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

Area of Science:

• Computer Vision
• Machine Learning
• Plant Science

Background:

• Deep learning for semantic segmentation demands extensive labeled data, which is costly and time-consuming to obtain.
• Plant phenotyping datasets present unique challenges, including occlusion and variable lighting, further complicating annotation.
• Active learning strategies can mitigate annotation costs by prioritizing the most informative samples for labeling.

Purpose of the Study:

• To empirically evaluate and benchmark the performance of uncertainty-based active learning strategies for plant organ segmentation.
• To assess the effectiveness of active learning on natural plant datasets, which have been understudied compared to general segmentation datasets.
• To investigate the impact of various training configurations on active learning performance in this domain.

Main Methods:

• Four distinct uncertainty-based active learning strategies were implemented and tested.
• The strategies were applied to three distinct natural plant organ segmentation datasets.
• Performance was analyzed across different training configurations, including data augmentation, image scaling, batch sizes, and data splits.

Main Results:

• The study provides a benchmark of active learning strategy effectiveness on plant organ segmentation tasks.
• Empirical results demonstrate the viability of active learning in reducing annotation effort for plant phenotyping.
• The influence of training parameters on active learning performance was systematically analyzed.

Conclusions:

• Uncertainty-based active learning is a promising approach to reduce annotation costs in plant organ segmentation.
• The findings offer valuable insights for researchers and practitioners in plant phenotyping and computer vision.
• Further research can build upon these benchmarks to optimize active learning pipelines for agricultural applications.