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

Rawat, Shivangana and Chandra, Akshay L. and Desai, Sai Vikas and Balasubramanian, Vineeth N and et al, . (2022) How Useful Is Image-Based Active Learning for Plant Organ Segmentation? Plant Phenomics, 2022. pp. 1-11. ISSN 2643-6515

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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. © 2022 Shivangana Rawat et al.

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IITH Creators:
IITH CreatorsORCiD
Balasubramanian, Vineeth N
Item Type: Article
Additional Information: This study was partially funded by the Indo-Japan DST-JST SICORP program “Data Science-based Farming Support System for Sustainable Crop Production under Climatic Change” and AIP Acceleration Research program “Studies of CPS platform to raise big-data-driven AI agriculture” by Japan Science and Technology Agency.
Uncontrolled Keywords: Active Learning; Heavy occlusion; Image-based; Labeled data; Learning models; Organ segmentation; Plant organs; Plant phenotyping; Prediction tasks; Semantic segmentation
Subjects: Computer science
Others > Agricultural engineering
Divisions: Department of Computer Science & Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 23 Jul 2022 04:28
Last Modified: 23 Jul 2022 04:28
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