README.md

---
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: train
    path: data/train/**
  - split: test
    path: data/test/**
---

# 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 | 836 |
| Arrangements (distinct kernel layouts) | 128 |
| Kernels per image | 170 – 1000 |
| Images with point annotations | 158 |

| Session | Species | Pollution | Cameras | Images | Arrangements | Kernels |
|---|---|---|---|---|---|---|
| S02 | oats-s | none | Galaxy S23, iPhone 8 | 124 | 31 | 240–798 |
| S03 | barley-w | light | Galaxy S23, Galaxy Tab S6 Lite, iPhone 8 | 231 | 28 | 225–733 |
| S04 | wheat-w | light | Galaxy Tab S6 Lite, Pixel 9 | 131 | 33 | 170–800 |
| S05 | durum-w | light | Galaxy S23, iPhone 8 | 265 | 31 | 230–782 |
| count_1000 | barley-w, durum-w, rye-w, wheat-w, oats-s | light, none | Galaxy S23, iPhone 8 | 85 | 5 | 1000–1000 |

## 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. |
| `pollution` | `none`, `light` or `heavy`: amount of chaff, awns, broken kernels, pebbles, dust or office materials mixed in. **Never counted.** |
| `camera` | Camera model from EXIF. |
| `taken_at` | Capture time from EXIF (camera clock, no time zone). |
| `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.

**Pollution in the data:** chaff, awns, broken kernels, pebbles, dust or office materials will
occur in the use case, so some sessions deliberately contain them as noise (`pollution` =
`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

### Sessions

- **S02:** spring oats (`oats-s`), not polluted; Galaxy S23 and iPhone 8;
  2026-10-02.
- **S03:** winter barley (`barley-w`), lightly polluted; Galaxy S23 (shot as DNG),
  iPhone 8 and Galaxy Tab S6 Lite; shot over three days, 2026-09-16 to 09-21.
- **S04:** winter soft wheat (`wheat-w`), lightly polluted; Pixel 9 and Galaxy Tab
  S6 Lite; 2026-10-15. 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.
- **S05:** winter durum wheat (`durum-w`), lightly polluted; Galaxy S23 (shot as
  DNG) and iPhone 8; 2026-10-15.
- **count_1000:** the `test` split. All five species with exactly 1000 kernels in every
  photo, 13–21 photos per species; barley lightly polluted, the others not polluted;
  Galaxy S23 (shot as DNG) and iPhone 8; 2026-10-26. No session cards were photographed:
  the species were assigned by capture time and checked by eye, and each species is one
  `arrangement_id`.

## 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 `session`; never by image, and never by `arrangement_id`
alone.

The `test` split is session `count_1000` (see *Sessions*); `train` holds all other sessions.

## 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 in the table above so far.
- Some sessions contain pollution (`pollution` ≠ `none`). It is visible in the photos and
  deliberately not part of the count.

## 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. -->