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

作     者：Shivangana Rawat Akshay L.Chandra Sai Vikas Desai Vineeth N.Balasubramanian Seishi Ninomiya Wei Guo Shivangana Rawat;Akshay L. Chandra;Sai Vikas Desai;Vineeth N. Balasubramanian;Seishi Ninomiya;Wei Guo

作者机构：Department of Computer Science and EngineeringIndian Institute of TechnologyHyderabadIndia Department of Computer ScienceUniversity of FreiburgGermany Graduate School of Agricultural and Life SciencesThe University of TokyoJapan 

出 版 物：《Plant Phenomics》 (植物表型组学（英文）)

年 卷 期：2022年第4卷第1期

页      面：384-394页

核心收录：

学科分类：0710[理学-生物学] 071001[理学-植物学] 07[理学] 08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：funded by the Indo-Japan DST-JST SICORP program “Data Science-based Farming Support System for Sustainable Crop Production under Climatic Change” AIP Acceleration Research program “Studies of CPS platform to raise big-data-driven AI agriculture” by Japan Science and Technology Agency 

主　　题：consuming lighting sizes 

摘      要：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 ***,plant phenotyping datasets pose additional challenges of heavy occlusion and varied lighting conditions which makes annotations more time-consuming to *** 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 *** learning for semantic segmentation has been well studied on datasets such as PASCAL VOC and ***,its effectiveness on plant datasets has not received much *** bridge this gap,we empirically study and benchmark the effectiveness of four uncertainty-based active learning strategies on three natural plant organ segmentation *** 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.