Datasets:

SmartFarmingLab
/

corn1000

---
pretty_name: corn1000 — counted cereal kernels on A4
license: cc-by-nc-4.0
language:
- en
size_categories:
- n<1K
tags:
- agriculture
- counting
- object-counting
- cereal
- grain
- thousand-kernel-weight
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test/**
  - split: train
    path: data/train/**
---
# corn1000 — counted cereal kernels on A4

Photos of cereal kernels spread on a white A4 sheet, each labelled with the **exact number
of kernels** on the sheet. The dataset is built to train models that count kernels from a
single phone photo, as the first step of estimating the thousand-kernel weight
(TKW = mass ÷ count × 1000). A 1 % count error is a 1 % TKW error, so the collection
protocol is built around label correctness.

## Contents

| | |
|---|---|
| Images | 853 |
| Arrangements (distinct kernel layouts) | 150 |
| Kernels per image | 170 – 800 |
| Images with point annotations | 28 |
| Splits | test: 131, train: 722 |

| Session | Species | Lot | Light | Chaff | Cameras | Images | Arrangements | Kernels |
|---|---|---|---|---|---|---|---|---|
| S01 | rye-w | rye-w-A | desk | light | Galaxy S23, iPhone 8 | 108 | 27 | 274–787 |
| S02 | oats-s | oats-s-A | desk | none | Galaxy S23, iPhone 8 | 124 | 31 | 240–798 |
| S03 | barley-w | barley-w-A | desk | light | Galaxy S23, Galaxy Tab S6 Lite, iPhone 8 | 225 | 28 | 225–733 |
| S04 | wheat-w | wheat-w-A | desk | light | Galaxy Tab S6 Lite, Pixel 9 | 131 | 33 | 170–800 |
| S05 | durum-w | durum-w-A | desk | light | Galaxy S23, iPhone 8 | 265 | 31 | 230–782 |

## Fields

| Column | Meaning |
|---|---|
| `image` | The photo. JPEG; see *Orientation* below. |
| `count` | Number of kernels on the sheet — the label. |
| `arrangement_id` | One physical layout of kernels (`<session>-<step>[-<take>]`). Every arrangement is photographed several times, see *Splitting*. |
| `session`, `step`, `take` | Session ID, card number within the session, and re-shoot number after a REDO. |
| `species` | `rye-w` winter rye, `durum-w` winter durum wheat, `barley-w` winter barley, `oats-s` spring oats, `wheat-w` winter soft wheat. |
| `lot` | Seed lot the kernels come from. |
| `light` | Lighting setup of the session (free text). |
| `chaff` | `none`, `light` or `heavy`: amount of chaff, awns and broken kernels mixed in. **Never counted.** |
| `camera` | Camera model from EXIF. |
| `taken_at` | Capture time from EXIF (camera clock, no time zone). |
| `weight_g` | Mass of the counted kernels in grams, only where they were weighed; else empty. |
| `tkw_g` | `weight_g ÷ count × 1000`, where `weight_g` exists. |
| `raw_file` | Path of the original raw file in this repo (`raw/…`) if the photo was shot as DNG; else empty. |
| `kernel_points` | `[x, y]` of every counted kernel, see *Point annotations*; null if the photo is not annotated. |
| `broken_points` | `[x, y]` of broken kernels (not counted); null if not annotated. |
| `invalid_points` | `[x, y]` of other objects marked as not countable; null if not annotated. |

## How it was collected

### Counting rules

Based on ISO 520:2010.

**Counts as a kernel:** an intact kernel, including shrivelled and small ones. Barley and
oats count with their husk. Twins count as two kernels.

**Never counts:** broken kernels; chaff, straw and awns; foreign seeds, dust and stones.
Borderline cases were sorted out rather than judged, to keep the count exact.

**Chaff and broken kernels in the data:** they will occur in the use case, so some sessions
deliberately contain them as noise (`chaff` = `light` or `heavy`). They are never part of
`count`.

### Materials

- White A4 sheet on a dark or high-contrast background
- Cups for the kernels, each holding a verified 50 kernels
- Printed session cards and special cards (REDO, INVALIDATE) from the planning script
- Optional: a gram scale

### Procedure

Each session covers one species, one seed lot, one lighting setup and one chaff level, and
follows a pre-generated plan of about 30 session cards. Each card carries a QR code and
states the new total and what to add or remove (`add 17`, `remove 12`, `add 1 cup + 9`).

1. Take the next session card and put the stated number of kernels on the A4 sheet.
2. **If the card says CHECK:** recount the kernels.
   - The count matches the large number on the card: continue.
   - It does not: photograph an *INVALIDATE* card and fix the pile until it matches the
     card.
3. Photograph the session card.
4. Spread the kernels on the sheet so that no piles remain; touching is fine.
5. Photograph the layout twice per phone, with the whole A4 sheet in frame:
   first roughly top-down, then tilted or from another distance.
6. **If the kernels are weighed:** photograph the scale display, filling the frame,
   straight on.
7. Next card.
If something goes wrong within a card, a *REDO* card is photographed, the pile is fixed to
the number on the current card, and the card and the layout are photographed again.

### Variation between sessions

Lighting (window light, desk lamp, cold and warm light, morning and evening), phones (see
the table above) and backgrounds (table, mat, tray, floor).

### Sessions

- **S01:** winter rye (`rye-w-A`), light chaff, desk light; Galaxy S23 and iPhone 8;
  2026-09-15/16.
- **S02:** spring oats (`oats-s-A`), no chaff, desk light; Galaxy S23 and iPhone 8;
  2026-10-02.
- **S03:** winter barley (`barley-w-A`), light chaff, desk light; Galaxy S23 (shot as DNG),
  iPhone 8 and Galaxy Tab S6 Lite; shot over three days, 2026-09-16 to 09-21.
  The only weighed session.
- **S04:** winter soft wheat (`wheat-w-A`), light chaff, desk light; Pixel 9 and Galaxy Tab
  S6 Lite; 2026-10-15. Test data. The tablet missed or misdated the photos of cards 0, 3, 8,
  14 and 15; its photos of those layouts were placed on the card by hand and checked
  against the Pixel photos.

### Labelling

A script decodes the card photos and assigns their total to the photos that follow them.
It checks that every total follows from the previous one and the card's delta. A group
whose chain breaks (a skipped or unread card) stays *suspect* until the next CHECK, and
groups before an INVALIDATE are *invalidated*. **Only groups whose chain validated are in
this dataset**, after a review of every group by eye. 

Card photos and scale photos are not included.

## Splitting

Images are **not independent**: every arrangement appears in 2–22 photos (several phones,
several angles), and consecutive arrangements of a session share most of their kernels.
Split by `lot`, or at least by `session`; never by image, and never by `arrangement_id`
alone.

The intended evaluation holds out winter soft wheat (`wheat-w`) entirely, plus at least one
lot of every training species. The `test` split follows that rule.

## Loading

```python
from datasets import load_dataset
ds = load_dataset("SmartFarmingLab/corn1000")
```

**Orientation:** most JPEGs are stored sideways with an EXIF orientation tag, exactly as the
phones wrote them. `datasets`, browsers and `cv2.imread` apply the tag; plain
`PIL.Image.open` does not — call `PIL.ImageOps.exif_transpose` after it.

**Raw photos:** some phones shot DNG. For those, the image in `data/` was developed with
LibRaw (rawpy, camera white balance, full resolution, JPEG quality 95). The originals are in
`raw/`, linked by `raw_file`. Colours of developed DNGs differ from the phones' own JPEG
processing.

## Point annotations

A subset of photos carries one point per object, placed by hand in CVAT. Coordinates are
pixels of the **upright** image, i.e. after the EXIF orientation is applied (what `datasets`
returns), with the origin at the top left. The points are a second, independent annotation:
`len(kernel_points)` can differ from `count` by a few kernels, mostly on dense sheets where
a kernel was missed or lies at the edge of the frame. `count` stays the label.

## Known limitations

- Only the species and lots in the table above so far.
- Some sessions contain chaff and broken kernels (`chaff` ≠ `none`). They are visible in the
  photos and deliberately not part of the count.

## Contributing

New sessions are welcome. **Do not upload photos to this repository directly.** Every file
here is generated by the export script from reviewed labels; photos without a
`metadata.jsonl` row have no count and are useless for training. The tools are in the
[project repository](https://github.com/SmartFarmingLab/corn1000) (private, access on
request from the Smart Farming Lab).

1. **Plan:** `tools/plan_session.py <SESSION> --species … --lot … --light … --chaff …`
   writes `sessions/<SESSION>/plan.json` and the cards to print. Keep `plan.json`: it is
   the reference the labels are checked against.
2. **Shoot** the session as described under [Procedure](#procedure).
3. **Label:** copy the original files (not via a messenger) to `sessions/<SESSION>/` and run
   `tools/sort_photos.py sessions/<SESSION> --out labels/<SESSION>`.
4. **Review** every group in `labels/<SESSION>/review.html` and mark the checked rows
   `review=done` in `labels.csv`.
5. **Hand in** `plan.json`, `labels/<SESSION>/` and the original photos to the maintainers
   (the photos are too large for git). They run the export and upload the session here.

## License

[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/): free to use and adapt for
non-commercial purposes with attribution. For commercial use, contact the Smart Farming Lab
(Universität Leipzig).

## Citation

No publication yet. <!-- TODO: add BibTeX once the paper or a DOI exists. -->

	---
	pretty_name: corn1000 — counted cereal kernels on A4
	license: cc-by-nc-4.0
	language:
	- en
	size_categories:
	- n<1K
	tags:
	- agriculture
	- counting
	- object-counting
	- cereal
	- grain
	- thousand-kernel-weight
	configs:
	- config_name: default
	data_files:
	- split: test
	path: data/test/**
	- split: train
	path: data/train/**
	---

	# corn1000 — counted cereal kernels on A4

	Photos of cereal kernels spread on a white A4 sheet, each labelled with the **exact number
	of kernels** on the sheet. The dataset is built to train models that count kernels from a
	single phone photo, as the first step of estimating the thousand-kernel weight
	(TKW = mass ÷ count × 1000). A 1 % count error is a 1 % TKW error, so the collection
	protocol is built around label correctness.

	## Contents

	\| \| \|
	\|---\|---\|
	\| Images \| 853 \|
	\| Arrangements (distinct kernel layouts) \| 150 \|
	\| Kernels per image \| 170 – 800 \|
	\| Images with point annotations \| 28 \|
	\| Splits \| test: 131, train: 722 \|

	\| Session \| Species \| Lot \| Light \| Chaff \| Cameras \| Images \| Arrangements \| Kernels \|
	\|---\|---\|---\|---\|---\|---\|---\|---\|---\|
	\| S01 \| rye-w \| rye-w-A \| desk \| light \| Galaxy S23, iPhone 8 \| 108 \| 27 \| 274–787 \|
	\| S02 \| oats-s \| oats-s-A \| desk \| none \| Galaxy S23, iPhone 8 \| 124 \| 31 \| 240–798 \|
	\| S03 \| barley-w \| barley-w-A \| desk \| light \| Galaxy S23, Galaxy Tab S6 Lite, iPhone 8 \| 225 \| 28 \| 225–733 \|
	\| S04 \| wheat-w \| wheat-w-A \| desk \| light \| Galaxy Tab S6 Lite, Pixel 9 \| 131 \| 33 \| 170–800 \|
	\| S05 \| durum-w \| durum-w-A \| desk \| light \| Galaxy S23, iPhone 8 \| 265 \| 31 \| 230–782 \|

	## Fields

	\| Column \| Meaning \|
	\|---\|---\|
	\| `image` \| The photo. JPEG; see Orientation below. \|
	\| `count` \| Number of kernels on the sheet — the label. \|
	\| `arrangement_id` \| One physical layout of kernels (`<session>-<step>[-<take>]`). Every arrangement is photographed several times, see Splitting. \|
	\| `session`, `step`, `take` \| Session ID, card number within the session, and re-shoot number after a REDO. \|
	\| `species` \| `rye-w` winter rye, `durum-w` winter durum wheat, `barley-w` winter barley, `oats-s` spring oats, `wheat-w` winter soft wheat. \|
	\| `lot` \| Seed lot the kernels come from. \|
	\| `light` \| Lighting setup of the session (free text). \|
	\| `chaff` \| `none`, `light` or `heavy`: amount of chaff, awns and broken kernels mixed in. Never counted. \|
	\| `camera` \| Camera model from EXIF. \|
	\| `taken_at` \| Capture time from EXIF (camera clock, no time zone). \|
	\| `weight_g` \| Mass of the counted kernels in grams, only where they were weighed; else empty. \|
	\| `tkw_g` \| `weight_g ÷ count × 1000`, where `weight_g` exists. \|
	\| `raw_file` \| Path of the original raw file in this repo (`raw/…`) if the photo was shot as DNG; else empty. \|
	\| `kernel_points` \| `[x, y]` of every counted kernel, see Point annotations; null if the photo is not annotated. \|
	\| `broken_points` \| `[x, y]` of broken kernels (not counted); null if not annotated. \|
	\| `invalid_points` \| `[x, y]` of other objects marked as not countable; null if not annotated. \|

	## How it was collected

	### Counting rules

	Based on ISO 520:2010.

	Counts as a kernel: an intact kernel, including shrivelled and small ones. Barley and
	oats count with their husk. Twins count as two kernels.

	Never counts: broken kernels; chaff, straw and awns; foreign seeds, dust and stones.
	Borderline cases were sorted out rather than judged, to keep the count exact.

	Chaff and broken kernels in the data: they will occur in the use case, so some sessions
	deliberately contain them as noise (`chaff` = `light` or `heavy`). They are never part of
	`count`.

	### Materials

	- White A4 sheet on a dark or high-contrast background
	- Cups for the kernels, each holding a verified 50 kernels
	- Printed session cards and special cards (REDO, INVALIDATE) from the planning script
	- Optional: a gram scale

	### Procedure

	Each session covers one species, one seed lot, one lighting setup and one chaff level, and
	follows a pre-generated plan of about 30 session cards. Each card carries a QR code and
	states the new total and what to add or remove (`add 17`, `remove 12`, `add 1 cup + 9`).

	1. Take the next session card and put the stated number of kernels on the A4 sheet.
	2. If the card says CHECK: recount the kernels.
	- The count matches the large number on the card: continue.
	- It does not: photograph an INVALIDATE card and fix the pile until it matches the
	card.
	3. Photograph the session card.
	4. Spread the kernels on the sheet so that no piles remain; touching is fine.
	5. Photograph the layout twice per phone, with the whole A4 sheet in frame:
	first roughly top-down, then tilted or from another distance.
	6. If the kernels are weighed: photograph the scale display, filling the frame,
	straight on.
	7. Next card.

	If something goes wrong within a card, a REDO card is photographed, the pile is fixed to
	the number on the current card, and the card and the layout are photographed again.

	### Variation between sessions

	Lighting (window light, desk lamp, cold and warm light, morning and evening), phones (see
	the table above) and backgrounds (table, mat, tray, floor).

	### Sessions

	- S01: winter rye (`rye-w-A`), light chaff, desk light; Galaxy S23 and iPhone 8;
	2026-09-15/16.
	- S02: spring oats (`oats-s-A`), no chaff, desk light; Galaxy S23 and iPhone 8;
	2026-10-02.
	- S03: winter barley (`barley-w-A`), light chaff, desk light; Galaxy S23 (shot as DNG),
	iPhone 8 and Galaxy Tab S6 Lite; shot over three days, 2026-09-16 to 09-21.
	The only weighed session.
	- S04: winter soft wheat (`wheat-w-A`), light chaff, desk light; Pixel 9 and Galaxy Tab
	S6 Lite; 2026-10-15. Test data. The tablet missed or misdated the photos of cards 0, 3, 8,
	14 and 15; its photos of those layouts were placed on the card by hand and checked
	against the Pixel photos.

	### Labelling

	A script decodes the card photos and assigns their total to the photos that follow them.
	It checks that every total follows from the previous one and the card's delta. A group
	whose chain breaks (a skipped or unread card) stays suspect until the next CHECK, and
	groups before an INVALIDATE are invalidated. **Only groups whose chain validated are in
	this dataset**, after a review of every group by eye.

	Card photos and scale photos are not included.

	## Splitting

	Images are not independent: every arrangement appears in 2–22 photos (several phones,
	several angles), and consecutive arrangements of a session share most of their kernels.
	Split by `lot`, or at least by `session`; never by image, and never by `arrangement_id`
	alone.

	The intended evaluation holds out winter soft wheat (`wheat-w`) entirely, plus at least one
	lot of every training species. The `test` split follows that rule.

	## Loading

	```python
	from datasets import load_dataset

	ds = load_dataset("SmartFarmingLab/corn1000")
	```

	Orientation: most JPEGs are stored sideways with an EXIF orientation tag, exactly as the
	phones wrote them. `datasets`, browsers and `cv2.imread` apply the tag; plain
	`PIL.Image.open` does not — call `PIL.ImageOps.exif_transpose` after it.

	Raw photos: some phones shot DNG. For those, the image in `data/` was developed with
	LibRaw (rawpy, camera white balance, full resolution, JPEG quality 95). The originals are in
	`raw/`, linked by `raw_file`. Colours of developed DNGs differ from the phones' own JPEG
	processing.

	## Point annotations

	A subset of photos carries one point per object, placed by hand in CVAT. Coordinates are
	pixels of the upright image, i.e. after the EXIF orientation is applied (what `datasets`
	returns), with the origin at the top left. The points are a second, independent annotation:
	`len(kernel_points)` can differ from `count` by a few kernels, mostly on dense sheets where
	a kernel was missed or lies at the edge of the frame. `count` stays the label.

	## Known limitations

	- Only the species and lots in the table above so far.
	- Some sessions contain chaff and broken kernels (`chaff` ≠ `none`). They are visible in the
	photos and deliberately not part of the count.

	## Contributing

	New sessions are welcome. Do not upload photos to this repository directly. Every file
	here is generated by the export script from reviewed labels; photos without a
	`metadata.jsonl` row have no count and are useless for training. The tools are in the
	[project repository](https://github.com/SmartFarmingLab/corn1000) (private, access on
	request from the Smart Farming Lab).

	1. Plan: `tools/plan_session.py <SESSION> --species … --lot … --light … --chaff …`
	writes `sessions/<SESSION>/plan.json` and the cards to print. Keep `plan.json`: it is
	the reference the labels are checked against.
	2. Shoot the session as described under [Procedure](#procedure).
	3. Label: copy the original files (not via a messenger) to `sessions/<SESSION>/` and run
	`tools/sort_photos.py sessions/<SESSION> --out labels/<SESSION>`.
	4. Review every group in `labels/<SESSION>/review.html` and mark the checked rows
	`review=done` in `labels.csv`.
	5. Hand in `plan.json`, `labels/<SESSION>/` and the original photos to the maintainers
	(the photos are too large for git). They run the export and upload the session here.

	## License

	[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/): free to use and adapt for
	non-commercial purposes with attribution. For commercial use, contact the Smart Farming Lab
	(Universität Leipzig).

	## Citation

	No publication yet. <!-- TODO: add BibTeX once the paper or a DOI exists. -->