README.md

metadata

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

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 (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.
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: 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.