How Useful Is Image-Based Active Learning for Plant Organ Segmentation?

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.

Automated summary

Training deep learning models requires a large amount of labeled data, which can be expensive and time-consuming, especially in tasks like semantic segmentation. Active learning helps reduce annotation costs by selecting the most informative samples to label, improving model performance with fewer annotations. However, the effectiveness of active learning on plant datasets has not been widely studied yet.