On the public citation graph

LettuceMOT: A dataset of lettuce detection and tracking with re-identification of re-occurred plants for agricultural robots

https://doi.org/10.3389/fpls.2022.1047356
0 signed · 36 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/rob.21675openalex-snapshot
Effective Vision‐based Classification for Separating Sugar Beets and Weeds for Precision Farming
inferredcites →https://doi.org/10.1002/rob.21726openalex-snapshot
3D‐vision based detection, localization, and sizing of broccoli heads in the field
inferredcites →https://doi.org/10.1002/rob.21877openalex-snapshot
A high‐resolution, multimodal data set for agricultural robotics: A Ladybird 's‐eye view of Brassica
inferredcites →https://doi.org/10.1007/978-3-030-58452-8_16openalex-snapshot
Segment as Points for Efficient Online Multi-Object Tracking and Segmentation
inferredcites →https://doi.org/10.1007/978-3-031-20047-2_1openalex-snapshot
ByteTrack: Multi-object Tracking by Associating Every Detection Box
inferredcites →https://doi.org/10.1007/978-3-319-48881-3_2openalex-snapshot
Performance Measures and a Data Set for Multi-target, Multi-camera Tracking
inferredcites →https://doi.org/10.1007/s11119-019-09642-0openalex-snapshot
Deep learning for real-time fruit detection and orchard fruit load estimation: benchmarking of ‘MangoYOLO’
inferredcites →https://doi.org/10.1007/s11263-020-01375-2openalex-snapshot
HOTA: A Higher Order Metric for Evaluating Multi-object Tracking
inferredcites →https://doi.org/10.1007/s11263-020-01393-0openalex-snapshot
MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking
inferredcites →https://doi.org/10.1007/s11263-021-01513-4openalex-snapshot
FairMOT: On the Fairness of Detection and Re-identification in Multiple Object Tracking
inferredcites →https://doi.org/10.1109/cvpr.2019.00813openalex-snapshot
MOTS: Multi-Object Tracking and Segmentation
inferredcites →https://doi.org/10.1109/cvprw.2009.5206735openalex-snapshot
Learning to associate: HybridBoosted multi-target tracker for crowded scene
inferredcites →https://doi.org/10.1109/iccvw54120.2021.00315openalex-snapshot
GIAOTracker: A comprehensive framework for MCMOT with global information and optimizing strategies in VisDrone 2021
inferredcites →https://doi.org/10.1109/icip.2016.7533003openalex-snapshot
Simple online and realtime tracking
inferredcites →https://doi.org/10.1109/icra.2017.7989417openalex-snapshot
Deep fruit detection in orchards
inferredcites →https://doi.org/10.1109/icra.2018.8460962openalex-snapshot
Real-Time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs
inferredcites →https://doi.org/10.1109/lra.2022.3199026openalex-snapshot
APPLE MOTS: Detection, Segmentation and Tracking of Homogeneous Objects Using MOTS
inferredcites →https://doi.org/10.1155/2008/246309openalex-snapshot
Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics
inferredcites →https://doi.org/10.1177/0278364917720510openalex-snapshot
Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields
inferredcites →https://doi.org/10.1186/s13007-019-0528-3openalex-snapshot
DeepSeedling: deep convolutional network and Kalman filter for plant seedling detection and counting in the field
inferredcites →https://doi.org/10.3390/agronomy12020356openalex-snapshot
Benchmark of Deep Learning and a Proposed HSV Colour Space Models for the Detection and Classification of Greenhouse Tomato
inferredcites →https://doi.org/10.48550/arxiv.1504.01942openalex-snapshot
MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking
inferredcites →https://doi.org/10.48550/arxiv.1603.00831openalex-snapshot
MOT16: A Benchmark for Multi-Object Tracking
inferredcites →https://doi.org/10.48550/arxiv.2003.09003openalex-snapshot
MOT20: A benchmark for multi object tracking in crowded scenes

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4693600openalex-extracted
Multiple Plant Tracking Using a Vision Transformer for Image Matching as a Spatial Association Strategy
inferred← citeshttps://doi.org/10.1016/j.asoc.2025.112971openalex-snapshot
Deep learning in produce perception of harvesting robots: A comprehensive review
inferred← citeshttps://doi.org/10.1016/j.atech.2025.100883openalex-snapshot
WeedsSORT: A weed tracking-by-detection framework for laser weeding applications within precision agriculture
inferred← citeshttps://doi.org/10.1016/j.compag.2024.109379openalex-snapshot
Multi-Object Tracking in Agricultural Applications using a Vision Transformer for Spatial Association
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2023.06.006openalex-snapshot
A multi-modal garden dataset and hybrid 3D dense reconstruction framework based on panoramic stereo images for a trimming robot
inferred← citeshttps://doi.org/10.1109/eircs70783.2026.11705775openalex-snapshot
An Edge-Oriented Spatio-Temporal Weed Tracking Framework for Robotic Laser Weeding
inferred← citeshttps://doi.org/10.1109/isie54533.2024.10595716openalex-snapshot
CropFinder: AI-based Detection and Tracking of Crops for Precision Agriculture
inferred← citeshttps://doi.org/10.1109/lra.2026.3655296openalex-snapshot
Weeds Automatic Annotation and Stem Localization Based on Spatial Association for Laser Weeding Robot
inferred← citeshttps://doi.org/10.32604/cmc.2025.066056openalex-snapshot
A Systematic Review of Deep Learning-Based Object Detection in Agriculture: Methods, Challenges, and Future Directions
inferred← citeshttps://doi.org/10.3389/fpls.2023.1175743openalex-snapshot
LFSD: a VSLAM dataset with plant detection and tracking in lettuce farm
inferred← citeshttps://doi.org/10.51583/ijltemas.2025.140600094openalex-snapshot
Optimizing Lettuce Cultivation: Nutrient and Disease Monitoring in Vertical Farms
inferred← citeshttps://doi.org/10.59476/mtt.v1i20.656openalex-snapshot
Determination of the area index of lettuce leaves with a monocular camera
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.