Dataset Card for LettuceMOTS

This is a FiftyOne dataset with 12 samples.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/LettuceMOTS")

# Launch the App
session = fo.launch_app(dataset)

Dataset Details

Dataset Description

LettuceMOTS is a lettuce multi-object tracking and segmentation (MOTS) dataset captured by a downward-facing RGB camera mounted on VegeBot, an agricultural robot developed by China Agricultural University, as it traveled through rows of a lettuce farm. The robot's motion includes straight, backward-and-forward, and turning-out-and-in trajectories, so the same lettuce plant frequently exits and later re-enters the camera's field of view. The dataset was built specifically to support re-identification of these "re-occurred" plants — recovering a plant's original track ID after a long absence — which is the key unsolved problem for robots that must spray each vegetable exactly once while moving back and forth along farm rows.

• Curated by: Nan Hu, Daobilige Su, Shuo Wang, Xuechang Wang, Huiyu Zhong, Zimeng Wang, Yongliang Qiao, Yu Tan (China Agricultural University and collaborating institutions)
• Funded by: [More Information Needed] (not stated in the accessible preprint/journal indexing; a related dataset from the same lab and robot, LettuceMOT, was funded by the National Natural Science Foundation of China, Grant No. 3217150435, and a China Agricultural University international cooperation seed fund — it is unconfirmed whether the same funding covers this dataset)
• Shared by: Nan Hu et al., via github.com/NanH5837/LettuceMOTS
• Language(s): N/A (image/video dataset, no text)
• License: The original authors did not explicitly state a data license (only "publicly released for the benefit of the community"); the accompanying code repository is licensed GPL-3.0. This FiftyOne re-release on Hugging Face Hub is tagged CC-BY-4.0 as a reasonable default for open research re-distribution — please attribute the original paper (see Citation) if you use this dataset.

Dataset Sources

• Repository: github.com/NanH5837/LettuceMOTS
• Paper: Hu, N., Su, D., Wang, S., Wang, X., Zhong, H., Wang, Z., Qiao, Y., & Tan, Y. (2024). Segmentation and tracking of vegetable plants by exploiting vegetable shape feature for precision spray of agricultural robots. Journal of Field Robotics, 41(3), 570–586. https://doi.org/10.1002/rob.22279 (preprint: arXiv:2306.13518)
• Demo: github.com/NanH5837/LettuceMOTS includes a demo video and reference YOLOv5-seg + shape-feature tracking implementation.

Uses

Direct Use

• Training and evaluating instance segmentation models for lettuce plants in field conditions.
• Training and evaluating multi-object tracking / MOTS methods, in particular data-association strategies that must handle long-term occlusion and re-entry (re-identification), as opposed to short-term occlusion handling alone.
• Benchmarking robotic precision-spray perception pipelines end to end (segment → track → decide whether a plant has already been sprayed).

Out-of-Scope Use

• General-purpose or multi-class object detection/segmentation — the dataset contains a single class (lettuce) grown in one farm, so it is not suited for evaluating broader vegetable/plant taxonomies.
• Deployment or generalization claims outside similar conditions to those captured: a fixed downward-facing camera at ~1.4 m height, ~810×1080 cropped frames, natural outdoor lighting, and lettuce spaced ~0.15–0.25 m apart. Different crops, camera geometry, or planting density would require re-validation.
• The dataset contains no people or personally identifiable information; it should not be repurposed for anything involving personal data.

Dataset Structure

Imported as a FiftyOne video dataset (media_type="video") named lettucemots with 12 samples — one video per raw image sequence — totaling 1,308 frames. Each of the 12 sequences corresponds to one continuous robot pass through a farm row; the 12 sequences span 3 growth stages (seedling/rosette, cupping, heading) × 4 sequences per stage, with 3 sequences per stage used for training and 1 held out for test (9 train / 3 test overall).

Each video was re-encoded from the source PNG frame stack to H.264 MP4 at the dataset's native 15 FPS acquisition rate (frame content and count are otherwise unmodified from the original release).

Sample fields

Field	FiftyOne type	Description
filepath	StringField	Path to the per-sequence MP4 (re-encoded from the source PNG frame stack)
tags	ListField(StringField)	train or test, mirroring split
sequence	StringField	Sequence identifier, "0000"–"0011" (verbatim from source directory names)
split	StringField	train or test, matching the dataset's original directory split
metadata	VideoMetadata	Frame size (810×1080), frame rate (15 fps), total frame count, duration — computed via dataset.compute_metadata()

Frame fields

Field	FiftyOne type	Description
frame_number	FrameNumberField	1-indexed frame number within the sequence (source frame_id + 1)
ground_truth	Detections	Per-frame instance segmentation + tracking labels (see below)

Label type and mapping

Each ground_truth detection is an fo.Detection with:

• label: always "lettuce" (the dataset has a single class)
• mask: a boolean instance mask, cropped to the detection's own tight bounding box
• bounding_box: [x, y, w, h] in relative [0, 1] coordinates, computed as the tight extent of the instance mask (the source format ships pixel masks but no separate box annotation)
• index: an integer track ID, unique within its sequence and stable across frames — this is what lets the same physical plant be re-identified across a long absence from the camera's field of view, and is what FiftyOne's App uses to render tracking trajectories

fo.Detections/masks (rather than fo.Segmentation) were chosen because the source annotations are already per-instance (KITTI-MOTS-style: one RLE mask per tracked object per frame), and per-instance masks are what's needed to carry the per-object index used for tracking — a single semantic fo.Segmentation mask per frame would have discarded the track identity.

Source ground truth ships in two redundant, pixel-verified-identical forms (only the RLE form was used for the FiftyOne import; the mask-PNG form is not otherwise referenced by the FiftyOne dataset):

1. A per-sequence KITTI-MOTS-style TXT file (frame_id object_id class_id img_height img_width RLE), one line per instance per frame.
2. A per-frame 16-bit grayscale PNG where pixel value 0 = background and 1000 + track_id = that instance's pixels.

The source object_id values are offset by 1000 (e.g. 1000, 1001, ...); the FiftyOne index field stores object_id - 1000 for a cleaner, dataset-consistent 0-based-ish per-sequence ID space.

dataset.info

Not currently populated ({}); dataset.default_classes is also unset. Since there is only one label value ("lettuce") across the whole dataset, no class-id map was needed for import, but a future pass could set dataset.default_classes = ["lettuce"] for clarity.

Parsing decisions

• Bounding boxes are derived, not sourced: the release does not ship separate bbox annotations, only masks; boxes are the tight pixel extent of each decoded instance mask.
• Frame numbering: source frame_id is 0-indexed and contiguous per sequence with zero gaps (verified for all 12 sequences); FiftyOne's frame_number is 1-indexed, so frame_number = frame_id + 1.
• Video re-encoding, not raw frame import: frames were stitched into MP4s (rather than imported as a flat image collection) specifically to use FiftyOne's video/track-native index field and App timeline scrubbing, since the source data is fundamentally a set of image sequences with persistent per-object identity. fiftyone.utils.utils.video.reencode_video's in_opts is silently overridden when reencode=True, so the true 15 FPS input rate had to be set via a direct ffmpeg -framerate 15 call rather than that helper's defaults (which fall back to 25 FPS for image-sequence inputs).
• YOLOv5-seg polygon labels not imported: the release also ships a secondary, redundant label set (LettuceMOTSyolo/, normalized polygon labels in YOLOv5-seg format) for convenience training with the reference implementation. These cover only the 9 training sequences, carry no track ID, and are fully derivable from the mask/RLE ground truth already imported — they were left out of the FiftyOne dataset as redundant, but remain available on disk (LettuceMOTSyolo/) if needed for exact reproduction of the paper's YOLOv5 training recipe.
• Pretrained weights (weights/0003.pt, 0007.pt, 0011.pt, one YOLOv5m-seg checkpoint per held-out test sequence) were downloaded alongside the dataset but are not part of the FiftyOne dataset itself; they require the original repo's models/ code on sys.path to unpickle (not vanilla ultralytics).

Dataset Creation

Curation Rationale

Existing agricultural MOTS/MOT datasets (e.g. Apple MOTS) are collected with cameras that move in one direction, so an object that leaves the frame never reappears — models built on them simply drop or discard tracks that disappear. Real spray robots, however, must reverse to avoid obstacles, temporarily leave a row (e.g. to recharge), or re-enter a row, causing genuine plants to leave and later re-enter the same camera's view. Without re-identification, a robot will spray such a plant more than once. LettuceMOTS was purpose-built with backward/forward and turn-out/turn-in robot trajectories specifically to create these long-gap re-occurrence cases and let researchers benchmark re-identification, not just short-term tracking-through-occlusion.

Source Data

Data Collection and Processing

Images were collected by VegeBot, a four-wheel-steer, four-wheel-drive agricultural robot, in a lettuce farm in Tongzhou District, Beijing, China, in September–October 2022. Lettuces were spaced ~0.15–0.25 m apart within a row and ~0.25–0.3 m between rows, with weed density up to ~10/m². Data was collected at 3 growth stages. A downward-facing RGB camera (1/2.8" Sony IMX317 CMOS sensor) was mounted ~1.4 m above the ground, capturing at 1920×1080 / 15 FPS under natural light with automatic exposure; frames were subsequently cropped to 810×1080 to remove irrelevant background. The robot was manually remote-controlled (rather than using its own vision-based autonomous navigation) to ensure consistent image quality, and moved at roughly 0.35–0.4 m/s. Annotation used the CVAT tool (its tracking/interpolation features reduce manual per-frame labeling effort), producing masks in a KITTI-MOTS-style RLE format with persistent per-plant track IDs.

Who are the source data producers?

The lettuce plants and farm scene were part of a working farm in Tongzhou District, Beijing; data collection was performed by the paper's authors' research group (China Agricultural University) using their VegeBot robot platform. No crowdsourcing was involved in data collection.

Annotations

Annotation process

Frames were manually annotated in CVAT with per-instance segmentation masks and consistent per-object track IDs across each sequence, then exported to a KITTI-MOTS-style text format (frame_id object_id category img_height img_width RLE) — one line per object instance per frame. category is always 1 (vegetable/lettuce). A secondary polygon-format label set for direct YOLOv5-seg training was also derived from these masks by the authors, covering only the training sequences.

Who are the annotators?

The paper states CVAT was used for annotation but does not name or describe the individual annotators.

Personal and Sensitive Information

None identified. The dataset consists exclusively of overhead farm imagery (soil, crop rows, and lettuce plants); no people, faces, or personally identifiable information appear in the released frames.

Citation

BibTeX:

@article{hu2024segmentation,
  title     = {Segmentation and tracking of vegetable plants by exploiting vegetable shape feature for precision spray of agricultural robots},
  author    = {Hu, Nan and Su, Daobilige and Wang, Shuo and Wang, Xuechang and Zhong, Huiyu and Wang, Zimeng and Qiao, Yongliang and Tan, Yu},
  journal   = {Journal of Field Robotics},
  volume    = {41},
  number    = {3},
  pages     = {570--586},
  year      = {2024},
  publisher = {Wiley},
  doi       = {10.1002/rob.22279}
}

APA:

Hu, N., Su, D., Wang, S., Wang, X., Zhong, H., Wang, Z., Qiao, Y., & Tan, Y. (2024). Segmentation and tracking of vegetable plants by exploiting vegetable shape feature for precision spray of agricultural robots. Journal of Field Robotics, 41(3), 570–586. https://doi.org/10.1002/rob.22279

More Information

Verified during import: total frame count (1,308), total tracked object instances (314), and total mask annotations (17,562) all match the values reported in the source paper exactly, as does the re-occurrence behavior the dataset was designed to capture (e.g. one track in sequence 0004 appears in frames 1–5, disappears, and reoccurs under the same ID in frames 114–117).

Do not confuse this dataset with the earlier, related LettuceMOT (no "S") dataset from the same lab and robot platform, which has bounding-box-only MOT annotations (no segmentation masks) and is hosted separately on Mega.nz.