ArticleScientific data2024
Crops3D: a diverse 3D crop dataset for realistic perception and segmentation toward agricultural applications.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- A Dataset of Temporally Consistent Instance Annotations for 4D Plant Phenotyping.Scientific data · 2026Article
- Three-Dimensional Crop Phenotyping for Crop Protection: Reconstruction Routes, Decision Pathways, and Digital-Twin Maturity.Plants (Basel, Switzerland) · 2026Review
- HiMamba-Net: a Hilbert-serialized Mamba network for 3D point cloud instance segmentation.Scientific reports · 2026Article
- TomatoPGT: A 3D point cloud dataset of tomato plants for segmentation and plant-trait extraction.Data in brief · 2026Article
- Research progress on rapid detection technology of soybean phenotypic indicators under saline-alkali stress.Planta · 2026Review
- Three-dimensional phenotyping: Technological advances and applications in genomics-assisted crop breeding.Plant communications · 2026Review
- MaizeField3D: A curated 3D point cloud and procedural model dataset of field-grown maize from a diversity panel.Plant phenomics (Washington, D.C.) · 2026Article
- SOY3DSEG: A high-precision universal point cloud segmentation model for soybean full growth period based on improved point transformer.Plant phenomics (Washington, D.C.) · 2026Article
- Pepper-4D: Spatiotemporal 3D Pepper Crop Dataset for Phenotyping.Plants (Basel, Switzerland) · 2026Article
- High-density field-based 3D reconstruction of rice architecture across diverse cultivars for genome-wide association studies.Plant methods · 2026Article
- ELGCot3D: a lightweight 3D cotton point cloud segmentation model based on EdgeConv-Local Attention-GCN and semantic feature enhancement.Frontiers in plant science · 2026Article
- A scalable framework for UAV-based point cloud reconstruction and organ segmentation of field-grown cotton in complex field environments.Frontiers in plant science · 2026Article
- PTV2-Fr: a point cloud segmentation network for phenotypic trait extraction and gibberellin effect analysis in sorghum seedlings.Frontiers in plant science · 2026Article
- A Review of Optical-Based Three-Dimensional Reconstruction and Multi-Source Fusion for Plant Phenotyping.Sensors (Basel, Switzerland) · 2025Review
- DCSFormer: a high-precision method for cotton seedling point cloud organ segmentation.Frontiers in plant science · 2025Article
- Crops3D: a diverse 3D crop dataset for realistic perception and segmentation toward agricultural applications.Scientific data · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Point cloud analysis is a crucial task in computer vision. Despite significant advances over the past decade, the developments in agricultural domain have faced challenges due to a scarcity of datasets. To facilitate 3D point cloud research in agriculture community, we introduce Crops3D, the diverse real-world dataset derived from authentic agricultural scenarios. Crops3D distinguishes itself through its unique properties: diversity, authenticity, and complexity. The dataset incorporates data from diverse point cloud acquisition methods, encompassing eight distinct crop types with 1,230 samples, authentically representing crops in the real-world. It stands as the pioneering dataset that comprehensively supports the three critical tasks in 3D crop phenotyping: instance segmentation of individual plants in agricultural settings, plant type perception, and plant organ segmentation. Additionally, the intricate crop structures in Crops3D exhibit higher complexity than available 3D public datasets, showcasing substantial self-occlusion and increased complexity as crops mature. We analyse diverse crop point cloud acquisition methods and evaluate multiple models' performance with the Crops3D dataset.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.