Published August 13, 2026 | Version 1.0.3

MaizeHorizon: a forward-motion, range-stratified per-plant maize detection dataset

Authors/Creators

  • 1. University of Engineering and Technology, Vietnam National University, Hanoi 10000, Vietnam

Description

MaizeHorizon is a per-plant maize detection dataset recorded from a forward-facing RGB camera on a ground robot driving along crop rows toward the horizon. This viewpoint makes detection a far-field small-object problem: plants recede until they occupy a handful of pixels and merge into soil and canopy clutter.

The record accompanies the paper Far-Field Per-Plant Maize Detection: Network Input Size Does Not Lower the Floor, and is built to support its central measurement: a dissociation between the pixels the sensor resolves on a plant and the network input size.

Contents

  • 5,979 RGB frames at 1920x1080 across 28 forward-motion clips, collected in two maize fields in Dan Phuong, Hanoi, Vietnam over three days.
  • Hand-labeled test set: 3 held-out clips, 120 frames, 4,067 plant instances and 941 ignore regions, stratified by pixels-on-target into near (2,747), mid (1,238) and far (82) tiers.
  • An explicit ignore class covering the unresolvable green band along the horizon, so that far-field evaluation neither penalizes correct detections nor rewards misses.
  • Clip-disjoint splits for two training arms, preventing temporal leakage between near and far views of the same plant.
  • Optically minted labels: 3,636 far-tier pseudo-label files and 10,355 near/far pairs of the same physical plant, obtained by detecting each plant when near and tracking it backward with optical flow.
  • Metadata: a human-verified per-plant count ledger and the test-set box-area cumulative distribution.

Files

MaizeHorizon-images.tar (5.7 GB) holds the frames; MaizeHorizon-annotations.tar.gz (3.0 MB) holds labels, splits and metadata and is usable on its own. Both extract into a single data/ directory. Verify with SHA256SUMS.txt; see README.md for the full layout, label format and usage.

Code

Analysis and training code: github.com/manhhv87/MaizeHorizon (AGPL-3.0, inherited from Ultralytics YOLO). The data here are CC BY 4.0.

Scope

The data come from a single locality. Absolute recall, AP and counting figures characterize this domain rather than guaranteeing cross-domain transfer, and no claim of season or cultivar invariance is made.

Notes

Labels are YOLO format, normalized cxcywh, with class 0 = plant and class 1 = ignore. Evaluation must be ignore-aware: detections falling inside an ignore region are scored as neither true positive nor false positive. Accompanies the paper "Far-Field Per-Plant Maize Detection: Network Input Size Does Not Lower the Floor". Version 1.0.3 corrects the author list to the single author of that paper; the data files are unchanged from 1.0.2.

Files

LICENSE.txt

Files (6.1 GB)

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Additional details

Related works

Is supplemented by
Software: https://github.com/manhhv87/MaizeHorizon (URL)