ArticlePlant phenomics (Washington, D.C.)2022
How Useful Is Image-Based Active Learning for Plant Organ Segmentation?
Article in Plant phenomics (Washington, D.C.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Dual-guided asymmetric MP-former for rice root instance segmentation.Plant phenomics (Washington, D.C.) · 2026Article
- Active learning for data-efficient segmentation of tea buds and leaves.Frontiers in plant science · 2026Article
- A crop cultivation monitoring platform for evaluating the early growth of cucumbers.Frontiers in plant science · 2026Article
- Plant stem and leaf segmentation and phenotypic parameter extraction using neural radiance fields and lightweight point cloud segmentation networks.Frontiers in plant science · 2025Article
- Efficient Wheat Head Segmentation with Minimal Annotation: A Generative Approach.Journal of imaging · 2024Article
- Mapping the diversity of land uses following deforestation across Africa.Scientific reports · 2024Article
- Semi-Self-Supervised Learning for Semantic Segmentation in Images with Dense Patterns.Plant phenomics (Washington, D.C.) · 2023Article
- WRA-Net: Wide Receptive Field Attention Network for Motion Deblurring in Crop and Weed Image.Plant phenomics (Washington, D.C.) · 2023Article
- Nondestructive 3D phenotyping method of passion fruit based on X-ray micro-computed tomography and deep learning.Frontiers in plant science · 2022Article
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Authors and funding
6 authors.
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Abstract
Training deep learning models typically requires a huge amount of labeled data which is expensive to acquire, especially in dense prediction tasks such as semantic segmentation. Moreover, plant phenotyping datasets pose additional challenges of heavy occlusion and varied lighting conditions which makes annotations more time-consuming to obtain. Active learning helps in reducing the annotation cost by selecting samples for labeling which are most informative to the model, thus improving model performance with fewer annotations. Active learning for semantic segmentation has been well studied on datasets such as PASCAL VOC and Cityscapes. However, its effectiveness on plant datasets has not received much importance. To bridge this gap, we empirically study and benchmark the effectiveness of four uncertainty-based active learning strategies on three natural plant organ segmentation datasets. We also study their behaviour in response to variations in training configurations in terms of augmentations used, the scale of training images, active learning batch sizes, and train-validation set splits.
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