Swati Bhugra

TL;DR: A large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database and the best performing teams have outperformed state-of-the-art approaches on both tasks.

TL;DR: A novel automated pipeline leveraging deep convolutional neural networks for stomata detection and its quantification shows a superior performance in contrast to the existing stomATA detection methods in terms of precision and recall.

TL;DR: In this paper, morphological, physiological, biochemical and molecular variations between drought tolerant (PB6 and Moroberakan) and drought sensitive (Way Rarem) varieties have been evaluated, and notable differences have been observed in root morphology, root xylem number and area, stomata number, relative water content, proline content, protein and gene expression.

TL;DR: Assessment of the correlation between genotypic and phenotypic traits that can contribute towards the emerging field of rice phenomics finds that there is a notable difference in gene expression of OsPIP2;5 and OsNip2;1 in various indica varieties of rice at different time periods of stress.

TL;DR: This paper proposes a novel end-to-end pipeline including 3D reconstruction, segmentation and feature extraction, leveraging deep neural networks at various stages, for drought stress study, and shows that the network outperforms conventional methods.