val/    <16 class folders>/ *.jpg   # 100% real
test/   <16 class folders>/ *.jpg   # 100% real

All images are Shades-of-Gray colour-constancy normalised (illumination white-balance), applied identically to every split.

The training set is expanded with train-only synthetic images: lesion copy-paste (_paste) and a 16-rotation expansion (_rot, 22.5° steps). To train on real images only, skip files matching *_paste* and *_rot*. val/ and test/ are 100% real (no rotations).

The 16 classes (folder name = label):

#	class (slug)	super-category	#	class (slug)	super-category
1	wildfire	Bacteria	9	pvy	Virus
2	brown_spot	Airborne fungi	10	tswv	Virus
3	frog_eye	Airborne fungi	11	weather_fleck	Nonparasitic
4	anthracnose	Airborne fungi	12	sunscald	Nonparasitic
5	target_spot	Soil-borne fungi	13	genetic_abnormality	Nonparasitic
6	black_shank	Soil-borne fungi	14	potato_tuber_moth	Pest-trace
7	tmv	Virus	15	nematodes	Pest-trace
8	cmv	Virus	16	healthy	Others

Split counts

split	real	lesion-paste (Tier 3)	16-rotation	total
train	1122	74	17,940	19,136
val	138	0	0	138
test	138	0	0	138
total	1398	74	17,940	19,552

Training images are expanded ~16× by rotating every real + lesion-paste image into 16 orientations (22.5° apart). Lesion copy-paste adds genuinely novel diseased leaves for the long tail (anthracnose +17, tswv +26, genetic_abnormality +26, pvy +5). black_shank has no real TV6 lesion crops (its TV6 folder was downscaled duplicates), so it relies on rotation only.

Per-class counts are long-tailed (rare classes: anthracnose, black_shank, tswv, genetic_abnormality). See class_map.yaml for the full folder→class map.

How it was processed

The raw TLA dataset ships as two sections — TV3 (whole-leaf, 696 imgs) and TV6 (disease-fragment crops, 734 imgs). Processing pipeline:

1. Clean — drop thumbs.db, Windows 副本 copies, and near-duplicates (DCT pHash, within-class).
2. Relabel — map raw folder numbers to the canonical 16-class taxonomy; TV6's 5 multi-symptom wildfire sub-folders collapse to wildfire.
3. Group-aware split (80/10/10) — the split unit is the source leaf (a leaf's whole-leaf image + its fragment crops move together), stratified by class with a fixed seed. This is critical: TV6 crops are derived from TV3 leaves (hundreds of shared filename stems), so a naive per-image split would leak. A gate asserts no source leaf appears in more than one split.
4. Materialize + colour constancy — EXIF-transpose, convert RGB, apply Shades-of-Gray white balance (illumination normalisation, applied to all splits), re-save JPEG.
5. Augment (train only) — (a) Tier-3 lesion copy-paste: real TV6 lesion crops blended onto host leaves (*_paste*); (b) 16-rotation expansion: every real + paste image is rotated into 16 orientations 22.5° apart (*_rot*), growing train ~16×. All synthetics live only in train/. val/ and test/ are 100% real (no rotations).

⚠️ A small number of rare-class TV6 fragments were sequentially renamed in the original data, so their source leaf is unrecoverable; those are placed train-only to stay leakage-safe.

Usage (Ultralytics)

from ultralytics import YOLO
model = YOLO("yolo26n-cls.pt")
model.train(data="path/to/this/dataset", imgsz=256, epochs=100)

Citation

@inproceedings{lin2022tpdd,
  title     = {Tobacco Plant Disease Dataset (TPDD)},
  author    = {Lin, Hong and Tse, Rita and Tang, Su-Kit and others},
  booktitle = {International Conference on Digital Image Processing (ICDIP)},
  year      = {2022},
  doi       = {10.1117/12.2644288}
}

@article{lin2024tla,
  title   = {A few-shot learning method for tobacco abnormality identification},
  author  = {Lin, Hong and Tse, Rita and Tang, Su-Kit and others},
  journal = {Frontiers in Plant Science},
  year    = {2024},
  doi     = {10.3389/fpls.2024.1333236}
}

License

The original TLA/TPDD images are © their authors (Macao Polytechnic University). This repackaging is provided for research/benchmarking; consult the source papers for terms and cite the original work. If you are an original author and have any concern about this redistribution, please open an issue on this repository.