ArticleScientific data2023
VegAnn, Vegetation Annotation of multi-crop RGB images acquired under diverse conditions for segmentation.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Hyperspectral Imaging-Based Evaluation of Seasonal Growth Characteristics in Turfgrass.Plants (Basel, Switzerland) · 2026Article
- A Leaf-Level Dataset for Soybean-Cotton Detection and Segmentation.Scientific data · 2026Article
- The Multi-Sensor and Multi-Temporal Dataset of Multiple Crops for In-Field Phenotyping and Monitoring.Scientific data · 2026Article
- A Mycelium Dataset with Edge-Precise Annotation for Semantic Segmentation.Scientific data · 2025Article
- Data-efficient and accurate rapeseed leaf area estimation by self-supervised vision transformer for germplasms early evaluation.Plant methods · 2025Article
- BOLM high resolution land use and land cover dataset and benchmark results for the rapidly developing City of Dhaka Bangladesh.Scientific reports · 2025Article
- Global rice multiclass segmentation dataset (RiceSEG): comprehensive and diverse high-resolution RGB-annotated images for the development and benchmarking of rice segmentation algorithms.Plant phenomics (Washington, D.C.) · 2025Article
- The Global Wheat Full Semantic Organ Segmentation (GWFSS) dataset.Plant phenomics (Washington, D.C.) · 2025Article
- The blessing of Depth Anything: An almost unsupervised approach to crop segmentation with depth-informed pseudo labeling.Plant phenomics (Washington, D.C.) · 2025Article
- Predictive modelling employing machine learning, convolutional neural networks (CNNs), and smartphone RGB images for non-destructive biomass estimation of pearl millet (Frontiers in plant science · 2025Article
- Pixel to practice: multi-scale image data for calibrating remote-sensing-based winter wheat monitoring methods.Scientific data · 2024Article
- Prediction accuracy and repeatability of UAV based biomass estimation in wheat variety trials as affected by variable type, modelling strategy and sampling location.Plant methods · 2024Article
- VegAnn, Vegetation Annotation of multi-crop RGB images acquired under diverse conditions for segmentation.Scientific data · 2023Article
- To What Extent Does Yellow Rust Infestation Affect Remotely Sensed Nitrogen Status?Plant phenomics (Washington, D.C.) · 2023Article
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Applying deep learning to images of cropping systems provides new knowledge and insights in research and commercial applications. Semantic segmentation or pixel-wise classification, of RGB images acquired at the ground level, into vegetation and background is a critical step in the estimation of several canopy traits. Current state of the art methodologies based on convolutional neural networks (CNNs) are trained on datasets acquired under controlled or indoor environments. These models are unable to generalize to real-world images and hence need to be fine-tuned using new labelled datasets. This motivated the creation of the VegAnn - Vegetation Annotation - dataset, a collection of 3775 multi-crop RGB images acquired for different phenological stages using different systems and platforms in diverse illumination conditions. We anticipate that VegAnn will help improving segmentation algorithm performances, facilitate benchmarking and promote large-scale crop vegetation segmentation research.
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